2041720074_Mochammad Hairullah_15.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"2UwfHn-cFfCi"},"source":["# UTS Machine Learning\n","## IDENTITAS\n","NAMA : MOCHAMMAD HAIRULLAH <br>\n","NIM : 2041720074 <br>\n","KELAS : TI-2H <br>\n","\n","## PENYELESAIAN TERBAIK : NAIVE BAYES (KLASIFIKASI)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6WdL_peVOn6q"},"outputs":[],"source":["# Pembagian soal, berdasarkan file PDF Pembagian Soal TI-3H yang telah dibagikan.\n","# Perhatikan dataset yang diberikan: \n","# a) dataset_1 adalah data koordinat trayektori berjalan manusia; \n","# b) dataset_2 adalah purchase barang; \n","# c) dataset_3 adalah tingkat konsumsi bahan bakar; \n","# d) dataset_4 adalah data kondisi lingkungan.\n","\n","# Pelajari data sesuai dengan pembagian, perhatika  kaitan antar variabel!\n","# Tentukan teknik penyelesaian terbaik, apakah regresi atau klasifikasi!\n","# Tentukan metode terbaik sesuai dengan teknik penyelesaian yang telah ditentukan di poin 4!\n","# Kerjakan penyelesaian kasus dataset pada Google Collab serta berikan visualisasi yang sesuai!\n","# Buat sebuah video presentasi hasil pengerjaan dengan durasi minimal 10 menit! Unggah video tersebut di YouTube dengan mode Public atau Unlisted\n","# Cantumkan link Google Collab dan Video di kolom Penugasan ini\n","\n","# Probabilitas\n","# 3 kategori = BAIK, SEDANG, TIDAK SEHAT\n","# 5 stasiun  = DKI1 - DKI5"]},{"cell_type":"code","execution_count":154,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2935,"status":"ok","timestamp":1666601098687,"user":{"displayName":"MOCHAMMAD HAIRULLAH","userId":"06941804815695378308"},"user_tz":-420},"id":"vKw5HuuMO8wa","outputId":"4c297e7a-8407-49d2-ee6d-d371fc542cc4"},"outputs":[{"name":"stdout","output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":155,"metadata":{"executionInfo":{"elapsed":300,"status":"ok","timestamp":1666601103573,"user":{"displayName":"MOCHAMMAD HAIRULLAH","userId":"06941804815695378308"},"user_tz":-420},"id":"TzYBSYc9R-zY"},"outputs":[],"source":["import numpy as np\n","import pandas as pd"]},{"cell_type":"code","execution_count":164,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":307,"status":"ok","timestamp":1666601316626,"user":{"displayName":"MOCHAMMAD HAIRULLAH","userId":"06941804815695378308"},"user_tz":-420},"id":"cB6rSJPFR8jE","outputId":"71b4d02e-0e05-4e9e-c623-63934d9446e0"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-ae09b6b1-b226-430f-ac36-2477d2aea288\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th 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<td>22</td>\n","      <td>12</td>\n","      <td>29</td>\n","      <td>8</td>\n","      <td>29</td>\n","      <td>O3</td>\n","      <td>BAIK</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2121-11-13 00:00:00</td>\n","      <td>DKI1 (Bunderan HI)</td>\n","      <td>39</td>\n","      <td>22</td>\n","      <td>14</td>\n","      <td>32</td>\n","      <td>11</td>\n","      <td>39</td>\n","      <td>PM11</td>\n","      <td>BAIK</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2121-11-14 00:00:00</td>\n","      <td>DKI1 (Bunderan HI)</td>\n","      <td>34</td>\n","      <td>22</td>\n","      <td>14</td>\n","      <td>38</td>\n","      <td>11</td>\n","      <td>38</td>\n","      <td>O3</td>\n","      <td>BAIK</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2121-11-15 00:00:00</td>\n","      <td>DKI1 (Bunderan HI)</td>\n","      <td>35</td>\n","      <td>22</td>\n","      <td>12</td>\n","      <td>31</td>\n","      <td>9</td>\n","      <td>35</td>\n","      <td>PM11</td>\n","      <td>BAIK</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae09b6b1-b226-430f-ac36-2477d2aea288')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 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  \n","4  2121-11-15 00:00:00  DKI1 (Bunderan HI)    35   22  12  31    9   35   \n","\n","  critical categori  \n","0       O3     BAIK  \n","1       O3     BAIK  \n","2     PM11     BAIK  \n","3       O3     BAIK  \n","4     PM11     BAIK  "]},"execution_count":164,"metadata":{},"output_type":"execute_result"}],"source":["path = './dataset/dataset_4_uts.xlsx'\n","df = pd.read_excel(path)\n","df = pd.DataFrame(df)\n","df.columns = ['tanggal', 'stasiun', 'pm10', 'so2', 'co', 'o3', 'no2', 'max', 'critical', 'categori']\n","df.head()"]},{"cell_type":"code","execution_count":165,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":297,"status":"ok","timestamp":1666601328678,"user":{"displayName":"MOCHAMMAD HAIRULLAH","userId":"06941804815695378308"},"user_tz":-420},"id":"sdjReaK2WveE","outputId":"f02f8539-8f28-4621-c113-cd0b227ea2c8"},"outputs":[{"data":{"text/plain":["tanggal     object\n","stasiun     object\n","pm10         int64\n","so2          int64\n","co           int64\n","o3           int64\n","no2          int64\n","max          int64\n","critical    object\n","categori    object\n","dtype: object"]},"execution_count":165,"metadata":{},"output_type":"execute_result"}],"source":["df.dtypes"]},{"cell_type":"code","execution_count":166,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":612},"executionInfo":{"elapsed":518,"status":"ok","timestamp":1666601338059,"user":{"displayName":"MOCHAMMAD HAIRULLAH","userId":"06941804815695378308"},"user_tz":-420},"id":"YGGUAOCHW5mD","outputId":"89821990-bf04-4f98-c124-f7660c0b9700"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 155 entries, 0 to 154\n","Data columns (total 10 columns):\n"," #   Column    Non-Null Count  Dtype \n","---  ------    --------------  ----- \n"," 0   tanggal   155 non-null    object\n"," 1   stasiun   155 non-null    object\n"," 2   pm10      155 non-null    int64 \n"," 3   so2       155 non-null    int64 \n"," 4   co        155 non-null    int64 \n"," 5   o3        155 non-null    int64 \n"," 6   no2       155 non-null    int64 \n"," 7   max       155 non-null    int64 \n"," 8   critical  155 non-null    object\n"," 9   categori  155 non-null    object\n","dtypes: int64(6), object(4)\n","memory usage: 12.2+ KB\n"]},{"data":{"text/html":["\n","  <div id=\"df-5dcd7e46-8feb-4480-9b2c-9a277dba504f\">\n","    <div class=\"colab-df-container\">\n","      <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>pm10</th>\n","      <th>so2</th>\n","      <th>co</th>\n","      <th>o3</th>\n","      <th>no2</th>\n","      <th>max</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>155.000000</td>\n","      <td>155.000000</td>\n","      <td>155.000000</td>\n","      <td>155.000000</td>\n","      <td>155.000000</td>\n","      <td>155.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>46.200000</td>\n","      <td>18.574194</td>\n","      <td>22.935484</td>\n","      <td>50.129032</td>\n","      <td>7.600000</td>\n","      <td>55.754839</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>14.283493</td>\n","      <td>10.866336</td>\n","      <td>11.394734</td>\n","      <td>20.985681</td>\n","      <td>3.868957</td>\n","      <td>18.735459</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>25.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>36.000000</td>\n","      <td>11.000000</td>\n","      <td>15.000000</td>\n","      <td>35.000000</td>\n","      <td>5.000000</td>\n","      <td>41.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>44.000000</td>\n","      <td>15.000000</td>\n","      <td>21.000000</td>\n","      <td>45.000000</td>\n","      <td>7.000000</td>\n","      <td>51.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>55.000000</td>\n","      <td>28.500000</td>\n","      <td>27.000000</td>\n","      <td>62.500000</td>\n","      <td>9.000000</td>\n","      <td>65.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>86.000000</td>\n","      <td>61.000000</td>\n","      <td>71.000000</td>\n","      <td>125.000000</td>\n","      <td>21.000000</td>\n","      <td>125.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" 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1.000000   25.000000\n","25%     36.000000   11.000000   15.000000   35.000000    5.000000   41.000000\n","50%     44.000000   15.000000   21.000000   45.000000    7.000000   51.000000\n","75%     55.000000   28.500000   27.000000   62.500000    9.000000   65.000000\n","max     86.000000   61.000000   71.000000  125.000000   21.000000  125.000000"]},"execution_count":166,"metadata":{},"output_type":"execute_result"}],"source":["df.shape\n","\n","# info df\n","df.info()\n","\n","# deskripsi df\n","df.describe()"]},{"cell_type":"code","execution_count":167,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":340},"executionInfo":{"elapsed":1073,"status":"ok","timestamp":1666601346884,"user":{"displayName":"MOCHAMMAD HAIRULLAH","userId":"06941804815695378308"},"user_tz":-420},"id":"gwSGfSOsW_WA","outputId":"4240fa5e-1cb8-4e4d-c959-aafc3c749e5a"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n","  warnings.warn(msg, UserWarning)\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 864x288 with 3 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt \n","import seaborn as sns\n","sns.pairplot(df, x_vars=['pm10', 'so2', 'co'], \n","             y_vars='max', size=4, aspect=1, kind='scatter')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"lS9ZKXDvHdb1"},"source":["# MENCARI MEAN [RUMUS]"]},{"cell_type":"markdown","metadata":{"id":"vwceyCFCHgyT"},"source":["![mean.png](data:image/png;base64,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)"]},{"cell_type":"markdown","metadata":{"id":"8yWHQHZPHnNE"},"source":["# MENCARI STDEV 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)"]},{"cell_type":"code","execution_count":169,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":771,"status":"ok","timestamp":1666601496133,"user":{"displayName":"MOCHAMMAD HAIRULLAH","userId":"06941804815695378308"},"user_tz":-420},"id":"AJFF5JVkiFZ7","outputId":"8797a2e2-fb57-4fa4-9606-bd638972b825"},"outputs":[{"name":"stdout","output_type":"stream","text":["--- Mencari Mean dan STDEV ---\n","--- BAIK ---\n","MEAN   :  35.12\n","STDEV  :  10.817886423265252 \n","\n","--- SEDANG ---\n","MEAN   :  53.51428571428571\n","STDEV  :  15.473642539666546 \n","\n","--- TIDAK SEHAT ---\n","MEAN   :  55.25\n","STDEV  :  24.45914961727002\n"]}],"source":["# Klasifikasi PM10\n","\n","def pm(x):\n","  return df[(df['categori'] == x)]\n","\n","pm_baik = pm('BAIK')['pm10'].drop_duplicates()\n","pm_sedang = pm('SEDANG')['pm10'].drop_duplicates()\n","pm_tidak_sehat = pm('TIDAK SEHAT')['pm10'].drop_duplicates()\n","\n","def meanPM(x, y):\n","  return x / y\n","\n","mean_pm_baik = meanPM(pm_baik.sum(), pm_baik.count())\n","mean_pm_sedang = meanPM(pm_sedang.sum(), pm_sedang.count())\n","mean_pm_tidak_sehat = meanPM(pm_tidak_sehat.sum(), pm_tidak_sehat.count())\n","\n","std_pm_baik = pm_baik.std()\n","std_pm_sedang = pm_sedang.std()\n","std_pm_tidak_sehat = pm_tidak_sehat.std()\n","\n","print('--- Mencari Mean dan STDEV ---')\n","print('--- BAIK ---')\n","print('MEAN   : ', mean_pm_baik)\n","print('STDEV  : ', std_pm_baik, '\\n')\n","print('--- SEDANG ---')\n","print('MEAN   : ', mean_pm_sedang)\n","print('STDEV  : ', std_pm_sedang, '\\n')\n","print('--- TIDAK SEHAT ---')\n","print('MEAN   : ', mean_pm_tidak_sehat)\n","print('STDEV  : ', std_pm_tidak_sehat)"]},{"cell_type":"code","execution_count":170,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":316,"status":"ok","timestamp":1666601498617,"user":{"displayName":"MOCHAMMAD HAIRULLAH","userId":"06941804815695378308"},"user_tz":-420},"id":"cqWCol9Rrkp1","outputId":"345e5f4b-0059-41a0-f706-81fd946c879e"},"outputs":[{"name":"stdout","output_type":"stream","text":["--- Mencari Mean dan STDEV ---\n","--- BAIK ---\n","MEAN   :  21.583333333333332\n","STDEV  :  10.640025607466189 \n","\n","--- SEDANG ---\n","MEAN   :  22.636363636363637\n","STDEV  :  13.960984596692702 \n","\n","--- TIDAK SEHAT ---\n","MEAN   :  17.5\n","STDEV  :  7.767453465154029\n"]}],"source":["# Klasifikasi SO2\n","def so(x):\n","  return df[(df['categori'] == x)]\n","\n","so_baik = so('BAIK')['so2'].drop_duplicates()\n","so_sedang = so('SEDANG')['so2'].drop_duplicates()\n","so_tidak_sehat = so('TIDAK SEHAT')['so2'].drop_duplicates()\n","\n","\n","def meanSO(x, y):\n","  return x / y\n","\n","mean_so_baik = meanSO(so_baik.sum(), so_baik.count())\n","mean_so_sedang = meanSO(so_sedang.sum(), so_sedang.count())\n","mean_so_tidak_sehat = meanSO(so_tidak_sehat.sum(), so_tidak_sehat.count())\n","\n","std_so_baik = so_baik.std()\n","std_so_sedang = so_sedang.std()\n","std_so_tidak_sehat = so_tidak_sehat.std()\n","\n","print('--- Mencari Mean dan STDEV ---')\n","print('--- BAIK ---')\n","print('MEAN   : ', mean_so_baik)\n","print('STDEV  : ', std_so_baik, '\\n')\n","print('--- SEDANG ---')\n","print('MEAN   : ', mean_so_sedang)\n","print('STDEV  : ', std_so_sedang, '\\n')\n","print('--- TIDAK SEHAT ---')\n","print('MEAN   : ', mean_so_tidak_sehat)\n","print('STDEV  : ', std_so_tidak_sehat)"]},{"cell_type":"code","execution_count":171,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":308,"status":"ok","timestamp":1666601541856,"user":{"displayName":"MOCHAMMAD HAIRULLAH","userId":"06941804815695378308"},"user_tz":-420},"id":"3_8Qej-0r4fU","outputId":"6482512a-e068-4f11-ffd3-cb226b0e540f"},"outputs":[{"name":"stdout","output_type":"stream","text":["--- Mencari Mean dan STDEV ---\n","--- BAIK ---\n","MEAN   :  18.782608695652176\n","STDEV  :  9.204527155595922 \n","\n","--- SEDANG ---\n","MEAN   :  30.20689655172414\n","STDEV  :  14.082358036982775 \n","\n","--- TIDAK SEHAT ---\n","MEAN   :  40.75\n","STDEV  :  15.861378670636842\n"]}],"source":["# Klasifikasi CO\n","\n","def co(x):\n","  return df[(df['categori'] == x)]\n","\n","co_baik = co('BAIK')['co'].drop_duplicates()\n","co_sedang = co('SEDANG')['co'].drop_duplicates()\n","co_tidak_sehat = co('TIDAK SEHAT')['co'].drop_duplicates()\n","\n","\n","def meanCO(x, y):\n","  return x / y\n","\n","mean_co_baik = meanCO(co_baik.sum(), co_baik.count())\n","mean_co_sedang = meanCO(co_sedang.sum(), co_sedang.count())\n","mean_co_tidak_sehat = meanCO(co_tidak_sehat.sum(), co_tidak_sehat.count())\n","\n","std_co_baik = co_baik.std()\n","std_co_sedang = co_sedang.std()\n","std_co_tidak_sehat = co_tidak_sehat.std()\n","\n","print('--- Mencari Mean dan STDEV ---')\n","print('--- BAIK ---')\n","print('MEAN   : ', mean_co_baik)\n","print('STDEV  : ', std_co_baik, '\\n')\n","print('--- SEDANG ---')\n","print('MEAN   : ', mean_co_sedang)\n","print('STDEV  : ', std_co_sedang, '\\n')\n","print('--- TIDAK SEHAT ---')\n","print('MEAN   : ', mean_co_tidak_sehat)\n","print('STDEV  : ', std_co_tidak_sehat)"]},{"cell_type":"code","execution_count":173,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":505,"status":"ok","timestamp":1666601648653,"user":{"displayName":"MOCHAMMAD HAIRULLAH","userId":"06941804815695378308"},"user_tz":-420},"id":"KtwRbaTs150L","outputId":"7e18624c-5810-44e3-a197-fdb0d76204de"},"outputs":[{"name":"stdout","output_type":"stream","text":["---- PROBABILITAS BERDASARKAN CATEGORI ----\n","BAIK         :  0.45161290322580644\n","SEDANG       :  0.5225806451612903\n","TIDAK SEHAT  :  0.025806451612903226\n"]}],"source":["# Mencari Probabilitas per Categori\n","count_all = df['categori'].count()\n","\n","def categ(x):\n","  return df[(df['categori'] == x)]\n","\n","def probabilitas(x, y):\n","  return x / y\n","\n","categories_baik = probabilitas(categ('BAIK')['categori'].count(), count_all)\n","categories_sedang = probabilitas(categ('SEDANG')['categori'].count(), count_all)\n","categories_tidak_sehat = probabilitas(categ('TIDAK SEHAT')['categori'].count(), count_all)\n","\n","print('---- PROBABILITAS BERDASARKAN CATEGORI ----')\n","print(\"BAIK         : \", categories_baik)\n","print(\"SEDANG       : \", categories_sedang)\n","print(\"TIDAK SEHAT  : \", categories_tidak_sehat)"]},{"cell_type":"code","execution_count":174,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":356},"executionInfo":{"elapsed":1206,"status":"ok","timestamp":1666601658081,"user":{"displayName":"MOCHAMMAD 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size 1440x360 with 3 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["names = ['BAIK', 'SEDANG', 'TIDAK SEHAT']\n","values = [(categories_baik*100), (categories_sedang*100), (categories_tidak_sehat*100)]\n","\n","plt.figure(figsize=(20, 5))\n","\n","plt.subplot(131)\n","plt.bar(names, values)\n","plt.subplot(132)\n","plt.scatter(names, values)\n","plt.subplot(133)\n","plt.plot(names, values)\n","plt.suptitle('CATEGORICAL')\n","plt.show()"]},{"cell_type":"code","execution_count":185,"metadata":{"executionInfo":{"elapsed":522,"status":"ok","timestamp":1666601943257,"user":{"displayName":"MOCHAMMAD HAIRULLAH","userId":"06941804815695378308"},"user_tz":-420},"id":"n7jROoBr34ep"},"outputs":[],"source":["# Membuat DATA SAMPEL untuk pengujian data\n","# X = yang akan di gunakan\n","pm10, so2, co = 31, 21, 11\n","\n","# Y = yang akan di gunakan\n","categori_result = ''"]},{"cell_type":"code","execution_count":186,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1666601945196,"user":{"displayName":"MOCHAMMAD HAIRULLAH","userId":"06941804815695378308"},"user_tz":-420},"id":"gphVUSVl4o1I","outputId":"0124ef6b-364d-4835-88c7-29dcd2c5c010"},"outputs":[{"name":"stdout","output_type":"stream","text":["---- PROBABILITAS PM10 ----\n","BAIK         :  0.10494354604011777\n","SEDANG       :  0.01221238619651271\n","TIDAK SEHAT  :  0.030192602412440064\n"]}],"source":["# Mencari Probabilitas di setiap kolom\n","# PM10\n","def prob(stdev, sampel, mean):\n","  result = 1 / np.sqrt(2 * 3.14 * stdev) * np.exp(-(pow(sampel - mean, 2))/pow(stdev, 2))\n","  return result\n","\n","prob_pm_baik = prob(std_pm_baik, pm10, mean_pm_baik)\n","prob_pm_sedang = prob(std_pm_sedang, pm10, mean_pm_sedang)\n","prob_pm_tidak_sehat = prob(std_pm_tidak_sehat, pm10, mean_pm_tidak_sehat)\n","\n","print('---- PROBABILITAS PM10 ----')\n","print(\"BAIK         : \", prob_pm_baik)\n","print(\"SEDANG       : \", prob_pm_sedang)\n","print(\"TIDAK SEHAT  : \", prob_pm_tidak_sehat)"]},{"cell_type":"code","execution_count":187,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":310,"status":"ok","timestamp":1666601947401,"user":{"displayName":"MOCHAMMAD HAIRULLAH","userId":"06941804815695378308"},"user_tz":-420},"id":"bkhMaWB176YG","outputId":"21460886-cb7a-4f55-d0ab-acfc46d86518"},"outputs":[{"name":"stdout","output_type":"stream","text":["---- PROBABILITAS SO2 ----\n","BAIK         :  0.12196731821508569\n","SEDANG       :  0.10534059357094201\n","TIDAK SEHAT  :  0.11686979233556904\n"]}],"source":["prob_so_baik = prob(std_so_baik, so2, mean_so_baik)\n","prob_so_sedang = prob(std_so_sedang, so2, mean_so_sedang)\n","prob_so_tidak_sehat = prob(std_so_tidak_sehat, so2, mean_so_tidak_sehat)\n","\n","print('---- PROBABILITAS SO2 ----')\n","print(\"BAIK         : \", prob_so_baik)\n","print(\"SEDANG       : \", prob_so_sedang)\n","print(\"TIDAK SEHAT  : \", prob_so_tidak_sehat)"]},{"cell_type":"code","execution_count":188,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1666601949012,"user":{"displayName":"MOCHAMMAD HAIRULLAH","userId":"06941804815695378308"},"user_tz":-420},"id":"Zwbm0jMD8CZl","outputId":"4fa49dd4-a675-47ea-8152-b53c0a49cf87"},"outputs":[{"name":"stdout","output_type":"stream","text":["---- PROBABILITAS CO ----\n","BAIK         :  0.06434885021472338\n","SEDANG       :  0.016550094130357008\n","TIDAK SEHAT  :  0.0029717683879102496\n"]}],"source":["prob_co_baik = prob(std_co_baik, co, mean_co_baik)\n","prob_co_sedang = prob(std_co_sedang, co, mean_co_sedang)\n","prob_co_tidak_sehat = prob(std_co_tidak_sehat, co, mean_co_tidak_sehat)\n","\n","print('---- PROBABILITAS CO ----')\n","print(\"BAIK         : \", prob_co_baik)\n","print(\"SEDANG       : \", prob_co_sedang)\n","print(\"TIDAK SEHAT  : \", prob_co_tidak_sehat)"]},{"cell_type":"markdown","metadata":{"id":"zm1UbyDCHAtk"},"source":["![prob.png](data:image/png;base64,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)"]},{"cell_type":"code","execution_count":189,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":510,"status":"ok","timestamp":1666601951316,"user":{"displayName":"MOCHAMMAD HAIRULLAH","userId":"06941804815695378308"},"user_tz":-420},"id":"t2a586lkAC8k","outputId":"4ea142cb-0fcb-4bf6-b9a0-9f12eed60e65"},"outputs":[{"name":"stdout","output_type":"stream","text":["----- HASIL KLASIFIKASI ----\n","BAIK         :  0.000823644876\n","SEDANG       :  0.000021291034\n","TIDAK SEHAT  :  0.000010486191\n","----- -------------------- ----\n","Berdasarkan hasil klasifikasi maka yang terbesar adalah hasil \n","akhirnya yang akan menjadi prediksi selanjutnya\n","RESULT       :  BAIK\n"]}],"source":["# HASIL KLASIFIKASI\n","\n","baik = prob_pm_baik * prob_so_baik * prob_co_baik\n","sedang = prob_pm_sedang * prob_so_sedang * prob_co_sedang\n","tidak_sehat = prob_pm_tidak_sehat * prob_so_tidak_sehat * prob_co_tidak_sehat\n","\n","def categoryClas(x, y, z):\n","  if x > y and x > z:\n","    result = 'BAIK'\n","  elif y > x and y > z:\n","    result = 'SEDANG'\n","  elif z > x and z > y:\n","    result = 'TIDAK SEHAT'\n","  \n","  return result\n","\n","print('----- HASIL KLASIFIKASI ----')\n","print('BAIK         : ', format(float(baik), '.12f'))\n","print('SEDANG       : ', format(float(sedang), '.12f'))\n","print('TIDAK SEHAT  : ', format(float(tidak_sehat), '.12f'))\n","print('----- -------------------- ----')\n","print('Berdasarkan hasil klasifikasi maka yang terbesar adalah hasil \\nakhirnya yang akan menjadi prediksi selanjutnya')\n","\n","print('RESULT       : ', categoryClas(baik, sedang, tidak_sehat))\n"]}],"metadata":{"colab":{"authorship_tag":"ABX9TyMOe+GHMlWWOVrd9NQj0NW3","collapsed_sections":[],"provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.0"},"vscode":{"interpreter":{"hash":"aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"}}},"nbformat":4,"nbformat_minor":0}