Membuat_Model_untuk_Klasifikasi_Dua_Kelas.ipynb

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    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "UM930hIhZTgZ"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('citrus.csv')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
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      "execution_count": 19,
      "outputs": [
        {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 6 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   name      10000 non-null  object \n",
            " 1   diameter  10000 non-null  float64\n",
            " 2   weight    10000 non-null  float64\n",
            " 3   red       10000 non-null  int64  \n",
            " 4   green     10000 non-null  int64  \n",
            " 5   blue      10000 non-null  int64  \n",
            "dtypes: float64(2), int64(3), object(1)\n",
            "memory usage: 468.9+ KB\n"
          ]
        }
      ]
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      "source": [
        "df.head (10000)"
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              "            name  diameter  weight  red  green  blue\n",
              "0         orange      2.96   86.76  172     85     2\n",
              "1         orange      3.91   88.05  166     78     3\n",
              "2         orange      4.42   95.17  156     81     2\n",
              "3         orange      4.47   95.60  163     81     4\n",
              "4         orange      4.48   95.76  161     72     9\n",
              "...          ...       ...     ...  ...    ...   ...\n",
              "9995  grapefruit     15.35  253.89  149     77    20\n",
              "9996  grapefruit     15.41  254.67  148     68     7\n",
              "9997  grapefruit     15.59  256.50  168     82    20\n",
              "9998  grapefruit     15.92  260.14  142     72    11\n",
              "9999  grapefruit     16.45  261.51  152     74     2\n",
              "\n",
              "[10000 rows x 6 columns]"
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              "      <th>name</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>orange</td>\n",
              "      <td>2.96</td>\n",
              "      <td>86.76</td>\n",
              "      <td>172</td>\n",
              "      <td>85</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>orange</td>\n",
              "      <td>3.91</td>\n",
              "      <td>88.05</td>\n",
              "      <td>166</td>\n",
              "      <td>78</td>\n",
              "      <td>3</td>\n",
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              "      <th>2</th>\n",
              "      <td>orange</td>\n",
              "      <td>4.42</td>\n",
              "      <td>95.17</td>\n",
              "      <td>156</td>\n",
              "      <td>81</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>orange</td>\n",
              "      <td>4.47</td>\n",
              "      <td>95.60</td>\n",
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              "      <th>9995</th>\n",
              "      <td>grapefruit</td>\n",
              "      <td>15.35</td>\n",
              "      <td>253.89</td>\n",
              "      <td>149</td>\n",
              "      <td>77</td>\n",
              "      <td>20</td>\n",
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              "      <th>9996</th>\n",
              "      <td>grapefruit</td>\n",
              "      <td>15.41</td>\n",
              "      <td>254.67</td>\n",
              "      <td>148</td>\n",
              "      <td>68</td>\n",
              "      <td>7</td>\n",
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              "      <th>9997</th>\n",
              "      <td>grapefruit</td>\n",
              "      <td>15.59</td>\n",
              "      <td>256.50</td>\n",
              "      <td>168</td>\n",
              "      <td>82</td>\n",
              "      <td>20</td>\n",
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              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>grapefruit</td>\n",
              "      <td>15.92</td>\n",
              "      <td>260.14</td>\n",
              "      <td>142</td>\n",
              "      <td>72</td>\n",
              "      <td>11</td>\n",
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              "      <th>9999</th>\n",
              "      <td>grapefruit</td>\n",
              "      <td>16.45</td>\n",
              "      <td>261.51</td>\n",
              "      <td>152</td>\n",
              "      <td>74</td>\n",
              "      <td>2</td>\n",
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              "<p>10000 rows × 6 columns</p>\n",
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      ]
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    {
      "cell_type": "code",
      "source": [
        "df.name[df.name == 'orange'] = 0\n",
        "df.name[df.name == 'grapefruit'] = 1\n",
        "# Mengubah nilai string 'orange' menjadi 0 dan 'grapefruit' menjadi 1\n",
        "\n",
        "dataset = df.values\n",
        "dataset\n",
        "# Mengkonversi dataframe menjadi array numpy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "niaUvqtpZzvf",
        "outputId": "039f6e8b-817d-4203-a2c8-4224813ba3e7"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-cd2df205fa94>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.name[df.name == 'orange'] = 0\n",
            "<ipython-input-21-cd2df205fa94>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.name[df.name == 'grapefruit'] = 1\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 2.96, 86.76, 172, 85, 2],\n",
              "       [0, 3.91, 88.05, 166, 78, 3],\n",
              "       [0, 4.42, 95.17, 156, 81, 2],\n",
              "       ...,\n",
              "       [1, 15.59, 256.5, 168, 82, 20],\n",
              "       [1, 15.92, 260.14, 142, 72, 11],\n",
              "       [1, 16.45, 261.51, 152, 74, 2]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset[:,1:6] # Memilih 5 kolom kedua sampai keenam sebagai atribut\n",
        "y = dataset[:,0] # Memilih kolom pertama sebagai label"
      ],
      "metadata": {
        "id": "Fm8KChWUa867"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "min_max_scaler = preprocessing.MinMaxScaler()\n",
        "X_scale = min_max_scaler.fit_transform(X)\n",
        "X_scale\n",
        "# Mengaplikasikan normalisasi dengan MinMaxScaler pada atribut"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hgKyvf8VbCPl",
        "outputId": "500838d5-efa7-4a04-c9e3-5fd7c9254e44"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.        , 0.74025974, 0.63529412, 0.        ],\n",
              "       [0.07042254, 0.00738197, 0.66233766, 0.55294118, 0.01851852],\n",
              "       [0.10822832, 0.04812589, 0.53246753, 0.58823529, 0.        ],\n",
              "       ...,\n",
              "       [0.93624907, 0.97133047, 0.68831169, 0.6       , 0.33333333],\n",
              "       [0.96071164, 0.99216023, 0.35064935, 0.48235294, 0.16666667],\n",
              "       [1.        , 1.        , 0.48051948, 0.50588235, 0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X_scale, y, test_size=0.3)\n",
        "# Memisahkan data menjadi 70% data latih dan 30% data uji"
      ],
      "metadata": {
        "id": "A8NM3Lb-ex37"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "Y_train = Y_train.astype(np.float32)\n",
        "Y_test = Y_test.astype(np.float32)"
      ],
      "metadata": {
        "id": "0jj1mBI2e4m4"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense"
      ],
      "metadata": {
        "id": "l42AVczjj5Ku"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential([\n",
        "                    Dense(32, activation='relu', input_shape=(5,)),\n",
        "                    Dense(32, activation='relu'),\n",
        "                    Dense(1, activation='sigmoid'),])"
      ],
      "metadata": {
        "id": "1jCzFuIVj-FQ"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='sgd',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "axajNbBqkAw_"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, Y_train, epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oqaZJaczkEnS",
        "outputId": "ad39c672-9c88-421c-b26d-c255c8b6ae1b"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "219/219 [==============================] - 1s 1ms/step - loss: 0.6572 - accuracy: 0.8454\n",
            "Epoch 2/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.6149 - accuracy: 0.9120\n",
            "Epoch 3/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.5527 - accuracy: 0.9167\n",
            "Epoch 4/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.4650 - accuracy: 0.9237\n",
            "Epoch 5/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.3695 - accuracy: 0.9241\n",
            "Epoch 6/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.2972 - accuracy: 0.9243\n",
            "Epoch 7/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.2517 - accuracy: 0.9256\n",
            "Epoch 8/100\n",
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            "Epoch 9/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.2084 - accuracy: 0.9256\n",
            "Epoch 10/100\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.1983 - accuracy: 0.9264\n",
            "Epoch 11/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1921 - accuracy: 0.9264\n",
            "Epoch 12/100\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.1877 - accuracy: 0.9273\n",
            "Epoch 13/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1851 - accuracy: 0.9277\n",
            "Epoch 14/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1829 - accuracy: 0.9273\n",
            "Epoch 15/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1817 - accuracy: 0.9274\n",
            "Epoch 16/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1807 - accuracy: 0.9267\n",
            "Epoch 17/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1799 - accuracy: 0.9281\n",
            "Epoch 18/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1793 - accuracy: 0.9271\n",
            "Epoch 19/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1788 - accuracy: 0.9267\n",
            "Epoch 20/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1785 - accuracy: 0.9274\n",
            "Epoch 21/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1782 - accuracy: 0.9263\n",
            "Epoch 22/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1783 - accuracy: 0.9264\n",
            "Epoch 23/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1781 - accuracy: 0.9279\n",
            "Epoch 24/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1778 - accuracy: 0.9281\n",
            "Epoch 25/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1773 - accuracy: 0.9280\n",
            "Epoch 26/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1781 - accuracy: 0.9263\n",
            "Epoch 27/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1778 - accuracy: 0.9267\n",
            "Epoch 28/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1777 - accuracy: 0.9273\n",
            "Epoch 29/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1774 - accuracy: 0.9273\n",
            "Epoch 30/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1775 - accuracy: 0.9274\n",
            "Epoch 31/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1777 - accuracy: 0.9269\n",
            "Epoch 32/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1776 - accuracy: 0.9279\n",
            "Epoch 33/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1773 - accuracy: 0.9274\n",
            "Epoch 34/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1773 - accuracy: 0.9283\n",
            "Epoch 35/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1775 - accuracy: 0.9274\n",
            "Epoch 36/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1773 - accuracy: 0.9271\n",
            "Epoch 37/100\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.1773 - accuracy: 0.9276\n",
            "Epoch 38/100\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.1771 - accuracy: 0.9269\n",
            "Epoch 39/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1775 - accuracy: 0.9276\n",
            "Epoch 40/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1772 - accuracy: 0.9267\n",
            "Epoch 41/100\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.1775 - accuracy: 0.9263\n",
            "Epoch 42/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1767 - accuracy: 0.9274\n",
            "Epoch 43/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1778 - accuracy: 0.9274\n",
            "Epoch 44/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1771 - accuracy: 0.9276\n",
            "Epoch 45/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1774 - accuracy: 0.9264\n",
            "Epoch 46/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1770 - accuracy: 0.9286\n",
            "Epoch 47/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1771 - accuracy: 0.9273\n",
            "Epoch 48/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1768 - accuracy: 0.9283\n",
            "Epoch 49/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1769 - accuracy: 0.9280\n",
            "Epoch 50/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1770 - accuracy: 0.9274\n",
            "Epoch 51/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1770 - accuracy: 0.9277\n",
            "Epoch 52/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1765 - accuracy: 0.9283\n",
            "Epoch 53/100\n",
            "219/219 [==============================] - 0s 2ms/step - loss: 0.1772 - accuracy: 0.9274\n",
            "Epoch 54/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1769 - accuracy: 0.9274\n",
            "Epoch 55/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1764 - accuracy: 0.9277\n",
            "Epoch 56/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1770 - accuracy: 0.9277\n",
            "Epoch 57/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1769 - accuracy: 0.9281\n",
            "Epoch 58/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1765 - accuracy: 0.9281\n",
            "Epoch 59/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1770 - accuracy: 0.9267\n",
            "Epoch 60/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1766 - accuracy: 0.9276\n",
            "Epoch 61/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1766 - accuracy: 0.9261\n",
            "Epoch 62/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1769 - accuracy: 0.9266\n",
            "Epoch 63/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1767 - accuracy: 0.9276\n",
            "Epoch 64/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1766 - accuracy: 0.9273\n",
            "Epoch 65/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1769 - accuracy: 0.9269\n",
            "Epoch 66/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1764 - accuracy: 0.9277\n",
            "Epoch 67/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1768 - accuracy: 0.9281\n",
            "Epoch 68/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1767 - accuracy: 0.9266\n",
            "Epoch 69/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1768 - accuracy: 0.9276\n",
            "Epoch 70/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1764 - accuracy: 0.9283\n",
            "Epoch 71/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1768 - accuracy: 0.9263\n",
            "Epoch 72/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1763 - accuracy: 0.9277\n",
            "Epoch 73/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1766 - accuracy: 0.9274\n",
            "Epoch 74/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1764 - accuracy: 0.9264\n",
            "Epoch 75/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1769 - accuracy: 0.9276\n",
            "Epoch 76/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1765 - accuracy: 0.9270\n",
            "Epoch 77/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1756 - accuracy: 0.9276\n",
            "Epoch 78/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1764 - accuracy: 0.9277\n",
            "Epoch 79/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1765 - accuracy: 0.9280\n",
            "Epoch 80/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1764 - accuracy: 0.9267\n",
            "Epoch 81/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1764 - accuracy: 0.9267\n",
            "Epoch 82/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1763 - accuracy: 0.9273\n",
            "Epoch 83/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1761 - accuracy: 0.9274\n",
            "Epoch 84/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1763 - accuracy: 0.9273\n",
            "Epoch 85/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1760 - accuracy: 0.9279\n",
            "Epoch 86/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1761 - accuracy: 0.9274\n",
            "Epoch 87/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1764 - accuracy: 0.9276\n",
            "Epoch 88/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1764 - accuracy: 0.9273\n",
            "Epoch 89/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1762 - accuracy: 0.9271\n",
            "Epoch 90/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1762 - accuracy: 0.9283\n",
            "Epoch 91/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1760 - accuracy: 0.9281\n",
            "Epoch 92/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1761 - accuracy: 0.9274\n",
            "Epoch 93/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1764 - accuracy: 0.9276\n",
            "Epoch 94/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1762 - accuracy: 0.9273\n",
            "Epoch 95/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1761 - accuracy: 0.9274\n",
            "Epoch 96/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1758 - accuracy: 0.9287\n",
            "Epoch 97/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1756 - accuracy: 0.9280\n",
            "Epoch 98/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1756 - accuracy: 0.9299\n",
            "Epoch 99/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1758 - accuracy: 0.9280\n",
            "Epoch 100/100\n",
            "219/219 [==============================] - 0s 1ms/step - loss: 0.1762 - accuracy: 0.9259\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f30919ed270>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, Y_test)\n",
        "# elemen pertama adalah loss dan elemen kedua adalah akurasi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KQdddRxjkIDB",
        "outputId": "daa9f49b-3859-4450-8554-c1c9257528d6"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "94/94 [==============================] - 0s 1ms/step - loss: 0.1822 - accuracy: 0.9277\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.18221807479858398, 0.9276666641235352]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    }
  ]
}