notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries yang digunakan\n",
    "melakukan import terhadap library yang digunakan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import plotly.express as px "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read file\n",
    "membaca file pada data sekaligus melakukan cek terhadap kolom dan baris dan juga isi dari file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_Id</th>\n",
       "      <th>Place_Id</th>\n",
       "      <th>Place_Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>344</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>373</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>300</td>\n",
       "      <td>425</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>300</td>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>300</td>\n",
       "      <td>311</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>300</td>\n",
       "      <td>279</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>300</td>\n",
       "      <td>163</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User_Id  Place_Id  Place_Ratings\n",
       "0           1       179              3\n",
       "1           1       344              2\n",
       "2           1         5              5\n",
       "3           1       373              3\n",
       "4           1       101              4\n",
       "...       ...       ...            ...\n",
       "9995      300       425              2\n",
       "9996      300        64              4\n",
       "9997      300       311              3\n",
       "9998      300       279              4\n",
       "9999      300       163              2\n",
       "\n",
       "[10000 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tourism_rating = pd.read_csv('tourism_rating.csv')\n",
    "data_tourism_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Place_Id</th>\n",
       "      <th>Place_Name</th>\n",
       "      <th>Description</th>\n",
       "      <th>Category</th>\n",
       "      <th>City</th>\n",
       "      <th>Price</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Time_Minutes</th>\n",
       "      <th>Coordinate</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Monumen Nasional</td>\n",
       "      <td>Monumen Nasional atau yang populer disingkat d...</td>\n",
       "      <td>Budaya</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>20000</td>\n",
       "      <td>4.6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>{'lat': -6.1753924, 'lng': 106.8271528}</td>\n",
       "      <td>-6.175392</td>\n",
       "      <td>106.827153</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kota Tua</td>\n",
       "      <td>Kota tua di Jakarta, yang juga bernama Kota Tu...</td>\n",
       "      <td>Budaya</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>90.0</td>\n",
       "      <td>{'lat': -6.137644799999999, 'lng': 106.8171245}</td>\n",
       "      <td>-6.137645</td>\n",
       "      <td>106.817125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Dunia Fantasi</td>\n",
       "      <td>Dunia Fantasi atau disebut juga Dufan adalah t...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>270000</td>\n",
       "      <td>4.6</td>\n",
       "      <td>360.0</td>\n",
       "      <td>{'lat': -6.125312399999999, 'lng': 106.8335377}</td>\n",
       "      <td>-6.125312</td>\n",
       "      <td>106.833538</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Taman Mini Indonesia Indah (TMII)</td>\n",
       "      <td>Taman Mini Indonesia Indah merupakan suatu kaw...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>10000</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'lat': -6.302445899999999, 'lng': 106.8951559}</td>\n",
       "      <td>-6.302446</td>\n",
       "      <td>106.895156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Atlantis Water Adventure</td>\n",
       "      <td>Atlantis Water Adventure atau dikenal dengan A...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>94000</td>\n",
       "      <td>4.5</td>\n",
       "      <td>60.0</td>\n",
       "      <td>{'lat': -6.12419, 'lng': 106.839134}</td>\n",
       "      <td>-6.124190</td>\n",
       "      <td>106.839134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>433</td>\n",
       "      <td>Museum Mpu Tantular</td>\n",
       "      <td>Museum Negeri Mpu Tantular adalah sebuah museu...</td>\n",
       "      <td>Budaya</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>2000</td>\n",
       "      <td>4.4</td>\n",
       "      <td>45.0</td>\n",
       "      <td>{'lat': -7.4338593, 'lng': 112.7199058}</td>\n",
       "      <td>-7.433859</td>\n",
       "      <td>112.719906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>434</td>\n",
       "      <td>Taman Bungkul</td>\n",
       "      <td>Taman Bungkul adalah taman wisata kota yang te...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'lat': -7.291346799999999, 'lng': 112.7398218}</td>\n",
       "      <td>-7.291347</td>\n",
       "      <td>112.739822</td>\n",
       "      <td>NaN</td>\n",
       "      <td>434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>435</td>\n",
       "      <td>Taman Air Mancur Menari Kenjeran</td>\n",
       "      <td>Air mancur menari atau dancing fountain juga a...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>45.0</td>\n",
       "      <td>{'lat': -7.2752955, 'lng': 112.7549381}</td>\n",
       "      <td>-7.275296</td>\n",
       "      <td>112.754938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>436</td>\n",
       "      <td>Taman Flora Bratang Surabaya</td>\n",
       "      <td>Taman Flora adalah salah satu taman kota di Su...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'lat': -7.294330299999999, 'lng': 112.7617534}</td>\n",
       "      <td>-7.294330</td>\n",
       "      <td>112.761753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>437</td>\n",
       "      <td>Gereja Perawan Maria Tak Berdosa Surabaya</td>\n",
       "      <td>Gereja Katolik Kelahiran Santa Perawan Maria m...</td>\n",
       "      <td>Tempat Ibadah</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>10000</td>\n",
       "      <td>4.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'lat': -7.2420758, 'lng': 112.7368158}</td>\n",
       "      <td>-7.242076</td>\n",
       "      <td>112.736816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>437 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Place_Id                                 Place_Name  \\\n",
       "0           1                           Monumen Nasional   \n",
       "1           2                                   Kota Tua   \n",
       "2           3                              Dunia Fantasi   \n",
       "3           4          Taman Mini Indonesia Indah (TMII)   \n",
       "4           5                   Atlantis Water Adventure   \n",
       "..        ...                                        ...   \n",
       "432       433                        Museum Mpu Tantular   \n",
       "433       434                              Taman Bungkul   \n",
       "434       435           Taman Air Mancur Menari Kenjeran   \n",
       "435       436               Taman Flora Bratang Surabaya   \n",
       "436       437  Gereja Perawan Maria Tak Berdosa Surabaya   \n",
       "\n",
       "                                           Description       Category  \\\n",
       "0    Monumen Nasional atau yang populer disingkat d...         Budaya   \n",
       "1    Kota tua di Jakarta, yang juga bernama Kota Tu...         Budaya   \n",
       "2    Dunia Fantasi atau disebut juga Dufan adalah t...  Taman Hiburan   \n",
       "3    Taman Mini Indonesia Indah merupakan suatu kaw...  Taman Hiburan   \n",
       "4    Atlantis Water Adventure atau dikenal dengan A...  Taman Hiburan   \n",
       "..                                                 ...            ...   \n",
       "432  Museum Negeri Mpu Tantular adalah sebuah museu...         Budaya   \n",
       "433  Taman Bungkul adalah taman wisata kota yang te...  Taman Hiburan   \n",
       "434  Air mancur menari atau dancing fountain juga a...  Taman Hiburan   \n",
       "435  Taman Flora adalah salah satu taman kota di Su...  Taman Hiburan   \n",
       "436  Gereja Katolik Kelahiran Santa Perawan Maria m...  Tempat Ibadah   \n",
       "\n",
       "         City   Price  Rating  Time_Minutes  \\\n",
       "0     Jakarta   20000     4.6          15.0   \n",
       "1     Jakarta       0     4.6          90.0   \n",
       "2     Jakarta  270000     4.6         360.0   \n",
       "3     Jakarta   10000     4.5           NaN   \n",
       "4     Jakarta   94000     4.5          60.0   \n",
       "..        ...     ...     ...           ...   \n",
       "432  Surabaya    2000     4.4          45.0   \n",
       "433  Surabaya       0     4.6           NaN   \n",
       "434  Surabaya       0     4.4          45.0   \n",
       "435  Surabaya       0     4.6           NaN   \n",
       "436  Surabaya   10000     4.8           NaN   \n",
       "\n",
       "                                          Coordinate       Lat        Long  \\\n",
       "0            {'lat': -6.1753924, 'lng': 106.8271528} -6.175392  106.827153   \n",
       "1    {'lat': -6.137644799999999, 'lng': 106.8171245} -6.137645  106.817125   \n",
       "2    {'lat': -6.125312399999999, 'lng': 106.8335377} -6.125312  106.833538   \n",
       "3    {'lat': -6.302445899999999, 'lng': 106.8951559} -6.302446  106.895156   \n",
       "4               {'lat': -6.12419, 'lng': 106.839134} -6.124190  106.839134   \n",
       "..                                               ...       ...         ...   \n",
       "432          {'lat': -7.4338593, 'lng': 112.7199058} -7.433859  112.719906   \n",
       "433  {'lat': -7.291346799999999, 'lng': 112.7398218} -7.291347  112.739822   \n",
       "434          {'lat': -7.2752955, 'lng': 112.7549381} -7.275296  112.754938   \n",
       "435  {'lat': -7.294330299999999, 'lng': 112.7617534} -7.294330  112.761753   \n",
       "436          {'lat': -7.2420758, 'lng': 112.7368158} -7.242076  112.736816   \n",
       "\n",
       "     Unnamed: 11  Unnamed: 12  \n",
       "0            NaN            1  \n",
       "1            NaN            2  \n",
       "2            NaN            3  \n",
       "3            NaN            4  \n",
       "4            NaN            5  \n",
       "..           ...          ...  \n",
       "432          NaN          433  \n",
       "433          NaN          434  \n",
       "434          NaN          435  \n",
       "435          NaN          436  \n",
       "436          NaN          437  \n",
       "\n",
       "[437 rows x 13 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tourism_with_id = pd.read_csv('tourism_with_id.csv')\n",
    "data_tourism_with_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_Id</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Semarang, Jawa Tengah</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bekasi, Jawa Barat</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Cirebon, Jawa Barat</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bekasi, Jawa Barat</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Lampung, Sumatera Selatan</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>296</td>\n",
       "      <td>Lampung, Sumatera Selatan</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>297</td>\n",
       "      <td>Palembang, Sumatera Selatan</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>298</td>\n",
       "      <td>Bogor, Jawa Barat</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>299</td>\n",
       "      <td>Sragen, Jawa Tengah</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>300</td>\n",
       "      <td>Ponorogo, Jawa Timur</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     User_Id                     Location  Age\n",
       "0          1        Semarang, Jawa Tengah   20\n",
       "1          2           Bekasi, Jawa Barat   21\n",
       "2          3          Cirebon, Jawa Barat   23\n",
       "3          4           Bekasi, Jawa Barat   21\n",
       "4          5    Lampung, Sumatera Selatan   20\n",
       "..       ...                          ...  ...\n",
       "295      296    Lampung, Sumatera Selatan   31\n",
       "296      297  Palembang, Sumatera Selatan   39\n",
       "297      298            Bogor, Jawa Barat   38\n",
       "298      299          Sragen, Jawa Tengah   27\n",
       "299      300         Ponorogo, Jawa Timur   26\n",
       "\n",
       "[300 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user = pd.read_csv('user.csv')\n",
    "data_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Package</th>\n",
       "      <th>City</th>\n",
       "      <th>Place_Tourism1</th>\n",
       "      <th>Place_Tourism2</th>\n",
       "      <th>Place_Tourism3</th>\n",
       "      <th>Place_Tourism4</th>\n",
       "      <th>Place_Tourism5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>Pasar Tanah Abang</td>\n",
       "      <td>Taman Ayodya</td>\n",
       "      <td>Museum Tekstil</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>Pasar Tanah Abang</td>\n",
       "      <td>Pasar Taman Puring</td>\n",
       "      <td>Pasar Petak Sembilan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>Perpustakaan Nasional</td>\n",
       "      <td>Monas</td>\n",
       "      <td>Masjid Istiqlal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>Pulau Tidung</td>\n",
       "      <td>Pulau Bidadari</td>\n",
       "      <td>Pulau Pari</td>\n",
       "      <td>Pulau Pramuka</td>\n",
       "      <td>Pulau Pelangi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>Museum Satria Mandala</td>\n",
       "      <td>Museum Wayang</td>\n",
       "      <td>Museum Bahari Jakarta</td>\n",
       "      <td>Museum Macan (Modern and Contemporary Art in N...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>96</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>Ekowisata Mangrove Wonorejo</td>\n",
       "      <td>Taman Harmoni Keputih</td>\n",
       "      <td>Taman Kunang-Kunang</td>\n",
       "      <td>Taman Ekspresi Dan Perpustakaan</td>\n",
       "      <td>Museum De Javasche Bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>Ciputra Waterpark</td>\n",
       "      <td>Kenjeran Park</td>\n",
       "      <td>Kebun Binatang Surabaya</td>\n",
       "      <td>Food Junction Grand Pakuwon</td>\n",
       "      <td>| Atlantis Land Surabaya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>Museum Kesehatan Dr. Adhyatma</td>\n",
       "      <td>Patung Buddha Empat Rupa</td>\n",
       "      <td>Taman Hiburan Rakyat</td>\n",
       "      <td>Taman Bungkul</td>\n",
       "      <td>Taman Flora Bratang Surabaya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>99</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>Taman Ekspresi Dan Perpustakaan</td>\n",
       "      <td>Museum De Javasche Bank</td>\n",
       "      <td>Surabaya Museum (Gedung Siola)</td>\n",
       "      <td>Museum Mpu Tantular</td>\n",
       "      <td>Taman Air Mancur Menari Kenjeran</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>Taman Buah Surabaya</td>\n",
       "      <td>Hutan Bambu Keputih</td>\n",
       "      <td>Taman Barunawati</td>\n",
       "      <td>Kebun Bibit Wonorejo</td>\n",
       "      <td>Taman Mundu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Package      City                   Place_Tourism1  \\\n",
       "0         1   Jakarta                Pasar Tanah Abang   \n",
       "1         2   Jakarta                Pasar Tanah Abang   \n",
       "2         3   Jakarta            Perpustakaan Nasional   \n",
       "3         4   Jakarta                     Pulau Tidung   \n",
       "4         5   Jakarta            Museum Satria Mandala   \n",
       "..      ...       ...                              ...   \n",
       "95       96  Surabaya      Ekowisata Mangrove Wonorejo   \n",
       "96       97  Surabaya                Ciputra Waterpark   \n",
       "97       98  Surabaya    Museum Kesehatan Dr. Adhyatma   \n",
       "98       99  Surabaya  Taman Ekspresi Dan Perpustakaan   \n",
       "99      100  Surabaya              Taman Buah Surabaya   \n",
       "\n",
       "              Place_Tourism2                  Place_Tourism3  \\\n",
       "0               Taman Ayodya                  Museum Tekstil   \n",
       "1         Pasar Taman Puring            Pasar Petak Sembilan   \n",
       "2                      Monas                 Masjid Istiqlal   \n",
       "3             Pulau Bidadari                      Pulau Pari   \n",
       "4              Museum Wayang           Museum Bahari Jakarta   \n",
       "..                       ...                             ...   \n",
       "95     Taman Harmoni Keputih             Taman Kunang-Kunang   \n",
       "96             Kenjeran Park         Kebun Binatang Surabaya   \n",
       "97  Patung Buddha Empat Rupa            Taman Hiburan Rakyat   \n",
       "98   Museum De Javasche Bank  Surabaya Museum (Gedung Siola)   \n",
       "99       Hutan Bambu Keputih                Taman Barunawati   \n",
       "\n",
       "                                       Place_Tourism4  \\\n",
       "0                                                 NaN   \n",
       "1                                                 NaN   \n",
       "2                                                 NaN   \n",
       "3                                       Pulau Pramuka   \n",
       "4   Museum Macan (Modern and Contemporary Art in N...   \n",
       "..                                                ...   \n",
       "95                    Taman Ekspresi Dan Perpustakaan   \n",
       "96                        Food Junction Grand Pakuwon   \n",
       "97                                      Taman Bungkul   \n",
       "98                                Museum Mpu Tantular   \n",
       "99                               Kebun Bibit Wonorejo   \n",
       "\n",
       "                      Place_Tourism5  \n",
       "0                                NaN  \n",
       "1                                NaN  \n",
       "2                                NaN  \n",
       "3                      Pulau Pelangi  \n",
       "4                                NaN  \n",
       "..                               ...  \n",
       "95           Museum De Javasche Bank  \n",
       "96          | Atlantis Land Surabaya  \n",
       "97      Taman Flora Bratang Surabaya  \n",
       "98  Taman Air Mancur Menari Kenjeran  \n",
       "99                       Taman Mundu  \n",
       "\n",
       "[100 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_package_tourism = pd.read_csv('package_tourism.csv')\n",
    "data_package_tourism"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA (Exploratory Data Analysis)\n",
    "\n",
    "melakukan explorasi terhadap data dan variabel data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Univariate Analysis\n",
    "analisis univariate untuk beberapa variabel pada data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan analisis pada variabel Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data_user['Age'], kde=True, bins=10).title.set_text('Age Distribution') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>300.0</td>\n",
       "      <td>28.7</td>\n",
       "      <td>6.393716</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     count  mean       std   min   25%   50%   75%   max\n",
       "Age  300.0  28.7  6.393716  18.0  24.0  29.0  34.0  40.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data_user.Age.describe()).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan pengecekan distribusi pada variabel age menggunakan skew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kemiringan pada data :  0.008177316085058168\n"
     ]
    }
   ],
   "source": [
    "print(\"Kemiringan pada data : \", data_user.Age.skew()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pada variabel Age memiliki distribusi yang normal karena skewness tidak melebihi treshold yaitu 0,5\n",
    "\n",
    "berarti kita dapat mengetahui rata-rata wisatawan yang berkunjung berumur 18 hingga 40"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan analisis pada variabel City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "bingroup": "x",
         "histfunc": "count",
         "hovertemplate": "City=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "Jakarta",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Jakarta",
         "offsetgroup": "Jakarta",
         "orientation": "v",
         "showlegend": true,
         "texttemplate": "%{value}",
         "type": "histogram",
         "x": [
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta",
          "Jakarta"
         ],
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "bingroup": "x",
         "histfunc": "count",
         "hovertemplate": "City=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "Yogyakarta",
         "marker": {
          "color": "#EF553B",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Yogyakarta",
         "offsetgroup": "Yogyakarta",
         "orientation": "v",
         "showlegend": true,
         "texttemplate": "%{value}",
         "type": "histogram",
         "x": [
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta",
          "Yogyakarta"
         ],
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "bingroup": "x",
         "histfunc": "count",
         "hovertemplate": "City=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "Bandung",
         "marker": {
          "color": "#00cc96",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Bandung",
         "offsetgroup": "Bandung",
         "orientation": "v",
         "showlegend": true,
         "texttemplate": "%{value}",
         "type": "histogram",
         "x": [
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung",
          "Bandung"
         ],
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "bingroup": "x",
         "histfunc": "count",
         "hovertemplate": "City=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "Semarang",
         "marker": {
          "color": "#ab63fa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Semarang",
         "offsetgroup": "Semarang",
         "orientation": "v",
         "showlegend": true,
         "texttemplate": "%{value}",
         "type": "histogram",
         "x": [
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang",
          "Semarang"
         ],
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "bingroup": "x",
         "histfunc": "count",
         "hovertemplate": "City=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "Surabaya",
         "marker": {
          "color": "#FFA15A",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Surabaya",
         "offsetgroup": "Surabaya",
         "orientation": "v",
         "showlegend": true,
         "texttemplate": "%{value}",
         "type": "histogram",
         "x": [
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya",
          "Surabaya"
         ],
         "xaxis": "x",
         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "relative",
        "legend": {
         "title": {
          "text": "City"
         },
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "City's Tourism Destination"
        },
        "xaxis": {
         "anchor": "y",
         "categoryarray": [
          "Jakarta",
          "Yogyakarta",
          "Bandung",
          "Semarang",
          "Surabaya"
         ],
         "categoryorder": "array",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "City"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "count"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.histogram(data_frame=data_tourism_with_id, x='City', color='City', title='City\\'s Tourism Destination',histfunc='count',text_auto=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wisatawan lebih memilih Yogyakarta dan Bandung sebagai tempat wisata yang dipilihnya"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multivariate Analysis\n",
    "\n",
    "analisis pada 2 atau lebih variabel pada data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pada variabel rating dengan tempat wisata\n",
    "\n",
    "membuat variabel baru yaitu testing untuk mengelompokan Place_Id berdasarkan rata - rata Place_ratings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Place_Id</th>\n",
       "      <th>Place_Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>416</td>\n",
       "      <td>3.967742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>139</td>\n",
       "      <td>3.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52</td>\n",
       "      <td>3.842105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>254</td>\n",
       "      <td>3.789474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>97</td>\n",
       "      <td>3.772727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>322</td>\n",
       "      <td>3.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>112</td>\n",
       "      <td>3.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>3.722222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>321</td>\n",
       "      <td>3.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>157</td>\n",
       "      <td>3.695652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Place_Id  Place_Ratings\n",
       "0       416       3.967742\n",
       "1       139       3.882353\n",
       "2        52       3.842105\n",
       "3       254       3.789474\n",
       "4        97       3.772727\n",
       "5       322       3.766667\n",
       "6       112       3.764706\n",
       "7         1       3.722222\n",
       "8       321       3.714286\n",
       "9       157       3.695652"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing = data_tourism_rating.groupby('Place_Id')['Place_Ratings'].mean().sort_values(ascending=False).reset_index().head(10)\n",
    "testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan pengubahan terhadap nama Place_Id menjadi nama tempat aslinya sehingga lebih mudah untuk dikenali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,data in testing.iterrows():\n",
    "    # testing.loc[i,'Place_Id'] = data_tourism_with_id.\n",
    "    # print(data['Place_Id'].astype(int))\n",
    "    testing.loc[i,'Place_Id'] = data_tourism_with_id.loc[data_tourism_with_id.Place_Id == data['Place_Id'].astype(int),'Place_Name'].values[0]\n",
    "    # print(data_tourism_with_id.loc[data_tourism_with_id.Place_Id == data['Place_Id'].astype(int),'Place_Name'].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "Place_Id=%{y}<br>Place_Ratings=%{x}<extra></extra>",
         "legendgroup": "Keraton Surabaya",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Keraton Surabaya",
         "offsetgroup": "Keraton Surabaya",
         "orientation": "h",
         "showlegend": true,
         "textposition": "auto",
         "texttemplate": "%{x}",
         "type": "bar",
         "x": [
          3.967741935483871
         ],
         "xaxis": "x",
         "y": [
          "Keraton Surabaya"
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Place_Id=%{y}<br>Place_Ratings=%{x}<extra></extra>",
         "legendgroup": "Puncak Gunung Api Purba - Nglanggeran",
         "marker": {
          "color": "#EF553B",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Puncak Gunung Api Purba - Nglanggeran",
         "offsetgroup": "Puncak Gunung Api Purba - Nglanggeran",
         "orientation": "h",
         "showlegend": true,
         "textposition": "auto",
         "texttemplate": "%{x}",
         "type": "bar",
         "x": [
          3.8823529411764706
         ],
         "xaxis": "x",
         "y": [
          "Puncak Gunung Api Purba - Nglanggeran"
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Place_Id=%{y}<br>Place_Ratings=%{x}<extra></extra>",
         "legendgroup": "Kampung Cina",
         "marker": {
          "color": "#00cc96",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Kampung Cina",
         "offsetgroup": "Kampung Cina",
         "orientation": "h",
         "showlegend": true,
         "textposition": "auto",
         "texttemplate": "%{x}",
         "type": "bar",
         "x": [
          3.8421052631578947
         ],
         "xaxis": "x",
         "y": [
          "Kampung Cina"
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Place_Id=%{y}<br>Place_Ratings=%{x}<extra></extra>",
         "legendgroup": "Teras Cikapundung BBWS",
         "marker": {
          "color": "#ab63fa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Teras Cikapundung BBWS",
         "offsetgroup": "Teras Cikapundung BBWS",
         "orientation": "h",
         "showlegend": true,
         "textposition": "auto",
         "texttemplate": "%{x}",
         "type": "bar",
         "x": [
          3.789473684210526
         ],
         "xaxis": "x",
         "y": [
          "Teras Cikapundung BBWS"
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Place_Id=%{y}<br>Place_Ratings=%{x}<extra></extra>",
         "legendgroup": "Monumen Yogya Kembali",
         "marker": {
          "color": "#FFA15A",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Monumen Yogya Kembali",
         "offsetgroup": "Monumen Yogya Kembali",
         "orientation": "h",
         "showlegend": true,
         "textposition": "auto",
         "texttemplate": "%{x}",
         "type": "bar",
         "x": [
          3.772727272727273
         ],
         "xaxis": "x",
         "y": [
          "Monumen Yogya Kembali"
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Place_Id=%{y}<br>Place_Ratings=%{x}<extra></extra>",
         "legendgroup": "Bukit Jamur",
         "marker": {
          "color": "#19d3f3",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Bukit Jamur",
         "offsetgroup": "Bukit Jamur",
         "orientation": "h",
         "showlegend": true,
         "textposition": "auto",
         "texttemplate": "%{x}",
         "type": "bar",
         "x": [
          3.7666666666666666
         ],
         "xaxis": "x",
         "y": [
          "Bukit Jamur"
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Place_Id=%{y}<br>Place_Ratings=%{x}<extra></extra>",
         "legendgroup": "Bukit Bintang Yogyakarta",
         "marker": {
          "color": "#FF6692",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Bukit Bintang Yogyakarta",
         "offsetgroup": "Bukit Bintang Yogyakarta",
         "orientation": "h",
         "showlegend": true,
         "textposition": "auto",
         "texttemplate": "%{x}",
         "type": "bar",
         "x": [
          3.764705882352941
         ],
         "xaxis": "x",
         "y": [
          "Bukit Bintang Yogyakarta"
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Place_Id=%{y}<br>Place_Ratings=%{x}<extra></extra>",
         "legendgroup": "Monumen Nasional",
         "marker": {
          "color": "#B6E880",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Monumen Nasional",
         "offsetgroup": "Monumen Nasional",
         "orientation": "h",
         "showlegend": true,
         "textposition": "auto",
         "texttemplate": "%{x}",
         "type": "bar",
         "x": [
          3.7222222222222223
         ],
         "xaxis": "x",
         "y": [
          "Monumen Nasional"
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Place_Id=%{y}<br>Place_Ratings=%{x}<extra></extra>",
         "legendgroup": "Glamping Lakeside Rancabali",
         "marker": {
          "color": "#FF97FF",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Glamping Lakeside Rancabali",
         "offsetgroup": "Glamping Lakeside Rancabali",
         "orientation": "h",
         "showlegend": true,
         "textposition": "auto",
         "texttemplate": "%{x}",
         "type": "bar",
         "x": [
          3.7142857142857144
         ],
         "xaxis": "x",
         "y": [
          "Glamping Lakeside Rancabali"
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Place_Id=%{y}<br>Place_Ratings=%{x}<extra></extra>",
         "legendgroup": "Pantai Baron",
         "marker": {
          "color": "#FECB52",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Pantai Baron",
         "offsetgroup": "Pantai Baron",
         "orientation": "h",
         "showlegend": true,
         "textposition": "auto",
         "texttemplate": "%{x}",
         "type": "bar",
         "x": [
          3.6956521739130435
         ],
         "xaxis": "x",
         "y": [
          "Pantai Baron"
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "relative",
        "legend": {
         "title": {
          "text": "Place_Id"
         },
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Top 10 Tourism Destination"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Place_Ratings"
         }
        },
        "yaxis": {
         "anchor": "x",
         "categoryarray": [
          "Pantai Baron",
          "Glamping Lakeside Rancabali",
          "Monumen Nasional",
          "Bukit Bintang Yogyakarta",
          "Bukit Jamur",
          "Monumen Yogya Kembali",
          "Teras Cikapundung BBWS",
          "Kampung Cina",
          "Puncak Gunung Api Purba - Nglanggeran",
          "Keraton Surabaya"
         ],
         "categoryorder": "array",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Place_Id"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.bar(data_frame=testing,text_auto=True,color='Place_Id',x='Place_Ratings',y='Place_Id',title='Top 10 Tourism Destination')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10 tujuan destinasi wisata terbaik menurut rata-rata wisatawan yang sudah melakukan kunjungan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "menggunakan method info untuk melakukan pengecekan *missing values*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_Id          0\n",
       "Place_Id         0\n",
       "Place_Ratings    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tourism_rating.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count  Dtype\n",
      "---  ------         --------------  -----\n",
      " 0   User_Id        10000 non-null  int64\n",
      " 1   Place_Id       10000 non-null  int64\n",
      " 2   Place_Ratings  10000 non-null  int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 234.5 KB\n"
     ]
    }
   ],
   "source": [
    "data_tourism_rating.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 300 entries, 0 to 299\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   User_Id   300 non-null    int64 \n",
      " 1   Location  300 non-null    object\n",
      " 2   Age       300 non-null    int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data_user.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 437 entries, 0 to 436\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Place_Id      437 non-null    int64  \n",
      " 1   Place_Name    437 non-null    object \n",
      " 2   Description   437 non-null    object \n",
      " 3   Category      437 non-null    object \n",
      " 4   City          437 non-null    object \n",
      " 5   Price         437 non-null    int64  \n",
      " 6   Rating        437 non-null    float64\n",
      " 7   Time_Minutes  205 non-null    float64\n",
      " 8   Coordinate    437 non-null    object \n",
      " 9   Lat           437 non-null    float64\n",
      " 10  Long          437 non-null    float64\n",
      " 11  Unnamed: 11   0 non-null      float64\n",
      " 12  Unnamed: 12   437 non-null    int64  \n",
      "dtypes: float64(5), int64(3), object(5)\n",
      "memory usage: 44.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data_tourism_with_id.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   Package         100 non-null    int64 \n",
      " 1   City            100 non-null    object\n",
      " 2   Place_Tourism1  100 non-null    object\n",
      " 3   Place_Tourism2  100 non-null    object\n",
      " 4   Place_Tourism3  100 non-null    object\n",
      " 5   Place_Tourism4  66 non-null     object\n",
      " 6   Place_Tourism5  39 non-null     object\n",
      "dtypes: int64(1), object(6)\n",
      "memory usage: 5.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data_package_tourism.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan dropping column untuk data_tourism_with_id yang tidak digunakan dengan menggunakan method drop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Place_Id</th>\n",
       "      <th>Place_Name</th>\n",
       "      <th>Description</th>\n",
       "      <th>Category</th>\n",
       "      <th>City</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Monumen Nasional</td>\n",
       "      <td>Monumen Nasional atau yang populer disingkat d...</td>\n",
       "      <td>Budaya</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kota Tua</td>\n",
       "      <td>Kota tua di Jakarta, yang juga bernama Kota Tu...</td>\n",
       "      <td>Budaya</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Dunia Fantasi</td>\n",
       "      <td>Dunia Fantasi atau disebut juga Dufan adalah t...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Taman Mini Indonesia Indah (TMII)</td>\n",
       "      <td>Taman Mini Indonesia Indah merupakan suatu kaw...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Atlantis Water Adventure</td>\n",
       "      <td>Atlantis Water Adventure atau dikenal dengan A...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>94000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>433</td>\n",
       "      <td>Museum Mpu Tantular</td>\n",
       "      <td>Museum Negeri Mpu Tantular adalah sebuah museu...</td>\n",
       "      <td>Budaya</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>434</td>\n",
       "      <td>Taman Bungkul</td>\n",
       "      <td>Taman Bungkul adalah taman wisata kota yang te...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>435</td>\n",
       "      <td>Taman Air Mancur Menari Kenjeran</td>\n",
       "      <td>Air mancur menari atau dancing fountain juga a...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>436</td>\n",
       "      <td>Taman Flora Bratang Surabaya</td>\n",
       "      <td>Taman Flora adalah salah satu taman kota di Su...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>437</td>\n",
       "      <td>Gereja Perawan Maria Tak Berdosa Surabaya</td>\n",
       "      <td>Gereja Katolik Kelahiran Santa Perawan Maria m...</td>\n",
       "      <td>Tempat Ibadah</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>437 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Place_Id                                 Place_Name  \\\n",
       "0           1                           Monumen Nasional   \n",
       "1           2                                   Kota Tua   \n",
       "2           3                              Dunia Fantasi   \n",
       "3           4          Taman Mini Indonesia Indah (TMII)   \n",
       "4           5                   Atlantis Water Adventure   \n",
       "..        ...                                        ...   \n",
       "432       433                        Museum Mpu Tantular   \n",
       "433       434                              Taman Bungkul   \n",
       "434       435           Taman Air Mancur Menari Kenjeran   \n",
       "435       436               Taman Flora Bratang Surabaya   \n",
       "436       437  Gereja Perawan Maria Tak Berdosa Surabaya   \n",
       "\n",
       "                                           Description       Category  \\\n",
       "0    Monumen Nasional atau yang populer disingkat d...         Budaya   \n",
       "1    Kota tua di Jakarta, yang juga bernama Kota Tu...         Budaya   \n",
       "2    Dunia Fantasi atau disebut juga Dufan adalah t...  Taman Hiburan   \n",
       "3    Taman Mini Indonesia Indah merupakan suatu kaw...  Taman Hiburan   \n",
       "4    Atlantis Water Adventure atau dikenal dengan A...  Taman Hiburan   \n",
       "..                                                 ...            ...   \n",
       "432  Museum Negeri Mpu Tantular adalah sebuah museu...         Budaya   \n",
       "433  Taman Bungkul adalah taman wisata kota yang te...  Taman Hiburan   \n",
       "434  Air mancur menari atau dancing fountain juga a...  Taman Hiburan   \n",
       "435  Taman Flora adalah salah satu taman kota di Su...  Taman Hiburan   \n",
       "436  Gereja Katolik Kelahiran Santa Perawan Maria m...  Tempat Ibadah   \n",
       "\n",
       "         City   Price  \n",
       "0     Jakarta   20000  \n",
       "1     Jakarta       0  \n",
       "2     Jakarta  270000  \n",
       "3     Jakarta   10000  \n",
       "4     Jakarta   94000  \n",
       "..        ...     ...  \n",
       "432  Surabaya    2000  \n",
       "433  Surabaya       0  \n",
       "434  Surabaya       0  \n",
       "435  Surabaya       0  \n",
       "436  Surabaya   10000  \n",
       "\n",
       "[437 rows x 6 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tourism_with_id.drop(['Rating','Time_Minutes','Coordinate','Lat','Long','Unnamed: 11','Unnamed: 12'],axis=1,inplace=True)\n",
    "data_tourism_with_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Membuat menjadi satu dataset dengan pandas.merge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Place_Id</th>\n",
       "      <th>Place_Ratings</th>\n",
       "      <th>Place_Name</th>\n",
       "      <th>Description</th>\n",
       "      <th>Category</th>\n",
       "      <th>City</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.722222</td>\n",
       "      <td>Monumen Nasional</td>\n",
       "      <td>Monumen Nasional atau yang populer disingkat d...</td>\n",
       "      <td>Budaya</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.840000</td>\n",
       "      <td>Kota Tua</td>\n",
       "      <td>Kota tua di Jakarta, yang juga bernama Kota Tu...</td>\n",
       "      <td>Budaya</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2.526316</td>\n",
       "      <td>Dunia Fantasi</td>\n",
       "      <td>Dunia Fantasi atau disebut juga Dufan adalah t...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2.857143</td>\n",
       "      <td>Taman Mini Indonesia Indah (TMII)</td>\n",
       "      <td>Taman Mini Indonesia Indah merupakan suatu kaw...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3.520000</td>\n",
       "      <td>Atlantis Water Adventure</td>\n",
       "      <td>Atlantis Water Adventure atau dikenal dengan A...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>94000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>433</td>\n",
       "      <td>3.304348</td>\n",
       "      <td>Museum Mpu Tantular</td>\n",
       "      <td>Museum Negeri Mpu Tantular adalah sebuah museu...</td>\n",
       "      <td>Budaya</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>434</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>Taman Bungkul</td>\n",
       "      <td>Taman Bungkul adalah taman wisata kota yang te...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>435</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Taman Air Mancur Menari Kenjeran</td>\n",
       "      <td>Air mancur menari atau dancing fountain juga a...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>436</td>\n",
       "      <td>3.090909</td>\n",
       "      <td>Taman Flora Bratang Surabaya</td>\n",
       "      <td>Taman Flora adalah salah satu taman kota di Su...</td>\n",
       "      <td>Taman Hiburan</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>437</td>\n",
       "      <td>3.289474</td>\n",
       "      <td>Gereja Perawan Maria Tak Berdosa Surabaya</td>\n",
       "      <td>Gereja Katolik Kelahiran Santa Perawan Maria m...</td>\n",
       "      <td>Tempat Ibadah</td>\n",
       "      <td>Surabaya</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>437 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Place_Id  Place_Ratings                                 Place_Name  \\\n",
       "0           1       3.722222                           Monumen Nasional   \n",
       "1           2       2.840000                                   Kota Tua   \n",
       "2           3       2.526316                              Dunia Fantasi   \n",
       "3           4       2.857143          Taman Mini Indonesia Indah (TMII)   \n",
       "4           5       3.520000                   Atlantis Water Adventure   \n",
       "..        ...            ...                                        ...   \n",
       "432       433       3.304348                        Museum Mpu Tantular   \n",
       "433       434       2.400000                              Taman Bungkul   \n",
       "434       435       3.000000           Taman Air Mancur Menari Kenjeran   \n",
       "435       436       3.090909               Taman Flora Bratang Surabaya   \n",
       "436       437       3.289474  Gereja Perawan Maria Tak Berdosa Surabaya   \n",
       "\n",
       "                                           Description       Category  \\\n",
       "0    Monumen Nasional atau yang populer disingkat d...         Budaya   \n",
       "1    Kota tua di Jakarta, yang juga bernama Kota Tu...         Budaya   \n",
       "2    Dunia Fantasi atau disebut juga Dufan adalah t...  Taman Hiburan   \n",
       "3    Taman Mini Indonesia Indah merupakan suatu kaw...  Taman Hiburan   \n",
       "4    Atlantis Water Adventure atau dikenal dengan A...  Taman Hiburan   \n",
       "..                                                 ...            ...   \n",
       "432  Museum Negeri Mpu Tantular adalah sebuah museu...         Budaya   \n",
       "433  Taman Bungkul adalah taman wisata kota yang te...  Taman Hiburan   \n",
       "434  Air mancur menari atau dancing fountain juga a...  Taman Hiburan   \n",
       "435  Taman Flora adalah salah satu taman kota di Su...  Taman Hiburan   \n",
       "436  Gereja Katolik Kelahiran Santa Perawan Maria m...  Tempat Ibadah   \n",
       "\n",
       "         City   Price  \n",
       "0     Jakarta   20000  \n",
       "1     Jakarta       0  \n",
       "2     Jakarta  270000  \n",
       "3     Jakarta   10000  \n",
       "4     Jakarta   94000  \n",
       "..        ...     ...  \n",
       "432  Surabaya    2000  \n",
       "433  Surabaya       0  \n",
       "434  Surabaya       0  \n",
       "435  Surabaya       0  \n",
       "436  Surabaya   10000  \n",
       "\n",
       "[437 rows x 7 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_rekomendasi = pd.merge(data_tourism_rating.groupby('Place_Id')['Place_Ratings'].mean(),data_tourism_with_id,on='Place_Id')\n",
    "data_rekomendasi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modeling with Content Based Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "melakukan import library yang digunakan untuk modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n",
    "from Sastrawi.StopWordRemover.StopWordRemoverFactory import StopWordRemoverFactory\n",
    "tv = TfidfVectorizer(max_features=5000)\n",
    "stem = StemmerFactory().create_stemmer()\n",
    "stopword = StopWordRemoverFactory().create_stop_word_remover()\n",
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "membuat functionn untuk preprocessing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing(data):\n",
    "    data = data.lower()\n",
    "    data = stem.stem(data)\n",
    "    data = stopword.remove(data)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mengumpulkan data text menjadi 1 variabel baru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Place_Id</th>\n",
       "      <th>Place_Name</th>\n",
       "      <th>Tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Monumen Nasional</td>\n",
       "      <td>Monumen Nasional atau yang populer disingkat d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kota Tua</td>\n",
       "      <td>Kota tua di Jakarta, yang juga bernama Kota Tu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Dunia Fantasi</td>\n",
       "      <td>Dunia Fantasi atau disebut juga Dufan adalah t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Taman Mini Indonesia Indah (TMII)</td>\n",
       "      <td>Taman Mini Indonesia Indah merupakan suatu kaw...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Atlantis Water Adventure</td>\n",
       "      <td>Atlantis Water Adventure atau dikenal dengan A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>433</td>\n",
       "      <td>Museum Mpu Tantular</td>\n",
       "      <td>Museum Negeri Mpu Tantular adalah sebuah museu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>434</td>\n",
       "      <td>Taman Bungkul</td>\n",
       "      <td>Taman Bungkul adalah taman wisata kota yang te...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>435</td>\n",
       "      <td>Taman Air Mancur Menari Kenjeran</td>\n",
       "      <td>Air mancur menari atau dancing fountain juga a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>436</td>\n",
       "      <td>Taman Flora Bratang Surabaya</td>\n",
       "      <td>Taman Flora adalah salah satu taman kota di Su...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>437</td>\n",
       "      <td>Gereja Perawan Maria Tak Berdosa Surabaya</td>\n",
       "      <td>Gereja Katolik Kelahiran Santa Perawan Maria m...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>437 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Place_Id                                 Place_Name  \\\n",
       "0           1                           Monumen Nasional   \n",
       "1           2                                   Kota Tua   \n",
       "2           3                              Dunia Fantasi   \n",
       "3           4          Taman Mini Indonesia Indah (TMII)   \n",
       "4           5                   Atlantis Water Adventure   \n",
       "..        ...                                        ...   \n",
       "432       433                        Museum Mpu Tantular   \n",
       "433       434                              Taman Bungkul   \n",
       "434       435           Taman Air Mancur Menari Kenjeran   \n",
       "435       436               Taman Flora Bratang Surabaya   \n",
       "436       437  Gereja Perawan Maria Tak Berdosa Surabaya   \n",
       "\n",
       "                                                  Tags  \n",
       "0    Monumen Nasional atau yang populer disingkat d...  \n",
       "1    Kota tua di Jakarta, yang juga bernama Kota Tu...  \n",
       "2    Dunia Fantasi atau disebut juga Dufan adalah t...  \n",
       "3    Taman Mini Indonesia Indah merupakan suatu kaw...  \n",
       "4    Atlantis Water Adventure atau dikenal dengan A...  \n",
       "..                                                 ...  \n",
       "432  Museum Negeri Mpu Tantular adalah sebuah museu...  \n",
       "433  Taman Bungkul adalah taman wisata kota yang te...  \n",
       "434  Air mancur menari atau dancing fountain juga a...  \n",
       "435  Taman Flora adalah salah satu taman kota di Su...  \n",
       "436  Gereja Katolik Kelahiran Santa Perawan Maria m...  \n",
       "\n",
       "[437 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_content_based_filtering = data_rekomendasi.copy()\n",
    "data_content_based_filtering['Tags'] = data_content_based_filtering['Description'] + ' ' + data_content_based_filtering['Category']\n",
    "data_content_based_filtering.drop(['Price','Place_Ratings','Description','Category','City'],axis=1,inplace=True)\n",
    "data_content_based_filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "apply tags ke function preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Place_Id</th>\n",
       "      <th>Place_Name</th>\n",
       "      <th>Tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Monumen Nasional</td>\n",
       "      <td>monumen nasional populer singkat monas tugu mo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kota Tua</td>\n",
       "      <td>kota tua jakarta juga nama kota tua pusat alun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Dunia Fantasi</td>\n",
       "      <td>dunia fantasi sebut dufan tempat hibur letak k...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Taman Mini Indonesia Indah (TMII)</td>\n",
       "      <td>taman mini indonesia indah rupa suatu kawasan ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Atlantis Water Adventure</td>\n",
       "      <td>atlantis water adventure kenal atlantis ancol ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>433</td>\n",
       "      <td>Museum Mpu Tantular</td>\n",
       "      <td>museum negeri mpu tantular buah museum negeri ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>434</td>\n",
       "      <td>Taman Bungkul</td>\n",
       "      <td>taman bungkul taman wisata kota letak pusat ko...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>435</td>\n",
       "      <td>Taman Air Mancur Menari Kenjeran</td>\n",
       "      <td>air mancur tari dancing fountain ada kawasan j...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>436</td>\n",
       "      <td>Taman Flora Bratang Surabaya</td>\n",
       "      <td>taman flora salah satu taman kota surabaya rup...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>437</td>\n",
       "      <td>Gereja Perawan Maria Tak Berdosa Surabaya</td>\n",
       "      <td>gereja katolik lahir santa perawan maria rupa ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>437 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Place_Id                                 Place_Name  \\\n",
       "0           1                           Monumen Nasional   \n",
       "1           2                                   Kota Tua   \n",
       "2           3                              Dunia Fantasi   \n",
       "3           4          Taman Mini Indonesia Indah (TMII)   \n",
       "4           5                   Atlantis Water Adventure   \n",
       "..        ...                                        ...   \n",
       "432       433                        Museum Mpu Tantular   \n",
       "433       434                              Taman Bungkul   \n",
       "434       435           Taman Air Mancur Menari Kenjeran   \n",
       "435       436               Taman Flora Bratang Surabaya   \n",
       "436       437  Gereja Perawan Maria Tak Berdosa Surabaya   \n",
       "\n",
       "                                                  Tags  \n",
       "0    monumen nasional populer singkat monas tugu mo...  \n",
       "1    kota tua jakarta juga nama kota tua pusat alun...  \n",
       "2    dunia fantasi sebut dufan tempat hibur letak k...  \n",
       "3    taman mini indonesia indah rupa suatu kawasan ...  \n",
       "4    atlantis water adventure kenal atlantis ancol ...  \n",
       "..                                                 ...  \n",
       "432  museum negeri mpu tantular buah museum negeri ...  \n",
       "433  taman bungkul taman wisata kota letak pusat ko...  \n",
       "434  air mancur tari dancing fountain ada kawasan j...  \n",
       "435  taman flora salah satu taman kota surabaya rup...  \n",
       "436  gereja katolik lahir santa perawan maria rupa ...  \n",
       "\n",
       "[437 rows x 3 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_content_based_filtering.Tags = data_content_based_filtering.Tags.apply(preprocessing)\n",
    "data_content_based_filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "membuat countvectorizer dari tfidf secara langsung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectors = tv.fit_transform(data_content_based_filtering.Tags).toarray()\n",
    "vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('zuider', 4961),\n",
       " ('zoo', 4960),\n",
       " ('zona', 4959),\n",
       " ('ziarah', 4958),\n",
       " ('zheng', 4957),\n",
       " ('zeven', 4956),\n",
       " ('zeeland', 4955),\n",
       " ('zaman', 4954),\n",
       " ('yustinus', 4953),\n",
       " ('yusgiantoro', 4952),\n",
       " ('yunani', 4951),\n",
       " ('yukata', 4950),\n",
       " ('yudhoyono', 4949),\n",
       " ('yuan', 4948),\n",
       " ('yogyakarta', 4947),\n",
       " ('yogya', 4946),\n",
       " ('yin', 4945),\n",
       " ('yesus', 4944),\n",
       " ('yesaya', 4943),\n",
       " ('yb', 4942),\n",
       " ('yayasan', 4941),\n",
       " ('yantekkes', 4940),\n",
       " ('yani', 4939),\n",
       " ('yang', 4938),\n",
       " ('yakin', 4937),\n",
       " ('yaitu', 4936),\n",
       " ('wujud', 4935),\n",
       " ('wudhu', 4934),\n",
       " ('wu', 4933),\n",
       " ('wot', 4932),\n",
       " ('world', 4931),\n",
       " ('workshop', 4930),\n",
       " ('wonosobo', 4929),\n",
       " ('wonosari', 4928),\n",
       " ('wonorejo', 4927),\n",
       " ('won', 4926),\n",
       " ('wolff', 4925),\n",
       " ('wiyogo', 4924),\n",
       " ('wiwit', 4923),\n",
       " ('wittenberg', 4922),\n",
       " ('wisnu', 4921),\n",
       " ('wisma', 4920),\n",
       " ('wisatawan', 4919),\n",
       " ('wisata', 4918),\n",
       " ('wirasonjaya', 4917),\n",
       " ('winogo', 4916),\n",
       " ('windu', 4915),\n",
       " ('winarsih', 4914),\n",
       " ('wilhelmina', 4913),\n",
       " ('wilde', 4912),\n",
       " ('wilayah', 4911),\n",
       " ('wijilan', 4910),\n",
       " ('wijayandaru', 4909),\n",
       " ('widodo', 4908),\n",
       " ('wibawa', 4907),\n",
       " ('wib', 4906),\n",
       " ('wetan', 4905),\n",
       " ('westmaas', 4904),\n",
       " ('wening', 4903),\n",
       " ('welut', 4902),\n",
       " ('weltevreden', 4901),\n",
       " ('welter', 4900),\n",
       " ('wehrkreise', 4899),\n",
       " ('weekend', 4898),\n",
       " ('weekday', 4897),\n",
       " ('wedus', 4896),\n",
       " ('wediombo', 4895),\n",
       " ('wedhiamba', 4894),\n",
       " ('wedani', 4893),\n",
       " ('wc', 4892),\n",
       " ('wbu', 4891),\n",
       " ('wayang', 4890),\n",
       " ('wawas', 4889),\n",
       " ('watu', 4888),\n",
       " ('waterproof', 4887),\n",
       " ('waterpark', 4886),\n",
       " ('waterlooplein', 4885),\n",
       " ('waterloo', 4884),\n",
       " ('waterboom', 4883),\n",
       " ('waterbom', 4882),\n",
       " ('water', 4881),\n",
       " ('waspada', 4880),\n",
       " ('washington', 4879),\n",
       " ('wasan', 4878),\n",
       " ('warungboto', 4877),\n",
       " ('warung', 4876),\n",
       " ('warni', 4875),\n",
       " ('warnasari', 4874),\n",
       " ('warna', 4873),\n",
       " ('waris', 4872),\n",
       " ('warior', 4871),\n",
       " ('warga', 4870),\n",
       " ('waralaba', 4869),\n",
       " ('wanita', 4868),\n",
       " ('wangunharja', 4867),\n",
       " ('wangsit', 4866),\n",
       " ('wangsa', 4865),\n",
       " ('wana', 4864),\n",
       " ('wall', 4863),\n",
       " ('walk', 4862),\n",
       " ('walisongo', 4861),\n",
       " ('walikota', 4860),\n",
       " ('wali', 4859),\n",
       " ('walaupun', 4858),\n",
       " ('waktu', 4857),\n",
       " ('wakil', 4856),\n",
       " ('wajib', 4855),\n",
       " ('wajah', 4854),\n",
       " ('waisak', 4853),\n",
       " ('wahyu', 4852),\n",
       " ('wahid', 4851),\n",
       " ('wahana', 4850),\n",
       " ('wage', 4849),\n",
       " ('waduk', 4848),\n",
       " ('wader', 4847),\n",
       " ('wadah', 4846),\n",
       " ('vulkaniknya', 4845),\n",
       " ('vulkanik', 4844),\n",
       " ('vrouwe', 4843),\n",
       " ('vreden', 4842),\n",
       " ('vredeburg', 4841),\n",
       " ('von', 4840),\n",
       " ('voc', 4839),\n",
       " ('vl', 4838),\n",
       " ('vital', 4837),\n",
       " ('visual', 4836),\n",
       " ('visioner', 4835),\n",
       " ('visi', 4834),\n",
       " ('visa', 4833),\n",
       " ('viral', 4832),\n",
       " ('vip', 4831),\n",
       " ('vinck', 4830),\n",
       " ('village', 4829),\n",
       " ('villa', 4828),\n",
       " ('vijhoek', 4827),\n",
       " ('vii', 4826),\n",
       " ('view', 4825),\n",
       " ('video', 4824),\n",
       " ('veteran', 4823),\n",
       " ('vertikal', 4822),\n",
       " ('versi', 4821),\n",
       " ('vereeniging', 4820),\n",
       " ('vereenigde', 4819),\n",
       " ('venue', 4818),\n",
       " ('venezuela', 4817),\n",
       " ('vegetasi', 4816),\n",
       " ('varietas', 4815),\n",
       " ('variasi', 4814),\n",
       " ('varian', 4813),\n",
       " ('vans', 4812),\n",
       " ('vanda', 4811),\n",
       " ('van', 4810),\n",
       " ('uum', 4809),\n",
       " ('uu', 4808),\n",
       " ('utuk', 4807),\n",
       " ('utuh', 4806),\n",
       " ('utrecht', 4805),\n",
       " ('utomo', 4804),\n",
       " ('utc', 4803),\n",
       " ('utas', 4802),\n",
       " ('utara', 4801),\n",
       " ('utan', 4800),\n",
       " ('utama', 4799),\n",
       " ('usung', 4798),\n",
       " ('usul', 4797),\n",
       " ('usir', 4796),\n",
       " ('usia', 4795),\n",
       " ('usaha', 4794),\n",
       " ('urus', 4793),\n",
       " ('urban', 4792),\n",
       " ('uras', 4791),\n",
       " ('urap', 4790),\n",
       " ('uptd', 4789),\n",
       " ('upt', 4788),\n",
       " ('upside', 4787),\n",
       " ('upi', 4786),\n",
       " ('upaya', 4785),\n",
       " ('upacara', 4784),\n",
       " ('upa', 4783),\n",
       " ('untung', 4782),\n",
       " ('untun', 4781),\n",
       " ('untuk', 4780),\n",
       " ('unta', 4779),\n",
       " ('unsur', 4778),\n",
       " ('unjung', 4777),\n",
       " ('universitas', 4776),\n",
       " ('unit', 4775),\n",
       " ('union', 4774),\n",
       " ('unik', 4773),\n",
       " ('uni', 4772),\n",
       " ('ungkap', 4771),\n",
       " ('unggul', 4770),\n",
       " ('ungar', 4769),\n",
       " ('unesco', 4768),\n",
       " ('undur', 4767),\n",
       " ('underwater', 4766),\n",
       " ('undang', 4765),\n",
       " ('unda', 4764),\n",
       " ('umur', 4763),\n",
       " ('umum', 4762),\n",
       " ('umbulharjo', 4761),\n",
       " ('umbul', 4760),\n",
       " ('umat', 4759),\n",
       " ('umar', 4758),\n",
       " ('ullen', 4757),\n",
       " ('ull', 4756),\n",
       " ('ulating', 4755),\n",
       " ('ular', 4754),\n",
       " ('ulang', 4753),\n",
       " ('ulam', 4752),\n",
       " ('ukur', 4751),\n",
       " ('ukir', 4750),\n",
       " ('ujung', 4749),\n",
       " ('ugm', 4748),\n",
       " ('uduk', 4747),\n",
       " ('udjo', 4746),\n",
       " ('udara', 4745),\n",
       " ('udang', 4744),\n",
       " ('ubah', 4743),\n",
       " ('uap', 4742),\n",
       " ('uang', 4741),\n",
       " ('type', 4740),\n",
       " ('tvri', 4739),\n",
       " ('tutup', 4738),\n",
       " ('tusuk', 4737),\n",
       " ('tus', 4736),\n",
       " ('turut', 4735),\n",
       " ('turun', 4734),\n",
       " ('turki', 4733),\n",
       " ('turism', 4732),\n",
       " ('turis', 4731),\n",
       " ('tur', 4730),\n",
       " ('tuntun', 4729),\n",
       " ('tuntang', 4728),\n",
       " ('tunjung', 4727),\n",
       " ('tunjuk', 4726),\n",
       " ('tunjang', 4725),\n",
       " ('tunggul', 4724),\n",
       " ('tunggu', 4723),\n",
       " ('tunggang', 4722),\n",
       " ('tunggal', 4721),\n",
       " ('tung', 4720),\n",
       " ('tunda', 4719),\n",
       " ('tumpuk', 4718),\n",
       " ('tumpu', 4717),\n",
       " ('tumpas', 4716),\n",
       " ('tumpang', 4715),\n",
       " ('tumpah', 4714),\n",
       " ('tumbuh', 4713),\n",
       " ('tulis', 4712),\n",
       " ('tulip', 4711),\n",
       " ('tulang', 4710),\n",
       " ('tukar', 4709),\n",
       " ('tukang', 4708),\n",
       " ('tujuh', 4707),\n",
       " ('tuju', 4706),\n",
       " ('tuhan', 4705),\n",
       " ('tugu', 4704),\n",
       " ('tugas', 4703),\n",
       " ('tufa', 4702),\n",
       " ('tuduh', 4701),\n",
       " ('tubun', 4700),\n",
       " ('tubing', 4699),\n",
       " ('tuban', 4698),\n",
       " ('tuan', 4697),\n",
       " ('tualang', 4696),\n",
       " ('tuai', 4695),\n",
       " ('tua', 4694),\n",
       " ('tu', 4693),\n",
       " ('tsb', 4692),\n",
       " ('try', 4691),\n",
       " ('trs', 4690),\n",
       " ('trisuci', 4689),\n",
       " ('trisik', 4688),\n",
       " ('trip', 4687),\n",
       " ('trimurti', 4686),\n",
       " ('triliun', 4685),\n",
       " ('trikora', 4684),\n",
       " ('tridi', 4683),\n",
       " ('tridharma', 4682),\n",
       " ('trick', 4681),\n",
       " ('tri', 4680),\n",
       " ('trekking', 4679),\n",
       " ('trek', 4678),\n",
       " ('tree', 4677),\n",
       " ('traveler', 4676),\n",
       " ('travel', 4675),\n",
       " ('tratag', 4674),\n",
       " ('transportasi', 4673),\n",
       " ('translit', 4672),\n",
       " ('transformasi', 4671),\n",
       " ('transendental', 4670),\n",
       " ('trans', 4669),\n",
       " ('training', 4668),\n",
       " ('trail', 4667),\n",
       " ('tragedi', 4666),\n",
       " ('traffic', 4665),\n",
       " ('tradisional', 4664),\n",
       " ('tradisi', 4663),\n",
       " ('track', 4662),\n",
       " ('tpa', 4661),\n",
       " ('town', 4660),\n",
       " ('tower', 4659),\n",
       " ('total', 4658),\n",
       " ('tot', 4657),\n",
       " ('torpedo', 4656),\n",
       " ('toreh', 4655),\n",
       " ('topeng', 4654),\n",
       " ('topang', 4653),\n",
       " ('tonton', 4652),\n",
       " ('tonjol', 4651),\n",
       " ('tongkrong', 4650),\n",
       " ('tongkol', 4649),\n",
       " ('tongkat', 4648),\n",
       " ('ton', 4647),\n",
       " ('tolak', 4646),\n",
       " ('tol', 4645),\n",
       " ('tokoh', 4644),\n",
       " ('toko', 4643),\n",
       " ('toilet', 4642),\n",
       " ('toha', 4641),\n",
       " ('toge', 4640),\n",
       " ('tobat', 4639),\n",
       " ('toa', 4638),\n",
       " ('tni', 4637),\n",
       " ('tmii', 4636),\n",
       " ('tlogo', 4635),\n",
       " ('tkr', 4634),\n",
       " ('tk', 4633),\n",
       " ('tjokropanolo', 4632),\n",
       " ('tjen', 4631),\n",
       " ('tiup', 4630),\n",
       " ('titik', 4629),\n",
       " ('titi', 4628),\n",
       " ('tirto', 4627),\n",
       " ('tirai', 4626),\n",
       " ('tipu', 4625),\n",
       " ('tipis', 4624),\n",
       " ('tipe', 4623),\n",
       " ('tiongkok', 4622),\n",
       " ('tionghoa', 4621),\n",
       " ('tinjomoyo', 4620),\n",
       " ('tingkat', 4619),\n",
       " ('tingi', 4618),\n",
       " ('tinggi', 4617),\n",
       " ('tinggal', 4616),\n",
       " ('tindih', 4615),\n",
       " ('tindak', 4614),\n",
       " ('timur', 4613),\n",
       " ('timpa', 4612),\n",
       " ('timbul', 4611),\n",
       " ('timbang', 4610),\n",
       " ('timang', 4609),\n",
       " ('tim', 4608),\n",
       " ('tilu', 4607),\n",
       " ('tikung', 4606),\n",
       " ('tiket', 4605),\n",
       " ('tii', 4604),\n",
       " ('tiga', 4603),\n",
       " ('tien', 4602),\n",
       " ('tidur', 4601),\n",
       " ('tidung', 4600),\n",
       " ('tidak', 4599),\n",
       " ('tibur', 4598),\n",
       " ('tiberias', 4597),\n",
       " ('tiba', 4596),\n",
       " ('tiap', 4595),\n",
       " ('tiang', 4594),\n",
       " ('tian', 4593),\n",
       " ('tiada', 4592),\n",
       " ('ti', 4591),\n",
       " ('thr', 4590),\n",
       " ('thp', 4589),\n",
       " ('thompson', 4588),\n",
       " ('thomas', 4587),\n",
       " ('thn', 4586),\n",
       " ('thian', 4585),\n",
       " ('theme', 4584),\n",
       " ('theatre', 4583),\n",
       " ('the', 4582),\n",
       " ('thamrin', 4581),\n",
       " ('thailand', 4580),\n",
       " ('tewas', 4579),\n",
       " ('teuku', 4578),\n",
       " ('tetes', 4577),\n",
       " ('tetapi', 4576),\n",
       " ('tetap', 4575),\n",
       " ('tetangga', 4574),\n",
       " ('tetak', 4573),\n",
       " ('terus', 4572),\n",
       " ('tertutp', 4571),\n",
       " ('tersier', 4570),\n",
       " ('terpa', 4569),\n",
       " ('terowongan', 4568),\n",
       " ('teropong', 4567),\n",
       " ('terong', 4566),\n",
       " ('terobos', 4565),\n",
       " ('ternak', 4564),\n",
       " ('terna', 4563),\n",
       " ('terminal', 4562),\n",
       " ('terlalu', 4561),\n",
       " ('terkadang', 4560),\n",
       " ('terjun', 4559),\n",
       " ('terjerembab', 4558),\n",
       " ('terjemah', 4557),\n",
       " ('terjal', 4556),\n",
       " ('teritampa', 4555),\n",
       " ('terbit', 4554),\n",
       " ('terbing', 4553),\n",
       " ('terbang', 4552),\n",
       " ('terawang', 4551),\n",
       " ('teratai', 4550),\n",
       " ('terasering', 4549),\n",
       " ('teras', 4548),\n",
       " ('terapi', 4547),\n",
       " ('terap', 4546),\n",
       " ('terang', 4545),\n",
       " ('tepus', 4544),\n",
       " ('tepi', 4543),\n",
       " ('tepat', 4542),\n",
       " ('teori', 4541),\n",
       " ('tenun', 4540),\n",
       " ('tentu', 4539),\n",
       " ('tentara', 4538),\n",
       " ('tentang', 4537),\n",
       " ('tengkleng', 4536),\n",
       " ('tenggelam', 4535),\n",
       " ('tenggara', 4534),\n",
       " ('tengara', 4533),\n",
       " ('tengah', 4532),\n",
       " ('teng', 4531),\n",
       " ('tenda', 4530),\n",
       " ('tenar', 4529),\n",
       " ('tenang', 4528),\n",
       " ('tenaga', 4527),\n",
       " ('ten', 4526),\n",
       " ('temu', 4525),\n",
       " ('tempur', 4524),\n",
       " ('tempuh', 4523),\n",
       " ('tempo', 4522),\n",
       " ('templek', 4521),\n",
       " ('temple', 4520),\n",
       " ('temperatur', 4519),\n",
       " ('tempel', 4518),\n",
       " ('tempat', 4517),\n",
       " ('temoloyo', 4516),\n",
       " ('tembus', 4515),\n",
       " ('tembok', 4514),\n",
       " ('tembi', 4513),\n",
       " ('tembel', 4512),\n",
       " ('tembalang', 4511),\n",
       " ('tembakau', 4510),\n",
       " ('tembak', 4509),\n",
       " ('tematik', 4508),\n",
       " ('teman', 4507),\n",
       " ('tema', 4506),\n",
       " ('teluk', 4505),\n",
       " ('telor', 4504),\n",
       " ('telomoyo', 4503),\n",
       " ('teliti', 4502),\n",
       " ('telinga', 4501),\n",
       " ('televisi', 4500),\n",
       " ('teletubbies', 4499),\n",
       " ('telepon', 4498),\n",
       " ('telekomunikasi', 4497),\n",
       " ('telegraph', 4496),\n",
       " ('telegraf', 4495),\n",
       " ('telah', 4494),\n",
       " ('tel', 4493),\n",
       " ('tektona', 4492),\n",
       " ('tekstil', 4491),\n",
       " ('teknologi', 4490),\n",
       " ('teknis', 4489),\n",
       " ('teknik', 4488),\n",
       " ('teki', 4487),\n",
       " ('teka', 4486),\n",
       " ('tek', 4485),\n",
       " ('tejun', 4484),\n",
       " ('tehnologi', 4483),\n",
       " ('teh', 4482),\n",
       " ('tegas', 4481),\n",
       " ('tegang', 4480),\n",
       " ('tegallega', 4479),\n",
       " ('tee', 4478),\n",
       " ('teduh', 4477),\n",
       " ('tedapat', 4476),\n",
       " ('tech', 4475),\n",
       " ('tebu', 4474),\n",
       " ('tebing', 4473),\n",
       " ('tebet', 4472),\n",
       " ('tebal', 4471),\n",
       " ('teater', 4470),\n",
       " ('team', 4469),\n",
       " ('tea', 4468),\n",
       " ('te', 4467),\n",
       " ('tby', 4466),\n",
       " ('tbk', 4465),\n",
       " ('tbjb', 4464),\n",
       " ('tawar', 4463),\n",
       " ('tawan', 4462),\n",
       " ('tauhiid', 4461),\n",
       " ('tauhid', 4460),\n",
       " ('tataraning', 4459),\n",
       " ('tatar', 4458),\n",
       " ('tatanan', 4457),\n",
       " ('tatah', 4456),\n",
       " ('tata', 4455),\n",
       " ('tasikmalaya', 4454),\n",
       " ('tarik', 4453),\n",
       " ('tarif', 4452),\n",
       " ('tari', 4451),\n",
       " ('target', 4450),\n",
       " ('tara', 4449),\n",
       " ('tar', 4448),\n",
       " ('tapi', 4447),\n",
       " ('tapa', 4446),\n",
       " ('tanya', 4445),\n",
       " ('tantular', 4444),\n",
       " ('tantang', 4443),\n",
       " ('tanpa', 4442),\n",
       " ('tank', 4441),\n",
       " ('tanjungsari', 4440),\n",
       " ('tanjung', 4439),\n",
       " ('tanjidor', 4438),\n",
       " ('tanjak', 4437),\n",
       " ('tani', 4436),\n",
       " ('tangkuban', 4435),\n",
       " ('tangkis', 4434),\n",
       " ('tangki', 4433),\n",
       " ('tangkar', 4432),\n",
       " ('tangkap', 4431),\n",
       " ('tanggung', 4430),\n",
       " ('tanggan', 4429),\n",
       " ('tanggal', 4428),\n",
       " ('tangga', 4427),\n",
       " ('tangerang', 4426),\n",
       " ('tangan', 4425),\n",
       " ('tangah', 4424),\n",
       " ('tang', 4423),\n",
       " ('tandus', 4422),\n",
       " ('tanduk', 4421),\n",
       " ('tandu', 4420),\n",
       " ('tandjoeng', 4419),\n",
       " ('tandes', 4418),\n",
       " ('tanda', 4417),\n",
       " ('tanam', 4416),\n",
       " ('tanah', 4415),\n",
       " ('tan', 4414),\n",
       " ('tamu', 4413),\n",
       " ('tampung', 4412),\n",
       " ('tampil', 4411),\n",
       " ('tampak', 4410),\n",
       " ('tambang', 4409),\n",
       " ('tambal', 4408),\n",
       " ('tambaksari', 4407),\n",
       " ('tambakboyo', 4406),\n",
       " ('tambak', 4405),\n",
       " ('tambah', 4404),\n",
       " ('tamansari', 4403),\n",
       " ('taman', 4402),\n",
       " ('tali', 4401),\n",
       " ('takut', 4400),\n",
       " ('takjub', 4399),\n",
       " ('takeshi', 4398),\n",
       " ('tak', 4397),\n",
       " ('tajam', 4396),\n",
       " ('taiwan', 4395),\n",
       " ('tailor', 4394),\n",
       " ('tahura', 4393),\n",
       " ('tahun', 4392),\n",
       " ('tahu', 4391),\n",
       " ('tahta', 4390),\n",
       " ('tahap', 4389),\n",
       " ('tahan', 4388),\n",
       " ('tafso', 4387),\n",
       " ('tadi', 4386),\n",
       " ('tabur', 4385),\n",
       " ('tabanas', 4384),\n",
       " ('taban', 4383),\n",
       " ('syuting', 4382),\n",
       " ('syuro', 4381),\n",
       " ('syukur', 4380),\n",
       " ('syarat', 4379),\n",
       " ('syailendra', 4378),\n",
       " ('syahdu', 4377),\n",
       " ('swasta', 4376),\n",
       " ('swafoto', 4375),\n",
       " ('suvenir', 4374),\n",
       " ('suturejo', 4373),\n",
       " ('sutrisno', 4372),\n",
       " ('sutra', 4371),\n",
       " ('sutiyoso', 4370),\n",
       " ('susur', 4369),\n",
       " ('susun', 4368),\n",
       " ('susu', 4367),\n",
       " ('susilo', 4366),\n",
       " ('susah', 4365),\n",
       " ('suryani', 4364),\n",
       " ('surya', 4363),\n",
       " ('surut', 4362),\n",
       " ('suropati', 4361),\n",
       " ('suroboyo', 4360),\n",
       " ('suro', 4359),\n",
       " ('surga', 4358),\n",
       " ('surat', 4357),\n",
       " ('suramadu', 4356),\n",
       " ('surakarta', 4355),\n",
       " ('surabaya', 4354),\n",
       " ('sura', 4353),\n",
       " ('suprapto', 4352),\n",
       " ('superhero', 4351),\n",
       " ('sup', 4350),\n",
       " ('sunset', 4349),\n",
       " ('sunrise', 4348),\n",
       " ('sungguh', 4347),\n",
       " ('sungai', 4346),\n",
       " ('sunga', 4345),\n",
       " ('sundial', 4344),\n",
       " ('sundak', 4343),\n",
       " ('sunda', 4342),\n",
       " ('sunat', 4341),\n",
       " ('sunaryo', 4340),\n",
       " ('sunan', 4339),\n",
       " ('sumurbandung', 4338),\n",
       " ('sumur', 4337),\n",
       " ('sumpah', 4336),\n",
       " ('sumiati', 4335),\n",
       " ('sumenep', 4334),\n",
       " ('sumedang', 4333),\n",
       " ('sumbu', 4332),\n",
       " ('sumbing', 4331),\n",
       " ('sumberharjo', 4330),\n",
       " ('sumber', 4329),\n",
       " ('sumbang', 4328),\n",
       " ('sulung', 4327),\n",
       " ('sultan', 4326),\n",
       " ('sulit', 4325),\n",
       " ('sulfur', 4324),\n",
       " ('sulawesi', 4323),\n",
       " ('sulap', 4322),\n",
       " ('suku', 4321),\n",
       " ('sukses', 4320),\n",
       " ('sukolilo', 4319),\n",
       " ('sukarno', 4318),\n",
       " ('sukarasa', 4317),\n",
       " ('suka', 4316),\n",
       " ('suhu', 4315),\n",
       " ('suguh', 4314),\n",
       " ('sudut', 4313),\n",
       " ('sudirman', 4312),\n",
       " ('sudibyo', 4311),\n",
       " ('sudah', 4310),\n",
       " ('suci', 4309),\n",
       " ('subur', 4308),\n",
       " ('subroto', 4307),\n",
       " ('subang', 4306),\n",
       " ('sub', 4305),\n",
       " ('suatu', 4304),\n",
       " ('suasanan', 4303),\n",
       " ('suasana', 4302),\n",
       " ('suara', 4301),\n",
       " ('suaka', 4300),\n",
       " ('stupa', 4299),\n",
       " ('stukturnya', 4298),\n",
       " ('studio', 4297),\n",
       " ('struktur', 4296),\n",
       " ('stres', 4295),\n",
       " ('street', 4294),\n",
       " ('stratovulcano', 4293),\n",
       " ('stratovolcano', 4292),\n",
       " ('strato', 4291),\n",
       " ('strategis', 4290),\n",
       " ('straat', 4289),\n",
       " ('stovia', 4288),\n",
       " ('stone', 4287),\n",
       " ('stok', 4286),\n",
       " ('stirum', 4285),\n",
       " ('stijl', 4284),\n",
       " ('sterrenwacht', 4283),\n",
       " ('sterrenkundige', 4282),\n",
       " ('stedelijk', 4281),\n",
       " ('stay', 4280),\n",
       " ('status', 4279),\n",
       " ('statik', 4278),\n",
       " ('stasiun', 4277),\n",
       " ('starhill', 4276),\n",
       " ('standing', 4275),\n",
       " ('standar', 4274),\n",
       " ('stand', 4273),\n",
       " ('stamford', 4272),\n",
       " ('stalls', 4271),\n",
       " ('stagen', 4270),\n",
       " ('staf', 4269),\n",
       " ('stadion', 4268),\n",
       " ('stadhuis', 4267),\n",
       " ('st', 4266),\n",
       " ('ssas', 4265),\n",
       " ('srunggo', 4264),\n",
       " ('sriwijaya', 4263),\n",
       " ('srigunting', 4262),\n",
       " ('srigading', 4261),\n",
       " ('sri', 4260),\n",
       " ('srengseng', 4259),\n",
       " ('spring', 4258),\n",
       " ('spotnya', 4257),\n",
       " ('spot', 4256),\n",
       " ('spiritualitas', 4255),\n",
       " ('spiritual', 4254),\n",
       " ('spesimen', 4253),\n",
       " ('spesifik', 4252),\n",
       " ('spesies', 4251),\n",
       " ('spesial', 4250),\n",
       " ('spektakuler', 4249),\n",
       " ('speedboat', 4248),\n",
       " ('spbu', 4247),\n",
       " ('spathodea', 4246),\n",
       " ('sparkling', 4245),\n",
       " ('spanyol', 4244),\n",
       " ('space', 4243),\n",
       " ('soviet', 4242),\n",
       " ('souvenir', 4241),\n",
       " ('soto', 4240),\n",
       " ('sosrowijayan', 4239),\n",
       " ('sosromenduran', 4238),\n",
       " ('sosok', 4237),\n",
       " ('sosmed', 4236),\n",
       " ('sosial', 4235),\n",
       " ('sorot', 4234),\n",
       " ('sorga', 4233),\n",
       " ('soreang', 4232),\n",
       " ('sore', 4231),\n",
       " ('sonobudoyo', 4230),\n",
       " ('songo', 4229),\n",
       " ('songgo', 4228),\n",
       " ('solusi', 4227),\n",
       " ('solo', 4226),\n",
       " ('soft', 4225),\n",
       " ('soesilo', 4224),\n",
       " ('soerabaia', 4223),\n",
       " ('soeprapto', 4222),\n",
       " ('soenarto', 4221),\n",
       " ('soemowidigdo', 4220),\n",
       " ('soemoprawiro', 4219),\n",
       " ('soekarnoputri', 4218),\n",
       " ('soekarno', 4217),\n",
       " ('soeharto', 4216),\n",
       " ('soedjatmoko', 4215),\n",
       " ('soedirman', 4214),\n",
       " ('soedarman', 4213),\n",
       " ('soebadio', 4212),\n",
       " ('social', 4211),\n",
       " ('sobat', 4210),\n",
       " ('sob', 4209),\n",
       " ('snow', 4208),\n",
       " ('snorkeling', 4207),\n",
       " ('smg', 4206),\n",
       " ('smart', 4205),\n",
       " ('sma', 4204),\n",
       " ('slors', 4203),\n",
       " ('slider', 4202),\n",
       " ('slempret', 4201),\n",
       " ('sleman', 4200),\n",
       " ('slamet', 4199),\n",
       " ('skywalk', 4198),\n",
       " ('sky', 4197),\n",
       " ('skeber', 4196),\n",
       " ('ske', 4195),\n",
       " ('skating', 4194),\n",
       " ('skala', 4193),\n",
       " ('sk', 4192),\n",
       " ('sjahrial', 4191),\n",
       " ('siwarak', 4190),\n",
       " ('siwagrha', 4189),\n",
       " ('siwa', 4188),\n",
       " ('siung', 4187),\n",
       " ('situwangi', 4186),\n",
       " ('situs', 4185),\n",
       " ('situasi', 4184),\n",
       " ('situ', 4183),\n",
       " ('sita', 4182),\n",
       " ('siswa', 4181),\n",
       " ('sister', 4180),\n",
       " ('sisik', 4179),\n",
       " ('sisih', 4178),\n",
       " ('sisi', 4177),\n",
       " ('sisa', 4176),\n",
       " ('sirsak', 4175),\n",
       " ('sirkus', 4174),\n",
       " ('sirkaya', 4173),\n",
       " ('sirih', 4172),\n",
       " ('sirat', 4171),\n",
       " ('sirap', 4170),\n",
       " ('sir', 4169),\n",
       " ('sion', 4168),\n",
       " ('siola', 4167),\n",
       " ('sinu', 4166),\n",
       " ('sinode', 4165),\n",
       " ('sinkhole', 4164),\n",
       " ('sini', 4163),\n",
       " ('singkat', 4162),\n",
       " ('singgung', 4161),\n",
       " ('singgah', 4160),\n",
       " ('singapura', 4159),\n",
       " ('singapore', 4158),\n",
       " ('singa', 4157),\n",
       " ('sindu', 4156),\n",
       " ('sindoro', 4155),\n",
       " ('sinar', 4154),\n",
       " ('simulasi', 4153),\n",
       " ('simpanse', 4152),\n",
       " ('simpang', 4151),\n",
       " ('simpan', 4150),\n",
       " ('simongan', 4149),\n",
       " ('simetris', 4148),\n",
       " ('simbolis', 4147),\n",
       " ('simbol', 4146),\n",
       " ('simbar', 4145),\n",
       " ('siliwangi', 4144),\n",
       " ('silandak', 4143),\n",
       " ('silam', 4142),\n",
       " ('silaban', 4141),\n",
       " ('sila', 4140),\n",
       " ('sikap', 4139),\n",
       " ('sih', 4138),\n",
       " ('sifat', 4137),\n",
       " ('sien', 4136),\n",
       " ('sidomulyo', 4135),\n",
       " ('sidomukti', 4134),\n",
       " ('sidoarjo', 4133),\n",
       " ('sidang', 4132),\n",
       " ('sibuk', 4131),\n",
       " ('siar', 4130),\n",
       " ('siapa', 4129),\n",
       " ('siap', 4128),\n",
       " ('siang', 4127),\n",
       " ('si', 4126),\n",
       " ('shopping', 4125),\n",
       " ('shooting', 4124),\n",
       " ('shirley', 4123),\n",
       " ('shi', 4122),\n",
       " ('sherina', 4121),\n",
       " ('shang', 4120),\n",
       " ('shalat', 4119),\n",
       " ('sewu', 4118),\n",
       " ('sewa', 4117),\n",
       " ('setu', 4116),\n",
       " ('setiya', 4115),\n",
       " ('setinggi', 4114),\n",
       " ('sete', 4113),\n",
       " ('setapak', 4112),\n",
       " ('sesuai', 4111),\n",
       " ('sespuh', 4110),\n",
       " ('sesneg', 4109),\n",
       " ('sesat', 4108),\n",
       " ('servis', 4107),\n",
       " ('seruni', 4106),\n",
       " ('seru', 4105),\n",
       " ('serta', 4104),\n",
       " ('sero', 4103),\n",
       " ('sermo', 4102),\n",
       " ('seringkali', 4101),\n",
       " ('sering', 4100),\n",
       " ('serikat', 4099),\n",
       " ('serbaguna', 4098),\n",
       " ('serba', 4097),\n",
       " ('serasa', 4096),\n",
       " ('serap', 4095),\n",
       " ('serang', 4094),\n",
       " ('serambi', 4093),\n",
       " ('serah', 4092),\n",
       " ('seragam', 4091),\n",
       " ('sepuh', 4090),\n",
       " ('september', 4089),\n",
       " ('seprti', 4088),\n",
       " ('sepi', 4087),\n",
       " ('seperti', 4086),\n",
       " ('sepenuh', 4085),\n",
       " ('sepeda', 4084),\n",
       " ('sepatu', 4083),\n",
       " ('separatis', 4082),\n",
       " ('seoul', 4081),\n",
       " ('seol', 4080),\n",
       " ('sentuh', 4079),\n",
       " ('sentral', 4078),\n",
       " ('sentra', 4077),\n",
       " ('sentir', 4076),\n",
       " ('sentalu', 4075),\n",
       " ('sensasi', 4074),\n",
       " ('senopati', 4073),\n",
       " ('senjata', 4072),\n",
       " ('senja', 4071),\n",
       " ('senior', 4070),\n",
       " ('senin', 4069),\n",
       " ('seniman', 4068),\n",
       " ('seni', 4067),\n",
       " ('sengketa', 4066),\n",
       " ('sengaja', 4065),\n",
       " ('senen', 4064),\n",
       " ('sendiri', 4063),\n",
       " ('sendangsari', 4062),\n",
       " ('sendang', 4061),\n",
       " ('senantiasa', 4060),\n",
       " ('senang', 4059),\n",
       " ('semua', 4058),\n",
       " ('sempurna', 4057),\n",
       " ('sempura', 4056),\n",
       " ('sempit', 4055),\n",
       " ('sempat', 4054),\n",
       " ('semirang', 4053),\n",
       " ('seminar', 4052),\n",
       " ('semi', 4051),\n",
       " ('semesta', 4050),\n",
       " ('semen', 4049),\n",
       " ('semburanapi', 4048),\n",
       " ('sembur', 4047),\n",
       " ('sembunyi', 4046),\n",
       " ('sembuh', 4045),\n",
       " ('semboyan', 4044),\n",
       " ('sembilan', 4043),\n",
       " ('sembari', 4042),\n",
       " ('sembako', 4041),\n",
       " ('sembahyang', 4040),\n",
       " ('sembah', 4039),\n",
       " ('semayam', 4038),\n",
       " ('semarang', 4037),\n",
       " ('semangat', 4036),\n",
       " ('semak', 4035),\n",
       " ('selusur', 4034),\n",
       " ('seluruh', 4033),\n",
       " ('seluncur', 4032),\n",
       " ('selopamioro', 4031),\n",
       " ('selfie', 4030),\n",
       " ('selesai', 4029),\n",
       " ('selera', 4028),\n",
       " ('selenggara', 4027),\n",
       " ('selendang', 4026),\n",
       " ('selebgram', 4025),\n",
       " ('selatan', 4024),\n",
       " ('selasar', 4023),\n",
       " ('selasa', 4022),\n",
       " ('selaras', 4021),\n",
       " ('selancar', 4020),\n",
       " ('selamat', 4019),\n",
       " ('selam', 4018),\n",
       " ('selalu', 4017),\n",
       " ('selaku', 4016),\n",
       " ('selain', 4015),\n",
       " ('sela', 4014),\n",
       " ('sekutu', 4013),\n",
       " ('sekunder', 4012),\n",
       " ('sekunar', 4011),\n",
       " ('sektor', 4010),\n",
       " ('seksi', 4009),\n",
       " ('sekretaris', 4008),\n",
       " ('sekolah', 4007),\n",
       " ('sekitar', 4006),\n",
       " ('sekira', 4005),\n",
       " ('sekian', 4004),\n",
       " ('sekertariat', 4003),\n",
       " ('sekeloa', 4002),\n",
       " ('sekarang', 4001),\n",
       " ('sekaligus', 4000),\n",
       " ('sekali', 3999),\n",
       " ('sekadar', 3998),\n",
       " ('sejuk', 3997),\n",
       " ('sejatine', 3996),\n",
       " ('sejati', 3995),\n",
       " ('sejarah', 3994),\n",
       " ('sejak', 3993),\n",
       " ('sejahtera', 3992),\n",
       " ('sehingga', 3991),\n",
       " ('sehat', 3990),\n",
       " ('segoro', 3989),\n",
       " ('sego', 3988),\n",
       " ('segitiga', 3987),\n",
       " ('segi', 3986),\n",
       " ('segera', 3985),\n",
       " ('segar', 3984),\n",
       " ('segala', 3983),\n",
       " ('seeng', 3982),\n",
       " ('sedikit', 3981),\n",
       " ('sedia', 3980),\n",
       " ('sederhana', 3979),\n",
       " ('sedekah', 3978),\n",
       " ('sedang', 3977),\n",
       " ('sedahan', 3976),\n",
       " ('secaara', 3975),\n",
       " ('sebut', 3974),\n",
       " ('seberang', 3973),\n",
       " ('sebar', 3972),\n",
       " ('sebab', 3971),\n",
       " ('seaworld', 3970),\n",
       " ('seaside', 3969),\n",
       " ('se', 3968),\n",
       " ('sculpture', 3967),\n",
       " ('school', 3966),\n",
       " ('schoemaker', 3965),\n",
       " ('schmidt', 3964),\n",
       " ('sayur', 3963),\n",
       " ('sayap', 3962),\n",
       " ...]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(tv.vocabulary_.items(),key=lambda x: x[1],reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "membuat cosine smiliarity untuk melihat kemiripan antar item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04510993, 0.07188361, 0.02807929, 0.00479488, 0.01497397,\n",
       "       0.02458836, 0.00394491, 0.01975822, 0.01633207])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity = cosine_similarity(vectors)\n",
    "similarity[0][1:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "membuat function untuk rekomendasi "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend_by_content_based_filtering(nama_tempat):\n",
    "    nama_tempat_index = data_content_based_filtering[data_content_based_filtering['Place_Name']==nama_tempat].index[0]\n",
    "    distancess = similarity[nama_tempat_index]\n",
    "    nama_tempat_list = sorted(list(enumerate(distancess)),key=lambda x: x[1],reverse=True)[1:10]\n",
    "    \n",
    "    recommended_nama_tempats = []\n",
    "    for i in nama_tempat_list:\n",
    "        recommended_nama_tempats.append([data_content_based_filtering.iloc[i[0]].Place_Name]+[i[1]])\n",
    "        # print(nama_tempats.iloc[i[0]].original_title)\n",
    "        \n",
    "    return recommended_nama_tempats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hasil 10 tempat yang serupa dengan Dunia Fantasi\t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Monumen Bandung Lautan Api', 0.28683907306335177],\n",
       " ['Monumen Selamat Datang', 0.2661577967639709],\n",
       " ['Monumen Perjuangan Rakyat Jawa Barat', 0.25729692362006246],\n",
       " ['Tugu Muda Semarang', 0.22491138935052768],\n",
       " ['Monumen Bambu Runcing Surabaya', 0.20482652874129093],\n",
       " ['Monumen Tugu Pahlawan', 0.18791275217429565],\n",
       " ['Monumen Sanapati', 0.1795335499410377],\n",
       " ['Monumen Yogya Kembali', 0.17871073001225599],\n",
       " ['Monumen Palagan Ambarawa', 0.16871094841229334]]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommend_by_content_based_filtering('Monumen Nasional')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modeling with Collaborative Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "melakukan import library yang digunakan untuk membuat model collaborative filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "from zipfile import ZipFile\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "membuat dataset baru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_Id</th>\n",
       "      <th>Place_Id</th>\n",
       "      <th>Place_Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>344</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>373</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>300</td>\n",
       "      <td>425</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>300</td>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>300</td>\n",
       "      <td>311</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>300</td>\n",
       "      <td>279</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>300</td>\n",
       "      <td>163</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User_Id  Place_Id  Place_Ratings\n",
       "0           1       179              3\n",
       "1           1       344              2\n",
       "2           1         5              5\n",
       "3           1       373              3\n",
       "4           1       101              4\n",
       "...       ...       ...            ...\n",
       "9995      300       425              2\n",
       "9996      300        64              4\n",
       "9997      300       311              3\n",
       "9998      300       279              4\n",
       "9999      300       163              2\n",
       "\n",
       "[10000 rows x 3 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_collaborative_filtering = data_tourism_rating.copy()\n",
    "data_collaborative_filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mengubah userID menjadi list dan membuat variabel untuk melakukan encode serta decode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "list userID:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300]\n",
      "encoded userID :  {1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 7, 9: 8, 10: 9, 11: 10, 12: 11, 13: 12, 14: 13, 15: 14, 16: 15, 17: 16, 18: 17, 19: 18, 20: 19, 21: 20, 22: 21, 23: 22, 24: 23, 25: 24, 26: 25, 27: 26, 28: 27, 29: 28, 30: 29, 31: 30, 32: 31, 33: 32, 34: 33, 35: 34, 36: 35, 37: 36, 38: 37, 39: 38, 40: 39, 41: 40, 42: 41, 43: 42, 44: 43, 45: 44, 46: 45, 47: 46, 48: 47, 49: 48, 50: 49, 51: 50, 52: 51, 53: 52, 54: 53, 55: 54, 56: 55, 57: 56, 58: 57, 59: 58, 60: 59, 61: 60, 62: 61, 63: 62, 64: 63, 65: 64, 66: 65, 67: 66, 68: 67, 69: 68, 70: 69, 71: 70, 72: 71, 73: 72, 74: 73, 75: 74, 76: 75, 77: 76, 78: 77, 79: 78, 80: 79, 81: 80, 82: 81, 83: 82, 84: 83, 85: 84, 86: 85, 87: 86, 88: 87, 89: 88, 90: 89, 91: 90, 92: 91, 93: 92, 94: 93, 95: 94, 96: 95, 97: 96, 98: 97, 99: 98, 100: 99, 101: 100, 102: 101, 103: 102, 104: 103, 105: 104, 106: 105, 107: 106, 108: 107, 109: 108, 110: 109, 111: 110, 112: 111, 113: 112, 114: 113, 115: 114, 116: 115, 117: 116, 118: 117, 119: 118, 120: 119, 121: 120, 122: 121, 123: 122, 124: 123, 125: 124, 126: 125, 127: 126, 128: 127, 129: 128, 130: 129, 131: 130, 132: 131, 133: 132, 134: 133, 135: 134, 136: 135, 137: 136, 138: 137, 139: 138, 140: 139, 141: 140, 142: 141, 143: 142, 144: 143, 145: 144, 146: 145, 147: 146, 148: 147, 149: 148, 150: 149, 151: 150, 152: 151, 153: 152, 154: 153, 155: 154, 156: 155, 157: 156, 158: 157, 159: 158, 160: 159, 161: 160, 162: 161, 163: 162, 164: 163, 165: 164, 166: 165, 167: 166, 168: 167, 169: 168, 170: 169, 171: 170, 172: 171, 173: 172, 174: 173, 175: 174, 176: 175, 177: 176, 178: 177, 179: 178, 180: 179, 181: 180, 182: 181, 183: 182, 184: 183, 185: 184, 186: 185, 187: 186, 188: 187, 189: 188, 190: 189, 191: 190, 192: 191, 193: 192, 194: 193, 195: 194, 196: 195, 197: 196, 198: 197, 199: 198, 200: 199, 201: 200, 202: 201, 203: 202, 204: 203, 205: 204, 206: 205, 207: 206, 208: 207, 209: 208, 210: 209, 211: 210, 212: 211, 213: 212, 214: 213, 215: 214, 216: 215, 217: 216, 218: 217, 219: 218, 220: 219, 221: 220, 222: 221, 223: 222, 224: 223, 225: 224, 226: 225, 227: 226, 228: 227, 229: 228, 230: 229, 231: 230, 232: 231, 233: 232, 234: 233, 235: 234, 236: 235, 237: 236, 238: 237, 239: 238, 240: 239, 241: 240, 242: 241, 243: 242, 244: 243, 245: 244, 246: 245, 247: 246, 248: 247, 249: 248, 250: 249, 251: 250, 252: 251, 253: 252, 254: 253, 255: 254, 256: 255, 257: 256, 258: 257, 259: 258, 260: 259, 261: 260, 262: 261, 263: 262, 264: 263, 265: 264, 266: 265, 267: 266, 268: 267, 269: 268, 270: 269, 271: 270, 272: 271, 273: 272, 274: 273, 275: 274, 276: 275, 277: 276, 278: 277, 279: 278, 280: 279, 281: 280, 282: 281, 283: 282, 284: 283, 285: 284, 286: 285, 287: 286, 288: 287, 289: 288, 290: 289, 291: 290, 292: 291, 293: 292, 294: 293, 295: 294, 296: 295, 297: 296, 298: 297, 299: 298, 300: 299}\n",
      "encoded angka ke userID:  {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 9, 9: 10, 10: 11, 11: 12, 12: 13, 13: 14, 14: 15, 15: 16, 16: 17, 17: 18, 18: 19, 19: 20, 20: 21, 21: 22, 22: 23, 23: 24, 24: 25, 25: 26, 26: 27, 27: 28, 28: 29, 29: 30, 30: 31, 31: 32, 32: 33, 33: 34, 34: 35, 35: 36, 36: 37, 37: 38, 38: 39, 39: 40, 40: 41, 41: 42, 42: 43, 43: 44, 44: 45, 45: 46, 46: 47, 47: 48, 48: 49, 49: 50, 50: 51, 51: 52, 52: 53, 53: 54, 54: 55, 55: 56, 56: 57, 57: 58, 58: 59, 59: 60, 60: 61, 61: 62, 62: 63, 63: 64, 64: 65, 65: 66, 66: 67, 67: 68, 68: 69, 69: 70, 70: 71, 71: 72, 72: 73, 73: 74, 74: 75, 75: 76, 76: 77, 77: 78, 78: 79, 79: 80, 80: 81, 81: 82, 82: 83, 83: 84, 84: 85, 85: 86, 86: 87, 87: 88, 88: 89, 89: 90, 90: 91, 91: 92, 92: 93, 93: 94, 94: 95, 95: 96, 96: 97, 97: 98, 98: 99, 99: 100, 100: 101, 101: 102, 102: 103, 103: 104, 104: 105, 105: 106, 106: 107, 107: 108, 108: 109, 109: 110, 110: 111, 111: 112, 112: 113, 113: 114, 114: 115, 115: 116, 116: 117, 117: 118, 118: 119, 119: 120, 120: 121, 121: 122, 122: 123, 123: 124, 124: 125, 125: 126, 126: 127, 127: 128, 128: 129, 129: 130, 130: 131, 131: 132, 132: 133, 133: 134, 134: 135, 135: 136, 136: 137, 137: 138, 138: 139, 139: 140, 140: 141, 141: 142, 142: 143, 143: 144, 144: 145, 145: 146, 146: 147, 147: 148, 148: 149, 149: 150, 150: 151, 151: 152, 152: 153, 153: 154, 154: 155, 155: 156, 156: 157, 157: 158, 158: 159, 159: 160, 160: 161, 161: 162, 162: 163, 163: 164, 164: 165, 165: 166, 166: 167, 167: 168, 168: 169, 169: 170, 170: 171, 171: 172, 172: 173, 173: 174, 174: 175, 175: 176, 176: 177, 177: 178, 178: 179, 179: 180, 180: 181, 181: 182, 182: 183, 183: 184, 184: 185, 185: 186, 186: 187, 187: 188, 188: 189, 189: 190, 190: 191, 191: 192, 192: 193, 193: 194, 194: 195, 195: 196, 196: 197, 197: 198, 198: 199, 199: 200, 200: 201, 201: 202, 202: 203, 203: 204, 204: 205, 205: 206, 206: 207, 207: 208, 208: 209, 209: 210, 210: 211, 211: 212, 212: 213, 213: 214, 214: 215, 215: 216, 216: 217, 217: 218, 218: 219, 219: 220, 220: 221, 221: 222, 222: 223, 223: 224, 224: 225, 225: 226, 226: 227, 227: 228, 228: 229, 229: 230, 230: 231, 231: 232, 232: 233, 233: 234, 234: 235, 235: 236, 236: 237, 237: 238, 238: 239, 239: 240, 240: 241, 241: 242, 242: 243, 243: 244, 244: 245, 245: 246, 246: 247, 247: 248, 248: 249, 249: 250, 250: 251, 251: 252, 252: 253, 253: 254, 254: 255, 255: 256, 256: 257, 257: 258, 258: 259, 259: 260, 260: 261, 261: 262, 262: 263, 263: 264, 264: 265, 265: 266, 266: 267, 267: 268, 268: 269, 269: 270, 270: 271, 271: 272, 272: 273, 273: 274, 274: 275, 275: 276, 276: 277, 277: 278, 278: 279, 279: 280, 280: 281, 281: 282, 282: 283, 283: 284, 284: 285, 285: 286, 286: 287, 287: 288, 288: 289, 289: 290, 290: 291, 291: 292, 292: 293, 293: 294, 294: 295, 295: 296, 296: 297, 297: 298, 298: 299, 299: 300}\n"
     ]
    }
   ],
   "source": [
    "# Mengubah userID menjadi list tanpa nilai yang sama\n",
    "user_ids = data_collaborative_filtering['User_Id'].unique().tolist()\n",
    "print('list userID: ', user_ids)\n",
    " \n",
    "# Melakukan encoding userID\n",
    "user_to_user_encoded = {x: i for i, x in enumerate(user_ids)}\n",
    "print('encoded userID : ', user_to_user_encoded)\n",
    " \n",
    "# Melakukan proses encoding angka ke ke userID\n",
    "user_encoded_to_user = {i: x for i, x in enumerate(user_ids)}\n",
    "print('encoded angka ke userID: ', user_encoded_to_user)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mengubah Place_Id menjadi list dan membuat variabel untuk melakukan encode serta decode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mengubah placeID menjadi list tanpa nilai yang sama\n",
    "resto_ids = data_collaborative_filtering['Place_Id'].unique().tolist()\n",
    " \n",
    "# Melakukan proses encoding placeID\n",
    "resto_to_resto_encoded = {x: i for i, x in enumerate(resto_ids)}\n",
    " \n",
    "# Melakukan proses encoding angka ke placeID\n",
    "resto_encoded_to_resto = {i: x for i, x in enumerate(resto_ids)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "melakukan mapping terhadap variabel baru, yaitu: user dan resto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping userID ke dataframe user\n",
    "data_collaborative_filtering['user'] = data_collaborative_filtering['User_Id'].map(user_to_user_encoded)\n",
    " \n",
    "# Mapping placeID ke dataframe resto\n",
    "data_collaborative_filtering['resto'] = data_collaborative_filtering['Place_Id'].map(resto_to_resto_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_Id</th>\n",
       "      <th>Place_Id</th>\n",
       "      <th>Place_Ratings</th>\n",
       "      <th>user</th>\n",
       "      <th>resto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>344</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>373</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>300</td>\n",
       "      <td>425</td>\n",
       "      <td>2</td>\n",
       "      <td>299</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>300</td>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
       "      <td>299</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>300</td>\n",
       "      <td>311</td>\n",
       "      <td>3</td>\n",
       "      <td>299</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>300</td>\n",
       "      <td>279</td>\n",
       "      <td>4</td>\n",
       "      <td>299</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>300</td>\n",
       "      <td>163</td>\n",
       "      <td>2</td>\n",
       "      <td>299</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User_Id  Place_Id  Place_Ratings  user  resto\n",
       "0           1       179              3     0      0\n",
       "1           1       344              2     0      1\n",
       "2           1         5              5     0      2\n",
       "3           1       373              3     0      3\n",
       "4           1       101              4     0      4\n",
       "...       ...       ...            ...   ...    ...\n",
       "9995      300       425              2   299    324\n",
       "9996      300        64              4   299    132\n",
       "9997      300       311              3   299    348\n",
       "9998      300       279              4   299    290\n",
       "9999      300       163              2   299    243\n",
       "\n",
       "[10000 rows x 5 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_collaborative_filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mendapatkan banyaknya user, resto, serta nilai rating terkecil dan terbesar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300\n",
      "437\n",
      "Number of User: 300, Number of Resto: 437, Min Rating: 1.0, Max Rating: 5.0\n"
     ]
    }
   ],
   "source": [
    "# Mendapatkan jumlah user\n",
    "num_users = len(user_to_user_encoded)\n",
    "print(num_users)\n",
    " \n",
    "# Mendapatkan jumlah resto\n",
    "num_resto = len(resto_encoded_to_resto)\n",
    "print(num_resto)\n",
    " \n",
    "# Mengubah rating menjadi nilai float\n",
    "data_collaborative_filtering['Place_Ratings'] = data_collaborative_filtering['Place_Ratings'].values.astype(np.float32)\n",
    " \n",
    "# Nilai minimum rating\n",
    "min_rating = min(data_collaborative_filtering['Place_Ratings'])\n",
    " \n",
    "# Nilai maksimal rating\n",
    "max_rating = max(data_collaborative_filtering['Place_Ratings'])\n",
    " \n",
    "print('Number of User: {}, Number of Resto: {}, Min Rating: {}, Max Rating: {}'.format(\n",
    "    num_users, num_resto, min_rating, max_rating\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mengacak dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_Id</th>\n",
       "      <th>Place_Id</th>\n",
       "      <th>Place_Ratings</th>\n",
       "      <th>user</th>\n",
       "      <th>resto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6252</th>\n",
       "      <td>188</td>\n",
       "      <td>207</td>\n",
       "      <td>3.0</td>\n",
       "      <td>187</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4684</th>\n",
       "      <td>142</td>\n",
       "      <td>268</td>\n",
       "      <td>2.0</td>\n",
       "      <td>141</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>54</td>\n",
       "      <td>103</td>\n",
       "      <td>3.0</td>\n",
       "      <td>53</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4742</th>\n",
       "      <td>144</td>\n",
       "      <td>119</td>\n",
       "      <td>3.0</td>\n",
       "      <td>143</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4521</th>\n",
       "      <td>138</td>\n",
       "      <td>288</td>\n",
       "      <td>5.0</td>\n",
       "      <td>137</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5734</th>\n",
       "      <td>173</td>\n",
       "      <td>402</td>\n",
       "      <td>5.0</td>\n",
       "      <td>172</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5191</th>\n",
       "      <td>157</td>\n",
       "      <td>85</td>\n",
       "      <td>4.0</td>\n",
       "      <td>156</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>163</td>\n",
       "      <td>18</td>\n",
       "      <td>3.0</td>\n",
       "      <td>162</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>28</td>\n",
       "      <td>416</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>219</td>\n",
       "      <td>258</td>\n",
       "      <td>2.0</td>\n",
       "      <td>218</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User_Id  Place_Id  Place_Ratings  user  resto\n",
       "6252      188       207            3.0   187    262\n",
       "4684      142       268            2.0   141     83\n",
       "1731       54       103            3.0    53     10\n",
       "4742      144       119            3.0   143    141\n",
       "4521      138       288            5.0   137    283\n",
       "...       ...       ...            ...   ...    ...\n",
       "5734      173       402            5.0   172    175\n",
       "5191      157        85            4.0   156     33\n",
       "5390      163        18            3.0   162     52\n",
       "860        28       416            4.0    27    187\n",
       "7270      219       258            2.0   218      6\n",
       "\n",
       "[10000 rows x 5 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_collaborative_filtering = data_collaborative_filtering.sample(frac=1, random_state=42)\n",
    "data_collaborative_filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "membagi dataset menjadi data latih dan data train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[187 262]\n",
      " [141  83]\n",
      " [ 53  10]\n",
      " ...\n",
      " [162  52]\n",
      " [ 27 187]\n",
      " [218   6]] [0.5  0.25 0.5  ... 0.5  0.75 0.25]\n"
     ]
    }
   ],
   "source": [
    "x = data_collaborative_filtering[['user', 'resto']].values\n",
    " \n",
    "# Membuat variabel y untuk membuat rating dari hasil \n",
    "y = data_collaborative_filtering['Place_Ratings'].apply(lambda x: (x - min_rating) / (max_rating - min_rating)).values\n",
    " \n",
    "# Membagi menjadi 80% data train dan 20% data validasi\n",
    "train_indices = int(0.8 * data_collaborative_filtering.shape[0])\n",
    "x_train, x_val, y_train, y_val = (\n",
    "    x[:train_indices],\n",
    "    x[train_indices:],\n",
    "    y[:train_indices],\n",
    "    y[train_indices:]\n",
    ")\n",
    " \n",
    "print(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "membuat class rekomendasi dengan menggunaakn keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RecommenderNet(tf.keras.Model):\n",
    " \n",
    "  # Insialisasi fungsi\n",
    "  def __init__(self, num_users, num_resto, embedding_size, **kwargs):\n",
    "    super(RecommenderNet, self).__init__(**kwargs)\n",
    "    self.num_users = num_users\n",
    "    self.num_resto = num_resto\n",
    "    self.embedding_size = embedding_size\n",
    "    self.user_embedding = layers.Embedding( # layer embedding user\n",
    "        num_users,\n",
    "        embedding_size,\n",
    "        embeddings_initializer = 'he_normal',\n",
    "        embeddings_regularizer = keras.regularizers.l2(1e-6)\n",
    "    )\n",
    "    self.user_bias = layers.Embedding(num_users, 1) # layer embedding user bias\n",
    "    self.resto_embedding = layers.Embedding( # layer embeddings resto\n",
    "        num_resto,\n",
    "        embedding_size,\n",
    "        embeddings_initializer = 'he_normal',\n",
    "        embeddings_regularizer = keras.regularizers.l2(1e-6)\n",
    "    )\n",
    "    self.resto_bias = layers.Embedding(num_resto, 1) # layer embedding resto bias\n",
    " \n",
    "  def call(self, inputs):\n",
    "    user_vector = self.user_embedding(inputs[:,0]) # memanggil layer embedding 1\n",
    "    user_bias = self.user_bias(inputs[:, 0]) # memanggil layer embedding 2\n",
    "    resto_vector = self.resto_embedding(inputs[:, 1]) # memanggil layer embedding 3\n",
    "    resto_bias = self.resto_bias(inputs[:, 1]) # memanggil layer embedding 4\n",
    " \n",
    "    dot_user_resto = tf.tensordot(user_vector, resto_vector, 2) \n",
    " \n",
    "    x = dot_user_resto + user_bias + resto_bias\n",
    "    \n",
    "    return tf.nn.sigmoid(x) # activation sigmoid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "melakukan inisiasi model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RecommenderNet(num_users, num_resto, 50) # inisialisasi model\n",
    " \n",
    "# model compile\n",
    "model.compile(\n",
    "    loss = tf.keras.losses.BinaryCrossentropy(),\n",
    "    optimizer = keras.optimizers.Adam(learning_rate=0.001),\n",
    "    metrics=[tf.keras.metrics.RootMeanSquaredError()]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "melakukan training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.6943 - root_mean_squared_error: 0.3462 - val_loss: 0.6941 - val_root_mean_squared_error: 0.3457\n",
      "Epoch 2/100\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.6871 - root_mean_squared_error: 0.3409 - val_loss: 0.6939 - val_root_mean_squared_error: 0.3455\n",
      "Epoch 3/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6809 - root_mean_squared_error: 0.3364 - val_loss: 0.6940 - val_root_mean_squared_error: 0.3455\n",
      "Epoch 4/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6782 - root_mean_squared_error: 0.3344 - val_loss: 0.6956 - val_root_mean_squared_error: 0.3466\n",
      "Epoch 5/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6733 - root_mean_squared_error: 0.3308 - val_loss: 0.6959 - val_root_mean_squared_error: 0.3468\n",
      "Epoch 6/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6706 - root_mean_squared_error: 0.3288 - val_loss: 0.6973 - val_root_mean_squared_error: 0.3478\n",
      "Epoch 7/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6664 - root_mean_squared_error: 0.3257 - val_loss: 0.6985 - val_root_mean_squared_error: 0.3486\n",
      "Epoch 8/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6633 - root_mean_squared_error: 0.3234 - val_loss: 0.7000 - val_root_mean_squared_error: 0.3496\n",
      "Epoch 9/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6600 - root_mean_squared_error: 0.3210 - val_loss: 0.7020 - val_root_mean_squared_error: 0.3509\n",
      "Epoch 10/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6590 - root_mean_squared_error: 0.3203 - val_loss: 0.7026 - val_root_mean_squared_error: 0.3512\n",
      "Epoch 11/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6571 - root_mean_squared_error: 0.3189 - val_loss: 0.7028 - val_root_mean_squared_error: 0.3512\n",
      "Epoch 12/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6557 - root_mean_squared_error: 0.3179 - val_loss: 0.7035 - val_root_mean_squared_error: 0.3517\n",
      "Epoch 13/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6574 - root_mean_squared_error: 0.3191 - val_loss: 0.7045 - val_root_mean_squared_error: 0.3523\n",
      "Epoch 14/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6550 - root_mean_squared_error: 0.3173 - val_loss: 0.7041 - val_root_mean_squared_error: 0.3520\n",
      "Epoch 15/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6529 - root_mean_squared_error: 0.3159 - val_loss: 0.7061 - val_root_mean_squared_error: 0.3534\n",
      "Epoch 16/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6546 - root_mean_squared_error: 0.3170 - val_loss: 0.7073 - val_root_mean_squared_error: 0.3540\n",
      "Epoch 17/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6522 - root_mean_squared_error: 0.3151 - val_loss: 0.7083 - val_root_mean_squared_error: 0.3547\n",
      "Epoch 18/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6518 - root_mean_squared_error: 0.3149 - val_loss: 0.7097 - val_root_mean_squared_error: 0.3556\n",
      "Epoch 19/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6524 - root_mean_squared_error: 0.3154 - val_loss: 0.7099 - val_root_mean_squared_error: 0.3557\n",
      "Epoch 20/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6532 - root_mean_squared_error: 0.3161 - val_loss: 0.7090 - val_root_mean_squared_error: 0.3550\n",
      "Epoch 21/100\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.6510 - root_mean_squared_error: 0.3143 - val_loss: 0.7099 - val_root_mean_squared_error: 0.3556\n",
      "Epoch 22/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6524 - root_mean_squared_error: 0.3154 - val_loss: 0.7101 - val_root_mean_squared_error: 0.3557\n",
      "Epoch 23/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6514 - root_mean_squared_error: 0.3146 - val_loss: 0.7118 - val_root_mean_squared_error: 0.3567\n",
      "Epoch 24/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6484 - root_mean_squared_error: 0.3125 - val_loss: 0.7113 - val_root_mean_squared_error: 0.3564\n",
      "Epoch 25/100\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.6515 - root_mean_squared_error: 0.3145 - val_loss: 0.7122 - val_root_mean_squared_error: 0.3570\n",
      "Epoch 26/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6517 - root_mean_squared_error: 0.3148 - val_loss: 0.7134 - val_root_mean_squared_error: 0.3577\n",
      "Epoch 27/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6516 - root_mean_squared_error: 0.3146 - val_loss: 0.7146 - val_root_mean_squared_error: 0.3585\n",
      "Epoch 28/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6516 - root_mean_squared_error: 0.3148 - val_loss: 0.7149 - val_root_mean_squared_error: 0.3587\n",
      "Epoch 29/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6510 - root_mean_squared_error: 0.3145 - val_loss: 0.7162 - val_root_mean_squared_error: 0.3595\n",
      "Epoch 30/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6498 - root_mean_squared_error: 0.3132 - val_loss: 0.7166 - val_root_mean_squared_error: 0.3598\n",
      "Epoch 31/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6520 - root_mean_squared_error: 0.3150 - val_loss: 0.7145 - val_root_mean_squared_error: 0.3584\n",
      "Epoch 32/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6523 - root_mean_squared_error: 0.3153 - val_loss: 0.7148 - val_root_mean_squared_error: 0.3585\n",
      "Epoch 33/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6514 - root_mean_squared_error: 0.3148 - val_loss: 0.7140 - val_root_mean_squared_error: 0.3579\n",
      "Epoch 34/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6517 - root_mean_squared_error: 0.3148 - val_loss: 0.7145 - val_root_mean_squared_error: 0.3582\n",
      "Epoch 35/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6501 - root_mean_squared_error: 0.3137 - val_loss: 0.7136 - val_root_mean_squared_error: 0.3576\n",
      "Epoch 36/100\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.6474 - root_mean_squared_error: 0.3117 - val_loss: 0.7151 - val_root_mean_squared_error: 0.3585\n",
      "Epoch 37/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6501 - root_mean_squared_error: 0.3136 - val_loss: 0.7158 - val_root_mean_squared_error: 0.3590\n",
      "Epoch 38/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6507 - root_mean_squared_error: 0.3142 - val_loss: 0.7174 - val_root_mean_squared_error: 0.3600\n",
      "Epoch 39/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6507 - root_mean_squared_error: 0.3141 - val_loss: 0.7185 - val_root_mean_squared_error: 0.3607\n",
      "Epoch 40/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6489 - root_mean_squared_error: 0.3128 - val_loss: 0.7183 - val_root_mean_squared_error: 0.3606\n",
      "Epoch 41/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6516 - root_mean_squared_error: 0.3148 - val_loss: 0.7173 - val_root_mean_squared_error: 0.3600\n",
      "Epoch 42/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6486 - root_mean_squared_error: 0.3127 - val_loss: 0.7157 - val_root_mean_squared_error: 0.3589\n",
      "Epoch 43/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6492 - root_mean_squared_error: 0.3130 - val_loss: 0.7151 - val_root_mean_squared_error: 0.3585\n",
      "Epoch 44/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6497 - root_mean_squared_error: 0.3134 - val_loss: 0.7152 - val_root_mean_squared_error: 0.3585\n",
      "Epoch 45/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6495 - root_mean_squared_error: 0.3133 - val_loss: 0.7167 - val_root_mean_squared_error: 0.3594\n",
      "Epoch 46/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6510 - root_mean_squared_error: 0.3144 - val_loss: 0.7172 - val_root_mean_squared_error: 0.3596\n",
      "Epoch 47/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6491 - root_mean_squared_error: 0.3128 - val_loss: 0.7175 - val_root_mean_squared_error: 0.3599\n",
      "Epoch 48/100\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.6502 - root_mean_squared_error: 0.3138 - val_loss: 0.7168 - val_root_mean_squared_error: 0.3593\n",
      "Epoch 49/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6517 - root_mean_squared_error: 0.3148 - val_loss: 0.7164 - val_root_mean_squared_error: 0.3591\n",
      "Epoch 50/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6489 - root_mean_squared_error: 0.3129 - val_loss: 0.7167 - val_root_mean_squared_error: 0.3594\n",
      "Epoch 51/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6499 - root_mean_squared_error: 0.3136 - val_loss: 0.7167 - val_root_mean_squared_error: 0.3593\n",
      "Epoch 52/100\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.6495 - root_mean_squared_error: 0.3133 - val_loss: 0.7170 - val_root_mean_squared_error: 0.3596\n",
      "Epoch 53/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6523 - root_mean_squared_error: 0.3152 - val_loss: 0.7172 - val_root_mean_squared_error: 0.3597\n",
      "Epoch 54/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6509 - root_mean_squared_error: 0.3141 - val_loss: 0.7171 - val_root_mean_squared_error: 0.3597\n",
      "Epoch 55/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6503 - root_mean_squared_error: 0.3138 - val_loss: 0.7173 - val_root_mean_squared_error: 0.3598\n",
      "Epoch 56/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6500 - root_mean_squared_error: 0.3137 - val_loss: 0.7170 - val_root_mean_squared_error: 0.3595\n",
      "Epoch 57/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6489 - root_mean_squared_error: 0.3129 - val_loss: 0.7186 - val_root_mean_squared_error: 0.3604\n",
      "Epoch 58/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6499 - root_mean_squared_error: 0.3135 - val_loss: 0.7192 - val_root_mean_squared_error: 0.3608\n",
      "Epoch 59/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6491 - root_mean_squared_error: 0.3130 - val_loss: 0.7190 - val_root_mean_squared_error: 0.3606\n",
      "Epoch 60/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6498 - root_mean_squared_error: 0.3134 - val_loss: 0.7190 - val_root_mean_squared_error: 0.3606\n",
      "Epoch 61/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6482 - root_mean_squared_error: 0.3120 - val_loss: 0.7186 - val_root_mean_squared_error: 0.3603\n",
      "Epoch 62/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6512 - root_mean_squared_error: 0.3142 - val_loss: 0.7193 - val_root_mean_squared_error: 0.3609\n",
      "Epoch 63/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6511 - root_mean_squared_error: 0.3144 - val_loss: 0.7212 - val_root_mean_squared_error: 0.3621\n",
      "Epoch 64/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6474 - root_mean_squared_error: 0.3117 - val_loss: 0.7200 - val_root_mean_squared_error: 0.3613\n",
      "Epoch 65/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6510 - root_mean_squared_error: 0.3143 - val_loss: 0.7210 - val_root_mean_squared_error: 0.3619\n",
      "Epoch 66/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6507 - root_mean_squared_error: 0.3141 - val_loss: 0.7224 - val_root_mean_squared_error: 0.3628\n",
      "Epoch 67/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6500 - root_mean_squared_error: 0.3135 - val_loss: 0.7213 - val_root_mean_squared_error: 0.3621\n",
      "Epoch 68/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6490 - root_mean_squared_error: 0.3129 - val_loss: 0.7213 - val_root_mean_squared_error: 0.3621\n",
      "Epoch 69/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6502 - root_mean_squared_error: 0.3137 - val_loss: 0.7202 - val_root_mean_squared_error: 0.3614\n",
      "Epoch 70/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6488 - root_mean_squared_error: 0.3126 - val_loss: 0.7208 - val_root_mean_squared_error: 0.3617\n",
      "Epoch 71/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6495 - root_mean_squared_error: 0.3132 - val_loss: 0.7223 - val_root_mean_squared_error: 0.3626\n",
      "Epoch 72/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6489 - root_mean_squared_error: 0.3129 - val_loss: 0.7206 - val_root_mean_squared_error: 0.3615\n",
      "Epoch 73/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6516 - root_mean_squared_error: 0.3147 - val_loss: 0.7218 - val_root_mean_squared_error: 0.3623\n",
      "Epoch 74/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6483 - root_mean_squared_error: 0.3124 - val_loss: 0.7220 - val_root_mean_squared_error: 0.3626\n",
      "Epoch 75/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6476 - root_mean_squared_error: 0.3119 - val_loss: 0.7211 - val_root_mean_squared_error: 0.3621\n",
      "Epoch 76/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6499 - root_mean_squared_error: 0.3135 - val_loss: 0.7194 - val_root_mean_squared_error: 0.3610\n",
      "Epoch 77/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6488 - root_mean_squared_error: 0.3126 - val_loss: 0.7195 - val_root_mean_squared_error: 0.3611\n",
      "Epoch 78/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6498 - root_mean_squared_error: 0.3133 - val_loss: 0.7199 - val_root_mean_squared_error: 0.3612\n",
      "Epoch 79/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6505 - root_mean_squared_error: 0.3140 - val_loss: 0.7200 - val_root_mean_squared_error: 0.3613\n",
      "Epoch 80/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6494 - root_mean_squared_error: 0.3132 - val_loss: 0.7173 - val_root_mean_squared_error: 0.3595\n",
      "Epoch 81/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6494 - root_mean_squared_error: 0.3132 - val_loss: 0.7181 - val_root_mean_squared_error: 0.3600\n",
      "Epoch 82/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6484 - root_mean_squared_error: 0.3124 - val_loss: 0.7190 - val_root_mean_squared_error: 0.3605\n",
      "Epoch 83/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6494 - root_mean_squared_error: 0.3131 - val_loss: 0.7189 - val_root_mean_squared_error: 0.3604\n",
      "Epoch 84/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6492 - root_mean_squared_error: 0.3131 - val_loss: 0.7187 - val_root_mean_squared_error: 0.3604\n",
      "Epoch 85/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6503 - root_mean_squared_error: 0.3138 - val_loss: 0.7183 - val_root_mean_squared_error: 0.3601\n",
      "Epoch 86/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6469 - root_mean_squared_error: 0.3112 - val_loss: 0.7174 - val_root_mean_squared_error: 0.3595\n",
      "Epoch 87/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6497 - root_mean_squared_error: 0.3132 - val_loss: 0.7169 - val_root_mean_squared_error: 0.3594\n",
      "Epoch 88/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6512 - root_mean_squared_error: 0.3144 - val_loss: 0.7170 - val_root_mean_squared_error: 0.3594\n",
      "Epoch 89/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6508 - root_mean_squared_error: 0.3142 - val_loss: 0.7175 - val_root_mean_squared_error: 0.3598\n",
      "Epoch 90/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6493 - root_mean_squared_error: 0.3131 - val_loss: 0.7176 - val_root_mean_squared_error: 0.3598\n",
      "Epoch 91/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6483 - root_mean_squared_error: 0.3122 - val_loss: 0.7186 - val_root_mean_squared_error: 0.3605\n",
      "Epoch 92/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6490 - root_mean_squared_error: 0.3126 - val_loss: 0.7185 - val_root_mean_squared_error: 0.3604\n",
      "Epoch 93/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6492 - root_mean_squared_error: 0.3131 - val_loss: 0.7199 - val_root_mean_squared_error: 0.3612\n",
      "Epoch 94/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6493 - root_mean_squared_error: 0.3130 - val_loss: 0.7191 - val_root_mean_squared_error: 0.3607\n",
      "Epoch 95/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6465 - root_mean_squared_error: 0.3109 - val_loss: 0.7199 - val_root_mean_squared_error: 0.3612\n",
      "Epoch 96/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6497 - root_mean_squared_error: 0.3135 - val_loss: 0.7194 - val_root_mean_squared_error: 0.3609\n",
      "Epoch 97/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6490 - root_mean_squared_error: 0.3128 - val_loss: 0.7196 - val_root_mean_squared_error: 0.3610\n",
      "Epoch 98/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6503 - root_mean_squared_error: 0.3138 - val_loss: 0.7205 - val_root_mean_squared_error: 0.3616\n",
      "Epoch 99/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6480 - root_mean_squared_error: 0.3121 - val_loss: 0.7180 - val_root_mean_squared_error: 0.3600\n",
      "Epoch 100/100\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.6493 - root_mean_squared_error: 0.3132 - val_loss: 0.7187 - val_root_mean_squared_error: 0.3604\n"
     ]
    }
   ],
   "source": [
    "# Memulai training\n",
    " \n",
    "history = model.fit(\n",
    "    x = x_train,\n",
    "    y = y_train,\n",
    "    batch_size = 8,\n",
    "    epochs = 100,\n",
    "    validation_data = (x_val, y_val)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mengambil user sample untuk melihat hasil rekomendasi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "resto_df = data_tourism_with_id\n",
    "df = data_tourism_rating\n",
    " \n",
    "# Mengambil sample user\n",
    "user_id = df.User_Id.sample(1).iloc[0]\n",
    "resto_visited_by_user = df[df.User_Id == user_id]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "membuat inisiasi terhadap tempat yang belum dikunjungi oleh user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "resto_not_visited = resto_df[~resto_df['Place_Id'].isin(resto_visited_by_user.Place_Id.values)]['Place_Id']\n",
    "resto_not_visited = list(\n",
    "    set(resto_not_visited)\n",
    "    .intersection(set(resto_to_resto_encoded.keys()))\n",
    ")\n",
    " \n",
    "resto_not_visited = [[resto_to_resto_encoded.get(x)] for x in resto_not_visited]\n",
    "user_encoder = user_to_user_encoded.get(user_id)\n",
    "user_resto_array = np.hstack(\n",
    "    ([[user_encoder]] * len(resto_not_visited), resto_not_visited)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "melakukan predict terhadap resto yang belum dikunju oleh user "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = model.predict(user_resto_array).flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10 rating tertinggi dari hasil prediksi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_ratings_indices = ratings.argsort()[-10:][::-1]\n",
    "recommended_resto_ids = [\n",
    "    resto_encoded_to_resto.get(resto_not_visited[x][0]) for x in top_ratings_indices\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "hasil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing recommendations for users: 107\n",
      "===========================\n",
      "Resto with high ratings from user\n",
      "--------------------------------\n",
      "Seribu Batu Songgo Langit\n",
      "Pantai Depok Jogja\n",
      "Gunung Papandayan\n",
      "Taman Lalu Lintas Ade Irma Suryani Nasution\n",
      "Museum Mandala Wangsit Siliwangi\n",
      "--------------------------------\n",
      "Top 10 resto recommendation\n",
      "--------------------------------\n",
      "Monumen Nasional\n",
      "Atlantis Water Adventure\n",
      "Museum Bank Indonesia\n",
      "Wisata Agro Edukatif Istana Susu Cibugary\n",
      "The World Landmarks - Merapi Park Yogyakarta\n",
      "Air Terjun Kedung Pedut\n",
      "Pantai Baron\n",
      "Pintoe Langit Dahromo\n",
      "Tafso Barn\n",
      "Keraton Surabaya\n"
     ]
    }
   ],
   "source": [
    "print('Showing recommendations for users: {}'.format(user_id))\n",
    "print('===' * 9)\n",
    "print('Resto with high ratings from user')\n",
    "print('----' * 8)\n",
    "\n",
    "top_resto_user = (\n",
    "    resto_visited_by_user.sort_values(\n",
    "        by = 'Place_Ratings',\n",
    "        ascending=False\n",
    "    )\n",
    "    .head(5)\n",
    "    .Place_Id.values\n",
    ")\n",
    "\n",
    "resto_df_rows = resto_df[resto_df['Place_Id'].isin(top_resto_user)]\n",
    "for row in resto_df_rows.itertuples():\n",
    "    print(row.Place_Name)\n",
    " \n",
    "print('----' * 8)\n",
    "print('Top 10 resto recommendation')\n",
    "print('----' * 8)\n",
    "\n",
    "recommended_resto = resto_df[resto_df['Place_Id'].isin(recommended_resto_ids)]\n",
    "for row in recommended_resto.itertuples():\n",
    "    print(row.Place_Name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluasi Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['root_mean_squared_error'])\n",
    "plt.plot(history.history['val_root_mean_squared_error'])\n",
    "plt.title('model_metrics')\n",
    "plt.ylabel('root_mean_squared_error')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "hasil evaluasi metric dengan menggunakan root mean squared error pada collaborative filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Pantai Kukup', 0.5659440531411729],\n",
       " ['Pantai Timang', 0.5185433503874808],\n",
       " ['Pantai Drini', 0.5034916444153296],\n",
       " ['Pantai Ngandong', 0.48939744234953786],\n",
       " ['Pantai Sundak', 0.4855410205632307],\n",
       " ['Pantai Ngrawe (Mesra)', 0.4655150705971857],\n",
       " ['Pantai Sadranan', 0.44906283441167205],\n",
       " ['Pantai Jungwok', 0.42546650148837023],\n",
       " ['Pantai Cipta', 0.4101023347137378]]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommend_by_content_based_filtering('Pantai Watu Kodok')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hasil evaluasi metric dengan menggunakan cosine similiarity"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "ab4cfb695e4995928d8d48b937410e7989a45ba361d6003efd8e9b7b9498f677"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}