{
  "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": 54,
      "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": 55,
      "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": "e22b033d-d9d7-4083-b5f8-84942f5df966"
      },
      "execution_count": 56,
      "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": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ip6y5Ene7OM7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d495766f-4d12-435d-8a83-732253c38126"
      },
      "source": [
        "X,y = generator[0]\n",
        "print(f'Given the Array: \\n{X.flatten()}')\n",
        "print(f'Predict this y: \\n {y}')"
      ],
      "execution_count": 58,
      "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": "d3cefe04-0cdd-4b70-8ff1-5f31999f45a2"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 12, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jm-T4H4I7UaV"
      },
      "source": [
        "# We do the same thing, but now instead for 6 months\n",
        "n_input = 6\n",
        "n_features = 1\n",
        "generator = TimeseriesGenerator(scaled_train, scaled_train, length=n_input, batch_size=1)"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJa-wFQf7jw6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6edd9d2a-0b2e-4e74-856c-b874964af46a"
      },
      "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": 61,
      "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": "bafa04f9-f538-4f11-aba2-2ba64ca4262e"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_2\"\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_2\"</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_2 (\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_2 (\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_2 (<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_2 (<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": "c033186d-7a6e-427c-f810-927f7749ed26"
      },
      "source": [
        "# fit model\n",
        "history = model.fit(generator,epochs=50)"
      ],
      "execution_count": 63,
      "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[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - loss: 0.1260\n",
            "Epoch 2/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.1071\n",
            "Epoch 3/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - loss: 0.0690\n",
            "Epoch 4/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0634\n",
            "Epoch 5/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0750\n",
            "Epoch 6/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0738\n",
            "Epoch 7/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0610\n",
            "Epoch 8/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0578\n",
            "Epoch 9/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0766\n",
            "Epoch 10/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0874\n",
            "Epoch 11/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0648\n",
            "Epoch 12/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 0.0534\n",
            "Epoch 13/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0512\n",
            "Epoch 14/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0632\n",
            "Epoch 15/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0566\n",
            "Epoch 16/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0530\n",
            "Epoch 17/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0610\n",
            "Epoch 18/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 0.0775\n",
            "Epoch 19/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 0.0547\n",
            "Epoch 20/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0698\n",
            "Epoch 21/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0590\n",
            "Epoch 22/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0584\n",
            "Epoch 23/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0457\n",
            "Epoch 24/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0549\n",
            "Epoch 25/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 0.0552\n",
            "Epoch 26/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0489\n",
            "Epoch 27/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 0.0579\n",
            "Epoch 28/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.0476\n",
            "Epoch 29/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0549\n",
            "Epoch 30/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0630\n",
            "Epoch 31/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0581\n",
            "Epoch 32/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0636\n",
            "Epoch 33/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0484\n",
            "Epoch 34/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0536\n",
            "Epoch 35/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0420\n",
            "Epoch 36/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0661\n",
            "Epoch 37/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0671\n",
            "Epoch 38/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0568\n",
            "Epoch 39/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0653\n",
            "Epoch 40/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0614\n",
            "Epoch 41/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0591\n",
            "Epoch 42/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0600\n",
            "Epoch 43/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 0.0577\n",
            "Epoch 44/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0606\n",
            "Epoch 45/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0484\n",
            "Epoch 46/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0443\n",
            "Epoch 47/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0536\n",
            "Epoch 48/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0601\n",
            "Epoch 49/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0433\n",
            "Epoch 50/50\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0531\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "OjDsOKR67rs8",
        "outputId": "fb5d2037-5f20-420c-99d8-58543af231d0"
      },
      "source": [
        "loss_per_epoch = model.history.history['loss']\n",
        "plt.plot(range(len(loss_per_epoch)),loss_per_epoch)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x78dc1c5fa850>]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "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[-6:]"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwwv8O-p79bN"
      },
      "source": [
        "last_train_batch = last_train_batch.reshape((1, n_input, n_features))"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-4WD4t78MpJ",
        "outputId": "aa6dc987-8fb4-4260-ae4e-a088022ef030"
      },
      "source": [
        "model.predict(last_train_batch)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 221ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.50715786]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbojFADE8StA",
        "outputId": "0054a66d-bc05-4c4a-eedc-08378486d468"
      },
      "source": [
        "scaled_test[0]"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.55599019])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MP-5Wez8jbY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c73b5f0b-402b-4466-9ab8-d844ba2df6ea"
      },
      "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": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/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 45ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mhvFuEy8sB7",
        "outputId": "a8db5a05-e405-45b0-d744-821dedb541ff"
      },
      "source": [
        "test_predictions"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([0.50715786], dtype=float32),\n",
              " array([0.4894229], dtype=float32),\n",
              " array([0.5376978], dtype=float32),\n",
              " array([0.53069186], dtype=float32),\n",
              " array([0.44583556], dtype=float32),\n",
              " array([0.42191714], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YYSqV10E9hlO",
        "outputId": "c354d55e-100a-4382-f084-828ac6121deb"
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 71,
      "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": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHHzjNDK9EJZ"
      },
      "source": [
        "true_predictions = scaler.inverse_transform(test_predictions)\n",
        "test['Predictions'] = true_predictions"
      ],
      "execution_count": 72,
      "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": "c342ae3b-e36d-4e72-f5ae-2f5a74959a54"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Nomer  Tahun   Bulan  Produksi    Predictions      Error\n",
              "54     55   2023  Jul-23    448708  417249.237978   7.010965\n",
              "55     56   2023  Aug-23    412512  405824.012626   1.621283\n",
              "56     57   2023  Sep-23    299914  436923.671597  45.682986\n",
              "57     58   2023  Oct-23    368213  432410.311406  17.434830\n",
              "58     59   2023  Nov-23    523068  377744.184824  27.782968\n",
              "59     60   2023  Dec-23    686476  362335.460243  47.218044"
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              "        const element = document.querySelector('#df-1e35fe9e-7ef8-47f3-8227-a4c35f881be2');\n",
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              "          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",
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              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-44b9426f-ce6e-4858-ae1a-c42336f6d2fa\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-44b9426f-ce6e-4858-ae1a-c42336f6d2fa')\"\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",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-44b9426f-ce6e-4858-ae1a-c42336f6d2fa button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_7f7a4921-9913-4c95-bf4a-aee70f485f99\">\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_7f7a4921-9913-4c95-bf4a-aee70f485f99 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\": 29944.216336802205,\n        \"min\": 362335.46024250984,\n        \"max\": 436923.6715965271,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          417249.23797774315\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Error\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.260397874240432,\n        \"min\": 1.6212831078149674,\n        \"max\": 47.21804400408611,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          7.01096526521855\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "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": "33b840df-a5f3-4cfe-ffd0-92c640039ee6"
      },
      "execution_count": 74,
      "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": "3770976b-e266-45b4-f535-a6f1772c34ae"
      },
      "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": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE Model LSTM: 158173.65\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": "35db0776-a60b-4817-81b1-369750850cff"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAPE Model LSTM: 24.46%\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": "652d0261-bc9e-4b04-cb9f-482aa8fc69ef"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE Model LSTM: 118136.35\n"
          ]
        }
      ]
    }
  ]
}