{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRSlxT4q5EHJ"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import tensorflow as tf\n",
        "from google.colab import files\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Upload file dari PC\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "gaeeT_rKVXTW",
        "outputId": "62920dd2-9d02-4444-a9fb-1782ef764fee"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-46071371-f2c8-4c28-9cf4-dae47652c7cf\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-46071371-f2c8-4c28-9cf4-dae47652c7cf\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving data real bulanan 2019-2023 supremo.csv to data real bulanan 2019-2023 supremo.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fRF6mEW5QbK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "f0881ba6-3661-41b7-834d-96d505d5e6b3"
      },
      "source": [
        "df = pd.read_csv('data real bulanan 2019-2023 supremo.csv', delimiter=';')\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Nomer  Tahun   Bulan  Produksi\n",
              "0      1   2019  Jan-19    170252\n",
              "1      2   2019  Feb-19    323468\n",
              "2      3   2019  Mar-19    616344\n",
              "3      4   2019  Apr-19    503524\n",
              "4      5   2019  May-19    277856"
            ],
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 60,\n  \"fields\": [\n    {\n      \"column\": \"Nomer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 1,\n        \"max\": 60,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          1,\n          6,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tahun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2019,\n        \"max\": 2023,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2020,\n          2023,\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bulan\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 60,\n        \"samples\": [\n          \"Jan-19\",\n          \"Jun-19\",\n          \"Jan-22\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Produksi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 162557,\n        \"min\": 90528,\n        \"max\": 734748,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          170252,\n          104272,\n          530084\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(9, 6))\n",
        "plt.plot(df.index, df['Produksi'], color='blue', marker='o', markersize=2, linewidth=2, alpha=0.7)\n",
        "plt.title('Data Produksi')\n",
        "plt.xlabel('Bulan')\n",
        "plt.ylabel('Produksi')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "IctHV7x_M-vw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "61e89784-c93c-4d69-f238-258f37cb0012"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Changed period to 12 for monthly data\n",
        "results = seasonal_decompose(df['Produksi'], model='additive', period=12)\n",
        "results.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "BuK5VqtkV064",
        "outputId": "edc8be4a-a5ca-4c58-8a54-e8c523440dd1"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Seed Untuk Reproduksi Data\n",
        "# Set random seeds di semua library\n",
        "seed_value = 42\n",
        "random.seed(seed_value)\n",
        "np.random.seed(seed_value)\n",
        "tf.random.set_seed(seed_value)"
      ],
      "metadata": {
        "id": "OGF2sq0XWQ6K"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NoWcZEK6rPb"
      },
      "source": [
        "# Split data into training and testing sets\n",
        "train = df.iloc[:54].copy()\n",
        "test = df.iloc[54:].copy()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prepocessing Data"
      ],
      "metadata": {
        "id": "86lxwp6PWwNS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepocessing Data with MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaled_train = scaler.fit_transform(train[['Produksi']])\n",
        "scaled_test = scaler.transform(test[['Produksi']])\n",
        "\n",
        "print(\"=== Data Test ===\")\n",
        "print(\"Train Original:\", train['Produksi'].head(3).values)\n",
        "print(\"Train Scaled:\", scaled_train[:3].flatten())"
      ],
      "metadata": {
        "id": "xdvMcAEm8b4q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f127e7a-309e-4972-978f-ca43cdfebd4f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Data Test ===\n",
            "Train Original: [170252 323468 616344]\n",
            "Train Scaled: [0.12375276 0.36158455 0.81620564]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model LSTM"
      ],
      "metadata": {
        "id": "NTKpVM7VXIQG"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqyTKCVt69Z9"
      },
      "source": [
        "# define generator\n",
        "n_input = 12\n",
        "n_features = 1\n",
        "generator = TimeseriesGenerator(scaled_train, scaled_train, length=n_input, batch_size=1)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ip6y5Ene7OM7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95500389-0c6a-465d-f25e-bad3f7760430"
      },
      "source": [
        "X,y = generator[0]\n",
        "print(f'Given the Array: \\n{X.flatten()}')\n",
        "print(f'Predict this y: \\n {y}')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Given the Array: \n",
            "[0.12375276 0.36158455 0.81620564 0.64107913 0.29078265 0.02133433\n",
            " 0.55775977 0.45020645 0.2241284  0.13337059 0.51362578 0.33075036]\n",
            "Predict this y: \n",
            " [[0.2472944]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQyGmvsi8HyR",
        "outputId": "bbc40c20-3517-427d-fdaa-c191b747655c"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 12, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jm-T4H4I7UaV"
      },
      "source": [
        "# We do the same thing, but now instead for 1 months\n",
        "n_input = 12\n",
        "n_features = 1\n",
        "generator = TimeseriesGenerator(scaled_train, scaled_train, length=n_input, batch_size=1)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJa-wFQf7jw6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b00c43a-a3f9-45ce-d436-6dc6c0f74e5c"
      },
      "source": [
        "# Set seeds for reproducibility\n",
        "np.random.seed(42)\n",
        "random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# define model\n",
        "model = Sequential()\n",
        "model.add(LSTM(100, activation='relu', input_shape=(n_input, n_features)))\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer='adam', loss='mse')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "7i4ucUNv7lbk",
        "outputId": "68adcd33-71a8-435c-df87-12e0b4034e27"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m40,800\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m101\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">40,800</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">101</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m40,901\u001b[0m (159.77 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">40,901</span> (159.77 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m40,901\u001b[0m (159.77 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">40,901</span> (159.77 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8yqUZaz7nwp",
        "outputId": "d380c005-1ad7-4671-cc21-a2981ca082c8"
      },
      "source": [
        "# fit model\n",
        "history = model.fit(generator,epochs=50)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.1512\n",
            "Epoch 2/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0735\n",
            "Epoch 3/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0713\n",
            "Epoch 4/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0660\n",
            "Epoch 5/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0571\n",
            "Epoch 6/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0612\n",
            "Epoch 7/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0733\n",
            "Epoch 8/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0543\n",
            "Epoch 9/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0566\n",
            "Epoch 10/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - loss: 0.0715\n",
            "Epoch 11/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0583\n",
            "Epoch 12/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0450\n",
            "Epoch 13/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0625\n",
            "Epoch 14/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0816\n",
            "Epoch 15/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0673\n",
            "Epoch 16/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0733\n",
            "Epoch 17/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0709\n",
            "Epoch 18/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.0461\n",
            "Epoch 19/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0550\n",
            "Epoch 20/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0603\n",
            "Epoch 21/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0574\n",
            "Epoch 22/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0430\n",
            "Epoch 23/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0410\n",
            "Epoch 24/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0490\n",
            "Epoch 25/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0509\n",
            "Epoch 26/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0584\n",
            "Epoch 27/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0448\n",
            "Epoch 28/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - loss: 0.0535\n",
            "Epoch 29/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0522\n",
            "Epoch 30/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - loss: 0.0549\n",
            "Epoch 31/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0458\n",
            "Epoch 32/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0458\n",
            "Epoch 33/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0415\n",
            "Epoch 34/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0357\n",
            "Epoch 35/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0378\n",
            "Epoch 36/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0458\n",
            "Epoch 37/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0401\n",
            "Epoch 38/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0474\n",
            "Epoch 39/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0525\n",
            "Epoch 40/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0488\n",
            "Epoch 41/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0357\n",
            "Epoch 42/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0416\n",
            "Epoch 43/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0412\n",
            "Epoch 44/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0291\n",
            "Epoch 45/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0439\n",
            "Epoch 46/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0609\n",
            "Epoch 47/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0342\n",
            "Epoch 48/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0386\n",
            "Epoch 49/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0511\n",
            "Epoch 50/50\n",
            "\u001b[1m42/42\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0444\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "OjDsOKR67rs8",
        "outputId": "28abb4d4-f706-4e44-8e9c-27ba8e85e37f"
      },
      "source": [
        "loss_per_epoch = model.history.history['loss']\n",
        "plt.plot(range(len(loss_per_epoch)),loss_per_epoch)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x78dc18320fd0>]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6h-fell70_S"
      },
      "source": [
        "last_train_batch = scaled_train[-12:]"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwwv8O-p79bN"
      },
      "source": [
        "last_train_batch = last_train_batch.reshape((1, n_input, n_features))"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-4WD4t78MpJ",
        "outputId": "b331cfb7-2b89-4b61-e6d5-f3c4ea886192"
      },
      "source": [
        "model.predict(last_train_batch)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 216ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.525494]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbojFADE8StA",
        "outputId": "75736424-f111-45ec-87e3-239f52641131"
      },
      "source": [
        "scaled_test[0]"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.55599019])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MP-5Wez8jbY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6c76674-f0ea-4765-8740-f8681ccb8a84"
      },
      "source": [
        "test_predictions = []\n",
        "\n",
        "first_eval_batch = scaled_train[-n_input:]\n",
        "current_batch = first_eval_batch.reshape((1, n_input, n_features))\n",
        "\n",
        "for i in range(len(test)):\n",
        "\n",
        "    # get the prediction value for the first batch\n",
        "    current_pred = model.predict(current_batch)[0]\n",
        "\n",
        "    # append the prediction into the array\n",
        "    test_predictions.append(current_pred)\n",
        "\n",
        "    # use the prediction to update the batch and remove the first value\n",
        "    current_batch = np.append(current_batch[:,1:,:],[[current_pred]],axis=1)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mhvFuEy8sB7",
        "outputId": "45dcbf42-57af-4329-9243-7afce3152bd4"
      },
      "source": [
        "test_predictions"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([0.525494], dtype=float32),\n",
              " array([0.418258], dtype=float32),\n",
              " array([0.32469228], dtype=float32),\n",
              " array([0.49720806], dtype=float32),\n",
              " array([0.92161345], dtype=float32),\n",
              " array([1.0146182], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "YYSqV10E9hlO",
        "outputId": "e030544a-dd8a-4510-b11f-c262ce5ff29c"
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    Nomer  Tahun   Bulan  Produksi\n",
              "54     55   2023  Jul-23    448708\n",
              "55     56   2023  Aug-23    412512\n",
              "56     57   2023  Sep-23    299914\n",
              "57     58   2023  Oct-23    368213\n",
              "58     59   2023  Nov-23    523068"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Nomer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 55,\n        \"max\": 60,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          55,\n          56,\n          60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tahun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2023,\n        \"max\": 2023,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2023\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bulan\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Jul-23\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Produksi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 135373,\n        \"min\": 299914,\n        \"max\": 686476,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          448708\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHHzjNDK9EJZ"
      },
      "source": [
        "true_predictions = scaler.inverse_transform(test_predictions)\n",
        "test['Predictions'] = true_predictions"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the absolute difference between 'Produksi' and 'Predictions'\n",
        "test['Error'] = abs((test['Produksi'] - test['Predictions'])/test['Produksi'])*100\n",
        "\n",
        "# Display the updated DataFrame with the new 'Error' column\n",
        "test\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "qX-ypp5gqywe",
        "outputId": "c01c2e05-0ff2-433b-8a75-cb5dae276568"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Nomer  Tahun   Bulan  Produksi    Predictions      Error\n",
              "54     55   2023  Jul-23    448708  429061.731444   4.378408\n",
              "55     56   2023  Aug-23    412512  359978.176066  12.735102\n",
              "56     57   2023  Sep-23    299914  299701.260042   0.070934\n",
              "57     58   2023  Oct-23    368213  410839.375662  11.576554\n",
              "58     59   2023  Nov-23    523068  684249.819863  30.814697\n",
              "59     60   2023  Dec-23    686476  744165.309966   8.403689"
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-94473b98-6d79-41af-b4cd-0c91d3eab539')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-94473b98-6d79-41af-b4cd-0c91d3eab539 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-94473b98-6d79-41af-b4cd-0c91d3eab539');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-4332fafa-7fe3-4fd8-8886-f8a2291d9b35\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4332fafa-7fe3-4fd8-8886-f8a2291d9b35')\"\n",
              "                title=\"Suggest charts\"\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",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-4332fafa-7fe3-4fd8-8886-f8a2291d9b35 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_efe9bea0-91ba-4da8-946f-83270634978e\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_efe9bea0-91ba-4da8-946f-83270634978e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Nomer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 55,\n        \"max\": 60,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          55,\n          56,\n          60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tahun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2023,\n        \"max\": 2023,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2023\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bulan\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Jul-23\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Produksi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 135373,\n        \"min\": 299914,\n        \"max\": 686476,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          448708\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predictions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181883.55733544833,\n        \"min\": 299701.26004207134,\n        \"max\": 744165.3099660873,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          429061.731443882\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Error\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.63337216039579,\n        \"min\": 0.07093365362359128,\n        \"max\": 30.814697106938176,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.378408353788657\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ... (Your existing plotting code) ...\n",
        "\n",
        "# Calculate MAPE (assuming you already have y_true and y_pred)\n",
        "def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
        "\n",
        "y_true = test['Produksi'].values\n",
        "y_pred = test['Predictions'].values\n",
        "\n",
        "mape_final = mean_absolute_percentage_error(y_true, y_pred) #Calculate and assign mape_final\n",
        "\n",
        "# ... (Rest of your plotting code) ...\n",
        "\n",
        "mape_text = f\"MAPE: {mape_final:.2f}%\" #Now mape_final is defined\n",
        "\n",
        "plt.annotate(mape_text,\n",
        "             xy=(0.05, 0.95),\n",
        "             xycoords='axes fraction',\n",
        "             fontsize=12,\n",
        "             bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
        "\n",
        "\n",
        "plt.figure(figsize=(14, 7))\n",
        "\n",
        "# Plot data aktual\n",
        "plt.plot(test.index, test['Produksi'],\n",
        "         label='Data Aktual',\n",
        "         color='blue',\n",
        "         marker='o',\n",
        "         markersize=5,\n",
        "         linewidth=2,\n",
        "         alpha=0.7)\n",
        "\n",
        "# Plot prediksi\n",
        "plt.plot(test.index, test['Predictions'],\n",
        "         label='Prediksi Model',\n",
        "         color='red',\n",
        "         linestyle='--',\n",
        "         marker='x',\n",
        "         markersize=6,\n",
        "         linewidth=2)\n",
        "\n",
        "# Tambahkan detail grafik\n",
        "plt.title('Perbandingan Data Aktual vs Prediksi Model', fontsize=16, pad=20)\n",
        "plt.xlabel('Tanggal', fontsize=12)\n",
        "plt.ylabel('Produksi', fontsize=12)\n",
        "plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
        "plt.legend(fontsize=12)\n",
        "\n",
        "# Rotasi label tanggal jika terlalu padat\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "# Tambahkan anotasi MAPE\n",
        "mape_text = f\"MAPE: {mape_final:.2f}%\"\n",
        "plt.annotate(mape_text,\n",
        "             xy=(0.05, 0.95),\n",
        "             xycoords='axes fraction',\n",
        "             fontsize=12,\n",
        "             bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
        "\n",
        "# Highlight area error\n",
        "plt.fill_between(test.index,\n",
        "                 test['Produksi'],\n",
        "                 test['Predictions'],\n",
        "                 color='orange',\n",
        "                 alpha=0.1,\n",
        "                 label='Error')\n",
        "\n",
        "plt.tight_layout()  # Adjust layout\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "fjvREAUvjxQw",
        "outputId": "0230b000-b4ad-45a6-8ef1-b31d54410979"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCsYjzge9OVe",
        "outputId": "f0303ec7-cdac-4e7a-a39e-e3b92f556f57"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "rmse=sqrt(mean_squared_error(test['Produksi'],test['Predictions']))\n",
        "print(f\"RMSE Model LSTM: {rmse:.2f}\")"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE Model LSTM: 75576.08\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
        "\n",
        "# Gunakan nilai sebenarnya dari data test dan prediksi model:\n",
        "y_true = test['Produksi'].values\n",
        "y_pred = test['Predictions'].values\n",
        "\n",
        "mape = mean_absolute_percentage_error(y_true, y_pred)\n",
        "print(f\"MAPE Model LSTM: {mape:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0y8tGeJgLFR8",
        "outputId": "8230eb57-bd02-489f-e72f-1ac016179efc"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAPE Model LSTM: 11.33%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: evaluas mae-nya juga\n",
        "\n",
        "# Evaluasi MAE (Mean Absolute Error)\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "mae = mean_absolute_error(y_true, y_pred)\n",
        "print(f\"MAE Model LSTM: {mae:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMHlVmMH-vyM",
        "outputId": "cc985046-546a-4cae-ec5d-f7ffc6751d60"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE Model LSTM: 55648.39\n"
          ]
        }
      ]
    }
  ]
}