{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Membaca dataset dari file CSV dengan pemisah semicolon\n",
        "file_path = 'Dataset_beat.csv'  # Gantilah dengan path yang sesuai\n",
        "df = pd.read_csv(file_path, sep=';')\n",
        "\n",
        "# Menampilkan beberapa baris pertama dataset\n",
        "print(df.head())\n",
        "\n",
        "# Visualisasi Scatter plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(data=df, x='Sistem Pembakaran', y='Konsentrasi CO (ppm)', hue='Sistem Pembakaran', palette='Set2', s=100)\n",
        "plt.title('Scatter Plot - Sistem Pembakaran vs Konsentrasi CO (ppm)')\n",
        "plt.show()\n",
        "\n",
        "# Features dan target\n",
        "features = df[['Sistem Pembakaran', 'Konsentrasi CO (ppm)']]\n",
        "target = df['Sistem Pembakaran']  # Menyesuaikan target dengan Sistem Pembakaran\n",
        "\n",
        "# Convert categorical features to numerical\n",
        "features_encoded = pd.get_dummies(features)\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(features_encoded, target, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create a k-NN model\n",
        "model = KNeighborsClassifier(n_neighbors=3)\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "predictions = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, predictions)\n",
        "report = classification_report(y_test, predictions)\n",
        "\n",
        "# Print the results\n",
        "print(f'Accuracy: {accuracy}')\n",
        "print('Classification Report:')\n",
        "print(report)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UBxJWxuzu5Gx",
        "outputId": "1af640f8-7000-4847-87a5-d9d9967d358b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Nama Kendaraan Jenis Mesin Sistem Pembakaran Bahan Bakar  \\\n",
            "0      Honda Beat       4 tak        Karburator   Pertalite   \n",
            "1      Honda Beat       4 tak           Injeksi    Pertamax   \n",
            "2  Honda Beat esp       4 tak        Karburator    Pertamax   \n",
            "3      Honda Beat       4 tak           Injeksi    Pertamax   \n",
            "4      Honda Beat       4 tak           Injeksi   Pertalite   \n",
            "\n",
            "   Konsentrasi CO (ppm)  Tahun Pembuatan Nomor Kendaraan  \n",
            "0                    51             2013       K 5674 AK  \n",
            "1                    37             2020       H 7651 WU  \n",
            "2                    35             2012      AB 1234 AK  \n",
            "3                    44             2020      AB 9012 BC  \n",
            "4                    49             2016       K 5678 FG  \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     Injeksi       1.00      0.67      0.80         3\n",
            "  Karburator       0.67      1.00      0.80         2\n",
            "\n",
            "    accuracy                           0.80         5\n",
            "   macro avg       0.83      0.83      0.80         5\n",
            "weighted avg       0.87      0.80      0.80         5\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Membaca dataset dari file CSV dengan pemisah semicolon\n",
        "file_path = 'Dataset_beat.csv'  # Gantilah dengan path yang sesuai\n",
        "df = pd.read_csv(file_path, sep=';')\n",
        "\n",
        "# Menampilkan beberapa baris pertama dataset\n",
        "print(df.head())\n",
        "\n",
        "# Mencari kombinasi sistem pembakaran dan bahan bakar dengan konsentrasi CO(ppm) terendah\n",
        "min_co_combination = df.loc[df['Konsentrasi CO (ppm)'].idxmin(), ['Sistem Pembakaran', 'Bahan Bakar', 'Konsentrasi CO (ppm)']]\n",
        "\n",
        "# Mencari kombinasi sistem pembakaran dan bahan bakar dengan konsentrasi CO(ppm) tertinggi\n",
        "max_co_combination = df.loc[df['Konsentrasi CO (ppm)'].idxmax(), ['Sistem Pembakaran', 'Bahan Bakar', 'Konsentrasi CO (ppm)']]\n",
        "\n",
        "print(\"\\nKombinasi dengan Konsentrasi CO(ppm) terendah:\")\n",
        "print(min_co_combination)\n",
        "\n",
        "print(\"\\nKombinasi dengan Konsentrasi CO(ppm) tertinggi:\")\n",
        "print(max_co_combination)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JObQSzNRwkmW",
        "outputId": "deaff405-945f-4859-cefe-80bc545fe54f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Nama Kendaraan Jenis Mesin Sistem Pembakaran Bahan Bakar  \\\n",
            "0      Honda Beat       4 tak        Karburator   Pertalite   \n",
            "1      Honda Beat       4 tak           Injeksi    Pertamax   \n",
            "2  Honda Beat esp       4 tak        Karburator    Pertamax   \n",
            "3      Honda Beat       4 tak           Injeksi    Pertamax   \n",
            "4      Honda Beat       4 tak           Injeksi   Pertalite   \n",
            "\n",
            "   Konsentrasi CO (ppm)  Tahun Pembuatan Nomor Kendaraan  \n",
            "0                    51             2013       K 5674 AK  \n",
            "1                    37             2020       H 7651 WU  \n",
            "2                    35             2012      AB 1234 AK  \n",
            "3                    44             2020      AB 9012 BC  \n",
            "4                    49             2016       K 5678 FG  \n",
            "\n",
            "Kombinasi dengan Konsentrasi CO(ppm) terendah:\n",
            "Sistem Pembakaran       Karburator\n",
            "Bahan Bakar               Pertamax\n",
            "Konsentrasi CO (ppm)            35\n",
            "Name: 2, dtype: object\n",
            "\n",
            "Kombinasi dengan Konsentrasi CO(ppm) tertinggi:\n",
            "Sistem Pembakaran       Karburator\n",
            "Bahan Bakar              Pertalite\n",
            "Konsentrasi CO (ppm)            53\n",
            "Name: 11, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Membaca dataset dari file CSV dengan pemisah semicolon\n",
        "file_path = 'Dataset_beat.csv'  # Gantilah dengan path yang sesuai\n",
        "df = pd.read_csv(file_path, sep=';')\n",
        "\n",
        "# Menampilkan beberapa baris pertama dataset\n",
        "print(df.head())\n",
        "\n",
        "# Menentukan kategori untuk target \"Konsentrasi CO (ppm)\"\n",
        "bins = [0, 40, 60, 80, 100]  # Menyesuaikan interval kategori sesuai kebutuhan\n",
        "labels = ['Rendah', 'Sedang', 'Cukup Tinggi', 'Tinggi']\n",
        "df['Kategori CO'] = pd.cut(df['Konsentrasi CO (ppm)'], bins=bins, labels=labels, include_lowest=True)\n",
        "\n",
        "# Features dan target\n",
        "features = df[['Sistem Pembakaran', 'Bahan Bakar']]\n",
        "target = df['Kategori CO']\n",
        "\n",
        "# Convert categorical features to numerical\n",
        "features_encoded = pd.get_dummies(features)\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(features_encoded, target, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create a Decision Tree model\n",
        "model = DecisionTreeClassifier(random_state=42)\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "predictions = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, predictions)\n",
        "report = classification_report(y_test, predictions)\n",
        "\n",
        "# Print the results\n",
        "print(f'Accuracy: {accuracy}')\n",
        "print('Classification Report:')\n",
        "print(report)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7EYJlHGAzOfZ",
        "outputId": "7ea2a49c-5af8-40e4-af33-973f060b17cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Nama Kendaraan Jenis Mesin Sistem Pembakaran Bahan Bakar  \\\n",
            "0      Honda Beat       4 tak        Karburator   Pertalite   \n",
            "1      Honda Beat       4 tak           Injeksi    Pertamax   \n",
            "2  Honda Beat esp       4 tak        Karburator    Pertamax   \n",
            "3      Honda Beat       4 tak           Injeksi    Pertamax   \n",
            "4      Honda Beat       4 tak           Injeksi   Pertalite   \n",
            "\n",
            "   Konsentrasi CO (ppm)  Tahun Pembuatan Nomor Kendaraan  \n",
            "0                    51             2013       K 5674 AK  \n",
            "1                    37             2020       H 7651 WU  \n",
            "2                    35             2012      AB 1234 AK  \n",
            "3                    44             2020      AB 9012 BC  \n",
            "4                    49             2016       K 5678 FG  \n",
            "Accuracy: 0.6\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Rendah       0.00      0.00      0.00         1\n",
            "      Sedang       0.75      0.75      0.75         4\n",
            "\n",
            "    accuracy                           0.60         5\n",
            "   macro avg       0.38      0.38      0.38         5\n",
            "weighted avg       0.60      0.60      0.60         5\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "# Membaca dataset dari file CSV dengan pemisah semicolon\n",
        "file_path = 'Dataset_beat.csv'  # Gantilah dengan path yang sesuai\n",
        "df = pd.read_csv(file_path, sep=';')\n",
        "\n",
        "# Menampilkan beberapa baris pertama dataset\n",
        "print(df.head())\n",
        "\n",
        "# Visualisasi Scatter plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(data=df, x='Sistem Pembakaran', y='Konsentrasi CO (ppm)', hue='Sistem Pembakaran', palette='Set2', s=100)\n",
        "plt.title('Scatter Plot - Sistem Pembakaran vs Konsentrasi CO (ppm)')\n",
        "plt.show()\n",
        "\n",
        "# Features dan target\n",
        "features = df[['Sistem Pembakaran']]\n",
        "target = df['Konsentrasi CO (ppm)']\n",
        "\n",
        "# Convert categorical features to numerical\n",
        "features_encoded = pd.get_dummies(features)\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(features_encoded, target, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create a k-NN model\n",
        "model = KNeighborsRegressor(n_neighbors=3)\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "predictions = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "mse = mean_squared_error(y_test, predictions)\n",
        "r2 = r2_score(y_test, predictions)\n",
        "\n",
        "# Print the results\n",
        "print(f'Mean Squared Error: {mse}')\n",
        "print(f'R-squared: {r2}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 838
        },
        "id": "xEQPlh6sjkry",
        "outputId": "80a1774a-8ef8-4a2d-ab2b-c684c5dc7c80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Nama Kendaraan Jenis Mesin Sistem Pembakaran Bahan Bakar  \\\n",
            "0      Honda Beat       4 tak        Karburator   Pertalite   \n",
            "1      Honda Beat       4 tak           Injeksi    Pertamax   \n",
            "2  Honda Beat esp       4 tak        Karburator    Pertamax   \n",
            "3      Honda Beat       4 tak           Injeksi    Pertamax   \n",
            "4      Honda Beat       4 tak           Injeksi   Pertalite   \n",
            "\n",
            "   Konsentrasi CO (ppm)  Tahun Pembuatan Nomor Kendaraan  \n",
            "0                    51             2013       K 5674 AK  \n",
            "1                    37             2020       H 7651 WU  \n",
            "2                    35             2012      AB 1234 AK  \n",
            "3                    44             2020      AB 9012 BC  \n",
            "4                    49             2016       K 5678 FG  \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 35.71111111111112\n",
            "R-squared: -0.12158012283640462\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Membaca dataset dari file CSV dengan pemisah semicolon\n",
        "file_path = 'Dataset_beat.csv'  # Gantilah dengan path yang sesuai\n",
        "df = pd.read_csv(file_path, sep=';')\n",
        "\n",
        "# Menampilkan beberapa baris pertama dataset\n",
        "print(df.head())\n",
        "\n",
        "# Features dan target\n",
        "features = df[['Sistem Pembakaran', 'Bahan Bakar']]\n",
        "target = df['Konsentrasi CO (ppm)']\n",
        "\n",
        "# Convert categorical features to numerical\n",
        "features_encoded = pd.get_dummies(features)\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(features_encoded, target, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create a Linear Regression model\n",
        "model = LinearRegression()\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "predictions = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "mse = mean_squared_error(y_test, predictions)\n",
        "r2 = r2_score(y_test, predictions)\n",
        "\n",
        "# Print the results\n",
        "print(f'Mean Squared Error: {mse}')\n",
        "print(f'R-squared: {r2}')\n",
        "\n",
        "# Visualisasi hasil prediksi\n",
        "plt.scatter(X_test['Sistem Pembakaran_Karburator'], y_test, color='black', label='Actual')\n",
        "plt.scatter(X_test['Sistem Pembakaran_Karburator'], predictions, color='blue', label='Predicted')\n",
        "plt.title('Actual vs Predicted Konsentrasi CO (ppm) - Karburator')\n",
        "plt.xlabel('Sistem Pembakaran: Karburator')\n",
        "plt.ylabel('Konsentrasi CO (ppm)')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "id": "TxIgMxmCknaZ",
        "outputId": "efbcb561-0ea3-40d7-fa66-c9e727156a1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Nama Kendaraan Jenis Mesin Sistem Pembakaran Bahan Bakar  \\\n",
            "0      Honda Beat       4 tak        Karburator   Pertalite   \n",
            "1      Honda Beat       4 tak           Injeksi    Pertamax   \n",
            "2  Honda Beat esp       4 tak        Karburator    Pertamax   \n",
            "3      Honda Beat       4 tak           Injeksi    Pertamax   \n",
            "4      Honda Beat       4 tak           Injeksi   Pertalite   \n",
            "\n",
            "   Konsentrasi CO (ppm)  Tahun Pembuatan Nomor Kendaraan  \n",
            "0                    51             2013       K 5674 AK  \n",
            "1                    37             2020       H 7651 WU  \n",
            "2                    35             2012      AB 1234 AK  \n",
            "3                    44             2020      AB 9012 BC  \n",
            "4                    49             2016       K 5678 FG  \n",
            "Mean Squared Error: 14.55\n",
            "R-squared: 0.5430276381909549\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}