{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3aab7a28-a70c-4f44-84ce-8ad518cf107d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e2a90a58-2686-474e-9941-9afeaa1f2e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id,provinsi,kode_kabupaten_kota,nama_kabupaten_kota,jumlah_produksi,satuan,tahun</th>\n",
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       "      <td>0,JAWA BARAT,3201,KABUPATEN BOGOR,294050,KUINT...</td>\n",
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       "      <td>2,JAWA BARAT,3203,KABUPATEN CIANJUR,942580,KUI...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3,JAWA BARAT,3204,KABUPATEN BANDUNG,428480,KUI...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4,JAWA BARAT,3205,KABUPATEN GARUT,1128400,KUIN...</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>157,JAWA BARAT,3275,KOTA BEKASI,81980,KUINTAL,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>158,JAWA BARAT,3276,KOTA DEPOK,30020,KUINTAL,2018</td>\n",
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       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>159,JAWA BARAT,3277,KOTA CIMAHI,1050,KUINTAL,2018</td>\n",
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       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>160,JAWA BARAT,3278,KOTA TASIKMALAYA,44780,KUI...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>161,JAWA BARAT,3279,KOTA BANJAR,52370,KUINTAL,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id,provinsi,kode_kabupaten_kota,nama_kabupaten_kota,jumlah_produksi,satuan,tahun\n",
       "0    0,JAWA BARAT,3201,KABUPATEN BOGOR,294050,KUINT...                              \n",
       "1    1,JAWA BARAT,3202,KABUPATEN SUKABUMI,735710,KU...                              \n",
       "2    2,JAWA BARAT,3203,KABUPATEN CIANJUR,942580,KUI...                              \n",
       "3    3,JAWA BARAT,3204,KABUPATEN BANDUNG,428480,KUI...                              \n",
       "4    4,JAWA BARAT,3205,KABUPATEN GARUT,1128400,KUIN...                              \n",
       "..                                                 ...                              \n",
       "157  157,JAWA BARAT,3275,KOTA BEKASI,81980,KUINTAL,...                              \n",
       "158  158,JAWA BARAT,3276,KOTA DEPOK,30020,KUINTAL,2018                              \n",
       "159  159,JAWA BARAT,3277,KOTA CIMAHI,1050,KUINTAL,2018                              \n",
       "160  160,JAWA BARAT,3278,KOTA TASIKMALAYA,44780,KUI...                              \n",
       "161  161,JAWA BARAT,3279,KOTA BANJAR,52370,KUINTAL,...                              \n",
       "\n",
       "[162 rows x 1 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('Produksi Mangga Jawa Barat.csv', sep=';')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a46cb460-82a6-47ec-ba45-2b93fe29d192",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>provinsi</th>\n",
       "      <th>kode_kabupaten_kota</th>\n",
       "      <th>nama_kabupaten_kota</th>\n",
       "      <th>jumlah_produksi</th>\n",
       "      <th>satuan</th>\n",
       "      <th>tahun</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>JAWA BARAT</td>\n",
       "      <td>3201</td>\n",
       "      <td>KABUPATEN BOGOR</td>\n",
       "      <td>294050</td>\n",
       "      <td>KUINTAL</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>JAWA BARAT</td>\n",
       "      <td>3202</td>\n",
       "      <td>KABUPATEN SUKABUMI</td>\n",
       "      <td>735710</td>\n",
       "      <td>KUINTAL</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>JAWA BARAT</td>\n",
       "      <td>3203</td>\n",
       "      <td>KABUPATEN CIANJUR</td>\n",
       "      <td>942580</td>\n",
       "      <td>KUINTAL</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>JAWA BARAT</td>\n",
       "      <td>3204</td>\n",
       "      <td>KABUPATEN BANDUNG</td>\n",
       "      <td>428480</td>\n",
       "      <td>KUINTAL</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>JAWA BARAT</td>\n",
       "      <td>3205</td>\n",
       "      <td>KABUPATEN GARUT</td>\n",
       "      <td>1128400</td>\n",
       "      <td>KUINTAL</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    provinsi  kode_kabupaten_kota nama_kabupaten_kota  jumlah_produksi  \\\n",
       "0   0  JAWA BARAT                 3201     KABUPATEN BOGOR           294050   \n",
       "1   1  JAWA BARAT                 3202  KABUPATEN SUKABUMI           735710   \n",
       "2   2  JAWA BARAT                 3203   KABUPATEN CIANJUR           942580   \n",
       "3   3  JAWA BARAT                 3204   KABUPATEN BANDUNG           428480   \n",
       "4   4  JAWA BARAT                 3205     KABUPATEN GARUT          1128400   \n",
       "\n",
       "    satuan  tahun  \n",
       "0  KUINTAL   2013  \n",
       "1  KUINTAL   2013  \n",
       "2  KUINTAL   2013  \n",
       "3  KUINTAL   2013  \n",
       "4  KUINTAL   2013  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('Produksi Mangga Jawa Barat.csv', sep=',')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0088e8c4-f362-4cd3-b3d8-7dee3415959d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tahun</th>\n",
       "      <th>jumlah_produksi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
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       "      <td>2013</td>\n",
       "      <td>735710</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>942580</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>428480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>1128400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tahun  jumlah_produksi\n",
       "0   2013           294050\n",
       "1   2013           735710\n",
       "2   2013           942580\n",
       "3   2013           428480\n",
       "4   2013          1128400"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset[['tahun', 'jumlah_produksi']]\n",
    "dataset = dataset.dropna()\n",
    "\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f825b98-638f-4f8b-bc92-95fb77ff0e81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tahun</th>\n",
       "      <th>jumlah_produksi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>32707010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>32148180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>31022550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>26010640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>32545720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>40454210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tahun  jumlah_produksi\n",
       "0   2013         32707010\n",
       "1   2014         32148180\n",
       "2   2015         31022550\n",
       "3   2016         26010640\n",
       "4   2017         32545720\n",
       "5   2018         40454210"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_tahun = dataset.groupby('tahun').sum().reset_index()\n",
    "dataset_tahun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c4126101-e61d-4d80-8643-65583dd44533",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KuNHFfgWeHQFr0KABtmzZgtTUVNjb2yMsLAxubm7YtGmTyme9uM8qUPzfi67qLVTc31NhW+HnoGrVqvD29i7x81qjRg2V568axK+usLAwDB06FN98841K+/3791G5cuVSrfNl3xXPH9Gk8u+1PnLzKsOHD8fx48cRERGBc+fO4d1330X37t1x7dq1YvuvXr36pf/aplfr3LkzHj58WOTLecOGDcrXgWfTywFg48aNKv1++eWXIut0dXVFamqqyg/306dPsW/fvhLrmDlzJpYsWYJZs2YhMDCwxH6VK1fGO++8g3HjxiE9PV3t2VElcXJywqeffoo+ffogICCgxH6FF0h8ftDikydP8L///a/U792+fXscPHhQJfQVFBRgy5YtKv0KByBv2rRJpX3r1q0qs2MK63xx4Oq5c+dw8uTJUtVY+MP4/DqFEMqZbM9zdHSEp6cntm3bhtOnTyvDjZ+fH+7du4fFixfDxsYGzZs3V1m/tvcr8GyA8fnz5yGXy5VHK2UyGczMzFR+7FNSUoqdLVUSTep98UidOg4cOKDyd5Ofn49NmzbB3d1deVSzd+/euHDhAtzd3eHj41Pk8WK40ZbiPlu//fZbmS4W6urqinPnzqm0Xb16lTOgKqDX+sjNy1y/fh3h4eG4deuW8o9z2rRpiIyMRGhoaJF/LeTk5GDjxo344osvDFGuZAwdOhQ//vgjAgICkJiYiIYNG+LYsWP45ptv0LNnT+W/srt27Yp27drhs88+w6NHj+Dj44Pjx48X+6U+cOBAzJo1C++99x4+/fRTZGdnY9myZcjPz39pLZMmTYKVlRU+/vhjPHz4EMuWLYNMJkOfPn2U1/GpVq0a/v33XyxZsgQ1a9Ys9cyP582fP/+VfXr16oXFixfj/fffx8cff4y0tDR89913as2AKcmMGTOwe/dudO7cGTNmzICFhQVCQkLw6NEjAM+OPgBAgwYNMGjQICxatAjGxsbo1KkTLl68iEWLFkGhUCj7Ac9++L766ivMnj0b7du3x5UrVzB37ly4ubkVCUIlef7H38/PD2ZmZhg0aBA+++wzZGdnY+XKlfjvv/+KXbZz58744YcfYGFhoTwt6ubmBjc3N/zxxx/w9/dXGSekrf16+vRp5fTvu3fvYu3atbh8+TImT56sPMLSu3dvbN++HWPHjsU777yDmzdv4quvvoKjo2OJ/4B6kSb1NmzYEBEREdi0aRNq1aoFc3NzNGzY8KXrr1q1Kjp16oQvv/xSOVvq8uXLKtPB586di6ioKLRu3RoTJ05E3bp1kZ2djcTEROzduxchISHFnt4tq969e2PdunWoV68evL29cfr0aXz77bdleq8hQ4Zg8ODBGDt2LPr3749///0XCxcu1OiaVVROGHpEc3kBQOzYsUP5vHDUe6VKlVQeJiYmYsCAAUWW/+WXX4SJiYlITk7WY9Xlz/Ozf14mICBAVKpUqdjX0tLSxOjRo4Wjo6MwMTERNWvWFIGBgSI7O1ul34MHD8SIESNE5cqVhaWlpfDz8xOXL18uMltKCCH27t0rGjduLCwsLEStWrXE8uXLXzpb6nnh4eHCxMREDB8+XOTn54tFixaJ1q1bi6pVqwozMzPxxhtviJEjR4rExMQi+0GT2VIvU9xMl7Vr14q6desKuVwuatWqJYKDg8WaNWuKvG/NmjVFr169iqzzxRkgQghx9OhR0bJlSyGXy4WDg4P49NNPlTPVHjx4oOyXnZ0tpkyZIuzt7YW5ublo1aqVOHnypFAoFCozVXJycsS0adOEk5OTMDc3F02bNhU7d+4scQbbi5o3by6aNWum0rZ7927RqFEjYW5uLpycnMSnn36qnD136NAhlb6//vqrACD8/PxU2j/66CMBQCxbtqzIe6q7X4tT3GwpW1tb0bJlS7F27VqRn5+v0n/+/PnC1dVVyOVyUb9+fbFq1Sq1P5ea1puYmCi6du0qrK2tBYBX7v/C91yxYoVwd3cXpqamol69emLjxo1F+t67d09MnDhRuLm5CVNTU2FrayuaNWsmZsyYoZyJps6Mp5IUt+x///0nRo4cKezt7YWlpaVo27atOHr0aIkzm7Zs2VLsOkNDQ5VtBQUFYuHChaJWrVrC3Nxc+Pj4iIMHD5ZpnWQYMiHKcGUqCZHJZNixYwf69esH4Nkh9w8++AAXL14scr0CKyurIgPtOnfuDBsbmyKzIYgquq5duyIxMbHYCyQ+78SJE2jTpg02btyolVlrubm5cHR0RKdOnbB58+Yyr480U3jBw9JOuCAyJJ6WKkGTJk2Qn5+P1NTUV46hSUhIwKFDh7Br1y49VUekG1OmTEGTJk3g4uKC9PR0bNy4EVFRUVizZo1Kv6ioKJw8eRLNmjWDhYUFzp49i/nz56N27dp4++23y1RDZmYmYmNjsXHjRqSlpZV4uw4iopK81uHm4cOHyivnAs9CSnx8PGxtbVGnTh188MEHGDp0KBYtWoQmTZrg/v37OHjwIBo2bIiePXsql1u7di0cHR21enVcIkPIz8/HrFmzkJKSAplMBk9PT/zvf//D4MGDVfrZ2Njgjz/+wJIlS5CVlYWqVauiR48eCA4OVpm1UxpnzpxBt27d4OrqimXLlqFv375lWh8RvX5e69NShw8fVs66eV5AQADWrVuH3NxczJs3Dxs2bMDt27dhZ2cHX19fBAUFKQfiFRQUoGbNmhg6dOhLp+4SERGRfrzW4YaIiIikh9e5ISIiIklhuCEiIiJJee0GFBcUFODOnTuwtrbW2mXAiYiISLeEEMjKykKNGjVULhZanNcu3Ny5cwcuLi6GLoOIiIhK4ebNm6+8EvVrF26sra0BPNs5L96RmoiIiMqnzMxMuLi4KH/HX+a1CzeFp6JsbGwYboiIiCoYdYaUcEAxERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJymt3hWIiIiLSjfwCgb8S0pGalQ17a3O0cLOFsZH+b1LNcENERERlFnkhGUG7LyE5I1vZ5qgwx+w+nuju5ajXWnhaioiIiMok8kIyxoSdUQk2AJCSkY0xYWcQeSFZr/Uw3BAREVGp5RcIBO2+BFHMa4VtQbsvIb+guB66wXBDREREpfZXQnqRIzbPEwCSM7LxV0K63mpiuCEiIqJSS80qOdiUpp82MNwQERFRqdlbm2u1nzYw3BAREVGptXCzhaPCHCVN+Jbh2aypFm62equJ4YaIiIhKzdhIhtl9PAGgSMApfD67j6der3fDcENERERl0t3LESsHN4WDQvXUk4PCHCsHN9X7dW54ET8iIiIqs+5ejvDzdCgXVyguN0dugoODIZPJ8Mknn7y0X3R0NJo1awZzc3PUqlULISEh+imQiIiIXsrYSAZfdzv0bewEX3c7gwQboJyEm9jYWPz888/w9vZ+ab+EhAT07NkTb775JuLi4jB9+nRMnDgR27Zt01OlREREVN4ZPNw8fPgQH3zwAVatWoUqVaq8tG9ISAjeeOMNLFmyBPXr18eHH36IESNG4LvvvtNTtURERFTeGTzcjBs3Dr169UKXLl1e2ffkyZPo2rWrSlu3bt1w6tQp5ObmFrtMTk4OMjMzVR5EREQkXQYNNxEREThz5gyCg4PV6p+SkoLq1aurtFWvXh15eXm4f/9+scsEBwdDoVAoHy4uLmWum4iIiMovg4WbmzdvYtKkSQgLC4O5ufpXLZTJVAcnCSGKbS8UGBiIjIwM5ePmzZulL5qIiIjKPYNNBT99+jRSU1PRrFkzZVt+fj6OHDmC5cuXIycnB8bGxirLODg4ICUlRaUtNTUVJiYmsLOzK/Z95HI55HK59jeAiIiIyiWDhZvOnTvj/PnzKm3Dhw9HvXr18PnnnxcJNgDg6+uL3bt3q7T98ccf8PHxgampqU7rJSIioorBYOHG2toaXl5eKm2VKlWCnZ2dsj0wMBC3b9/Ghg0bAACjR4/G8uXLMWXKFHz00Uc4efIk1qxZg/DwcL3XT0REROWTwWdLvUxycjKSkpKUz93c3LB3714cPnwYjRs3xldffYVly5ahf//+BqySiIiIyhOZKByR+5rIzMyEQqFARkYGbGxsDF0OERERqUGT3+9yfeSGiIiISFMMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQYNNytXroS3tzdsbGxgY2MDX19f/P777y9dZuPGjWjUqBEsLS3h6OiI4cOHIy0tTU8VExERUXln0HDj7OyM+fPn49SpUzh16hQ6deqEvn374uLFi8X2P3bsGIYOHYqRI0fi4sWL2LJlC2JjY/Hhhx/quXIiIiIqr0wM+eZ9+vRRef71119j5cqViImJQYMGDYr0j4mJgaurKyZOnAgAcHNzw6hRo7Bw4UK91EtERETlX7kZc5Ofn4+IiAg8evQIvr6+xfZp3bo1bt26hb1790IIgbt372Lr1q3o1atXievNyclBZmamyoOIiIiky+Dh5vz587CysoJcLsfo0aOxY8cOeHp6Ftu3devW2LhxIwYOHAgzMzM4ODigcuXK+OGHH0pcf3BwMBQKhfLh4uKiq00hIiKickAmhBCGLODp06dISkrCgwcPsG3bNqxevRrR0dHFBpxLly6hS5cumDx5Mrp164bk5GR8+umnaN68OdasWVPs+nNycpCTk6N8npmZCRcXF2RkZMDGxkZn20VERETak5mZCYVCodbvt8HDzYu6dOkCd3d3/PTTT0VeGzJkCLKzs7FlyxZl27Fjx/Dmm2/izp07cHR0fOX6Ndk5REREVD5o8vtt8NNSLxJCqBxped7jx49hZKRasrGxsXI5IiIiIoPOlpo+fTp69OgBFxcXZGVlISIiAocPH0ZkZCQAIDAwELdv38aGDRsAPJtd9dFHH2HlypXK01KffPIJWrRogRo1ahhyU4iIiKicMGi4uXv3LoYMGYLk5GQoFAp4e3sjMjISfn5+AIDk5GQkJSUp+w8bNgxZWVlYvnw5pk6disqVK6NTp05YsGCBoTaBiIiIyplyN+ZG1zjmhoiIqOKp0GNuiIiIiMqC4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCTFRNMF8vPz8f3332Pz5s1ISkrC06dPVV5PT0/XWnFEREREmtL4yE1QUBAWL16MAQMGICMjA1OmTMHbb78NIyMjzJkzRwclEhEREalP43CzceNGrFq1CtOmTYOJiQkGDRqE1atXY9asWYiJidFFjURERERq0zjcpKSkoGHDhgAAKysrZGRkAAB69+6N3377TbvVEREREWlI43Dj7OyM5ORkAICHhwf++OMPAEBsbCzkcrl2qyMiIiLSkMbh5q233sKBAwcAAJMmTcKXX36J2rVrY+jQoRgxYoTWCyQiIiLShEwIIcqygpiYGJw4cQIeHh7w9/fXVl06k5mZCYVCgYyMDNjY2Bi6HCIiIlKDJr/fZb7OTatWrTBlypRSBZuVK1fC29sbNjY2sLGxga+vL37//feXLpOTk4MZM2agZs2akMvlcHd3x9q1a0tbPhEREUmMxte52bVrV7HtMpkM5ubm8PDwgJubm1rrcnZ2xvz58+Hh4QEAWL9+Pfr27Yu4uDg0aNCg2GUGDBiAu3fvYs2aNfDw8EBqairy8vI03QwiIiKSKI1PSxkZGUEmk+HFxQrbZDIZ2rZti507d6JKlSoaF2Rra4tvv/0WI0eOLPJaZGQk3nvvPdy4cQO2trYarxvgaSkiIqKKSKenpaKiotC8eXNERUUhIyMDGRkZiIqKQosWLbBnzx4cOXIEaWlpmDZtmkbrzc/PR0REBB49egRfX99i++zatQs+Pj5YuHAhnJycUKdOHUybNg1PnjzRdDOIiIhIojQ+LTVp0iT8/PPPaN26tbKtc+fOMDc3x8cff4yLFy9iyZIlas+cOn/+PHx9fZGdnQ0rKyvs2LEDnp6exfa9ceMGjh07BnNzc+zYsQP379/H2LFjkZ6eXuK4m5ycHOTk5CifZ2ZmarC1REREVNFofOTm+vXrxR4OsrGxwY0bNwAAtWvXxv3799VaX926dREfH4+YmBiMGTMGAQEBuHTpUrF9CwoKIJPJsHHjRrRo0QI9e/bE4sWLsW7duhKP3gQHB0OhUCgfLi4uam4pERERVUQah5tmzZrh008/xb1795Rt9+7dw2effYbmzZsDAK5duwZnZ2e11mdmZgYPDw/4+PggODgYjRo1wtKlS4vt6+joCCcnJygUCmVb/fr1IYTArVu3il0mMDBQefosIyMDN2/eVHdTiYiIqALSONysWbMGCQkJcHZ2hoeHB2rXrg1nZ2ckJiZi9erVAICHDx/iyy+/LFVBQgiV00jPa9OmDe7cuYOHDx8q265evQojI6MSw5RcLldONS98EBERkXSV6iJ+Qgjs27cPV69ehRAC9erVg5+fH4yMNMtK06dPR48ePeDi4oKsrCxERERg/vz5iIyMhJ+fHwIDA3H79m1s2LABwLPQVL9+fbRq1QpBQUG4f/8+PvzwQ7Rv3x6rVq1S6z05W4qIiKji0eT3W+MBxcCzad/du3dH9+7dS1Vgobt372LIkCFITk6GQqGAt7e3MtgAQHJyMpKSkpT9raysEBUVhQkTJsDHxwd2dnYYMGAA5s2bV6Y6iIiISDo0PnKzbNmy4lf03EX82rVrB2NjY60UqG08ckNERFTx6PTIzffff4979+7h8ePHqFKlCoQQePDgASwtLWFlZYXU1FTUqlULhw4d4swkIiIi0juNBxR/8803aN68Oa5du4a0tDSkp6fj6tWraNmyJZYuXYqkpCQ4ODhg8uTJuqiXiIiI6KU0Pi3l7u6Obdu2oXHjxirtcXFx6N+/P27cuIETJ06gf//+SE5O1matWsHTUkRERBWPTm+/kJycXOyNKvPy8pCSkgIAqFGjBrKysjRdNREREVGZaRxuOnbsiFGjRiEuLk7ZFhcXhzFjxqBTp04Ant1SQd07gxMRERFpU6ku4mdra4tmzZpBLpdDLpfDx8cHtra2WLNmDYBnU7YXLVqk9WKJiIiIXqVUF/EDgMuXL6tcxK9u3brark0nOOaGiIio4tH5RfwAoF69eqhXr15pFyciIiLSCY3DTX5+PtatW4cDBw4gNTUVBQUFKq8fPHhQa8URERERaUrjcDNp0iSsW7cOvXr1gpeXF2QymS7qIiIiIioVjcNNREQENm/ejJ49e+qiHiIiIqIy0Xi2lJmZGTw8PHRRCxEREVGZaRxupk6diqVLl6KUk6yIiIiIdErj01LHjh3DoUOH8Pvvv6NBgwYwNTVVeX379u1aK46IiIhIUxqHm8qVK+Ott97SRS1EREREZaZxuAkNDdVFHURERERaofGYGyIiIqLyrFRXKN66dSs2b96MpKQkPH36VOW1M2fOaKUwIiIiotLQ+MjNsmXLMHz4cNjb2yMuLg4tWrSAnZ0dbty4gR49euiiRiIiIiK1aRxuVqxYgZ9//hnLly+HmZkZPvvsM0RFRWHixInIyMjQRY1EREREatM43CQlJaF169YAAAsLC2RlZQEAhgwZgvDwcO1WR0RERKQhjcONg4MD0tLSAAA1a9ZETEwMACAhIYEX9iMiIiKD0zjcdOrUCbt37wYAjBw5EpMnT4afnx8GDhzI698QERGRwcmEhodbCgoKUFBQABOTZxOtNm/ejGPHjsHDwwOjR4+GmZmZTgrVlszMTCgUCmRkZMDGxsbQ5RAREZEaNPn91jjcVHQMN0RERBWPJr/fal/nJikpSa1+b7zxhrqrJCIiItI6tcONm5ub8r8LD/bIZDKVNplMhvz8fC2WR0RERKQZtcONTCaDs7Mzhg0bhj59+ijH3BARERGVJ2onlFu3bmH9+vVYt24dQkJCMHjwYIwcORL169fXZX1EREREGlF7KriDgwM+//xz/P3339i6dSv+++8/tGzZEq1atcKqVatQUFCgyzqJiIiI1FKqu4K3bdsWa9aswbVr12BpaYnRo0fjwYMHWi6NiIiISHOlCjcnTpzAhx9+iDp16uDhw4f48ccfUblyZS2XRkRERKQ5tcfcJCcnY8OGDQgNDcV///2HDz74ACdOnECDBg10WR8RERGRRtQONzVr1kSNGjUQEBAAf39/mJqaIj8/H+fOnVPp5+3trfUiiYiIiNSl9hWKjYz+/xmswuvbvLhoRbjODa9QTEREVPHo5ArFCQkJZS6MiIiISNc0Oi2lbStXrsTKlSuRmJgIAGjQoAFmzZqFHj16vHLZ48ePo3379vDy8kJ8fLzWayMiIqKKqVSzpbTF2dkZ8+fPx6lTp3Dq1Cl06tQJffv2xcWLF1+6XEZGBoYOHYrOnTvrqVIiIiKqKMrdXcFtbW3x7bffYuTIkSX2ee+991C7dm0YGxtj586dGh254ZgbIiKiikeT32+DHrl5Xn5+PiIiIvDo0SP4+vqW2C80NBTXr1/H7Nmz1VpvTk4OMjMzVR5EREQkXQa/++X58+fh6+uL7OxsWFlZYceOHfD09Cy277Vr1/DFF1/g6NGjat+4Mzg4GEFBQdosmYiIiMoxgx+5qVu3LuLj4xETE4MxY8YgICAAly5dKtIvPz8f77//PoKCglCnTh211x8YGIiMjAzl4+bNm9osn4iIiMoZtcbcNG3aFAcOHECVKlXQpEkT5XVuinPmzJkyFdSlSxe4u7vjp59+Uml/8OABqlSpAmNjY2VbQUEBhBAwNjbGH3/8gU6dOr1y/RxzQ0REVPFo/To3ffv2hVwuBwD069evzAW+jBACOTk5RdptbGxw/vx5lbYVK1bg4MGD2Lp1K9zc3HRaFxEREVUMaoWb5wfvqjuQVx3Tp09Hjx494OLigqysLERERODw4cOIjIwE8OyU0u3bt7FhwwYYGRnBy8tLZXl7e3uYm5sXaSciIqLXl8YDim/evAmZTAZnZ2cAwF9//YVffvkFnp6e+PjjjzVa1927dzFkyBAkJydDoVDA29sbkZGR8PPzA/DsZp1JSUmalkhERESvMY2vc/Pmm2/i448/xpAhQ5CSkoI6derAy8sLV69excSJEzFr1ixd1aoVHHNDRERU8ej0OjcXLlxAixYtAACbN29Gw4YNceLECfzyyy9Yt25dqQomIiIi0haNw01ubq5ycPH+/fvh7+8PAKhXrx6Sk5O1Wx0RERGRhjQONw0aNEBISAiOHj2KqKgodO/eHQBw584d2NnZab1AIiIiIk1oHG4WLFiAn376CR06dMCgQYPQqFEjAMCuXbuUp6uIiIiIDKVUN87Mz89HZmYmqlSpomxLTEyEpaUl7O3ttVqgtnFAMRERUcWj0wHFYWFhMDY2Vgk2AODq6opvv/1W09URERERaZXG4Wb8+PHYs2dPkfbJkycjLCxMK0URERERlZbG4SYiIgKDBw/GkSNHlG0TJkzA5s2bcejQIa0WR0RERKQpjcNN9+7dERISgn79+uHUqVMYO3Ystm/fjkOHDqFevXq6qJGIiIhIbRrffgEA3nvvPfz3339o27YtqlWrhujoaHh4eGi7NiIiIiKNqRVupkyZUmy7vb09mjRpghUrVijbFi9erJ3KiIiIiEpBrXATFxdXbLu7uzsyMzOVr8tkMu1VRkRERFQKaoUbDhQmIiKiikLjAcVERERE5ZnGA4o7duz40tNPBw8eLFNBRERERGWhcbhp3LixyvPc3FzEx8fjwoULCAgI0FZdRERERKWicbj5/vvvi22fM2cOHj58WOaCiIiIiMpCa2NuBg8ejLVr12prdURERESlUqqL+BXn5MmTMDc319bqKpz8AoG/EtKRmpUNe2tztHCzhbERp8YTERHpm8bh5u2331Z5LoRAcnIyTp06hS+//FJrhVUkkReSEbT7EpIzspVtjgpzzO7jie5ejgasjIiI6PWjcbhRKBQqz42MjFC3bl3MnTsXXbt21VphFUXkhWSMCTsD8UJ7SkY2xoSdwcrBTRlwiIiI9EjjcBMaGqqLOiqk/AKBoN2XigQbABAAZACCdl+Cn6cDT1ERERHpCS/iVwZ/JaSrnIp6kQCQnJGNvxLS9VcUEREVkV8gcPJ6Gn6Nv42T19OQX1DcP0tJKtQ6clOlShW17xuVnv76/JCnZpUcbErTj4iItI/jIl8/aoWbJUuWKP87LS0N8+bNQ7du3eDr6wvg2Uypffv2vXYDiu2t1Zsdpm4/IiLSLo6LfD3JhBAaHZvr378/OnbsiPHjx6u0L1++HPv378fOnTu1WZ/WZWZmQqFQICMjAzY2NmVaV36BQNsFB5GSkV3suBsZAAeFOY593oljboiI9KzwO7qk4QP8jq5YNPn91njMzb59+9C9e/ci7d26dcP+/fs1XV2FZmwkw+w+ngCe/ZE8r/D57D6e/KMhIjIAjot8fWkcbuzs7LBjx44i7Tt37oSdnZ1WiqpIuns5YuXgpnBQqJ56clCY83AnEZEBcVzk60vjqeBBQUEYOXIkDh8+rBxzExMTg8jISKxevVrrBVYE3b0c4efpwCsUExGVIxwX+frSONwMGzYM9evXx7Jly7B9+3YIIeDp6Ynjx4+jZcuWuqixQjA2ksHX/fU7ckVEVF61cLOFo8L8leMiW7jZ6rs00jGNBxRXdNocUExEROVb4WwpACoBp/C4OocPVBya/H6X6saZ+fn52LlzJ/7++2/IZDJ4enrC398fxsbGpSqYSF28QSkRaaJwXOSL17lx4HVuJE3jcPPPP/+gZ8+euH37NurWrQshBK5evQoXFxf89ttvcHd310WdRLwQFxGVCsdFvn40Pi3Vs2dPCCGwceNG2No+O0+ZlpaGwYMHw8jICL/99ptOCtUWnpaqmEq6EBcPLRMRvR40+f3WONxUqlQJMTExaNiwoUr72bNn0aZNGzx8+FDzivWI4abi4YW4iIhIpxfxk8vlyMrKKtL+8OFDmJmZabSulStXwtvbGzY2NrCxsYGvry9+//33Evtv374dfn5+qFatmrL/vn37NN0EqmB4IS4iItKExuGmd+/e+Pjjj/Hnn39CCAEhBGJiYjB69Gj4+/trtC5nZ2fMnz8fp06dwqlTp9CpUyf07dsXFy9eLLb/kSNH4Ofnh7179+L06dPo2LEj+vTpg7i4OE03gyoQXoiLiIg0ofFpqQcPHiAgIAC7d++GqakpACAvLw/+/v5Yt24dFApFmQqytbXFt99+i5EjR6rVv0GDBhg4cCBmzZqlVn+elqp4Tl5Pw6BVMa/sF/5RK15riIhIonQ2FVwIgYyMDISHh+POnTv4+++/lRfx8/DwKFPR+fn52LJlCx49eqS88vGrFBQUICsrSzmwmaSJF+IiIiJNaBxuateujYsXL6J27dplDjQAcP78efj6+iI7OxtWVlbYsWMHPD091Vp20aJFePToEQYMGFBin5ycHOTk5CifZ2Zmlrlm0q/CG5SOCTsDGYq/EBdvUEpERIU0GnNjZGSE2rVrIy0tTWsF1K1bF/Hx8YiJicGYMWMQEBCAS5cuvXK58PBwzJkzB5s2bYK9vX2J/YKDg6FQKJQPFxcXrdVO+sMblBIRkbo0HnPz22+/Yf78+Vi5ciW8vLy0XlCXLl3g7u6On376qcQ+mzZtwvDhw7Flyxb06tXrpesr7siNi4sLx9xUULxCMRHR60mnt18YPHgwHj9+jEaNGsHMzAwWFhYqr6enl206rhBCJYy8KDw8HCNGjEB4ePgrgw3wbOq6XC4vU01UfvAGpURE9Coah5vvv/8eMpl2/qU8ffp09OjRAy4uLsjKykJERAQOHz6MyMhIAEBgYCBu376NDRs2AHgWbIYOHYqlS5eiVatWSElJAQBYWFiUeZYWERERSYPG4WbQoEHIy8tDpUqVyvzmd+/exZAhQ5CcnAyFQgFvb29ERkbCz88PAJCcnIykpCRl/59++gl5eXkYN24cxo0bp2wPCAjAunXrylwPERERVXxqj7m5f/8+AgIC8Mcff6CgoAAtW7ZEWFgYatWqpesatYrXuSEiIqp4dHL7hcDAQJw+fRpBQUH49ttvcf/+fYwaNarMxRIRERFpk9qnpfbt24e1a9eiZ8+eAJ7dHdzLywu5ubnKKxUTERERGZraR27u3LmDJk2aKJ/Xq1cPZmZmuHPnjk4KIyIiIioNtcONEAImJqoHekxMTFBQUKD1ooiIiIhKS+3TUkIIdO7cWSXgPH78GH369IGZmZmy7cyZM9qtkIiIiEgDaoeb2bNnF2nr27evVoshIiIiKiuNb79Q0XEqOBERUcWjk6ngRERERBUBww0RERFJCsMNERERSQrDDREREUmKxjfOJCLpyy8Q+CshHalZ2bC3NkcLN1sYG8kMXRYRkVrUCjfLli1Te4UTJ04sdTFEZHiRF5IRtPsSkjOylW2OCnPM7uOJ7l6OBqyMiEg9ak0Fd3NzU29lMhlu3LhR5qJ0iVPBiUoWeSEZY8LO4MUvhcJjNisHN2XAISKD0OT3W60jNwkJCVopjIjKr/wCgaDdl4oEGwAQeBZwgnZfgp+nA09REVG5xgHFRAQA+CshXeVU1IsEgOSMbPyVkK6/ooiISqFUA4pv3bqFXbt2ISkpCU+fPlV5bfHixVopjIj0KzWr5GBTmn5ERIaicbg5cOAA/P394ebmhitXrsDLywuJiYkQQqBp06a6qJGI9MDe2lyr/YiIDEXj01KBgYGYOnUqLly4AHNzc2zbtg03b95E+/bt8e677+qiRiLSgxZutnBUmKOk0TQyPJs11cLNVp9lERFpTONw8/fffyMgIAAAYGJigidPnsDKygpz587FggULtF4gEemHsZEMs/t4AkCRgFP4fHYfTw4mJqJyT+NwU6lSJeTk5AAAatSogevXrytfu3//vvYqIyK96+7liJWDm8JBoXrqyUFhzmngRFRhaDzmplWrVjh+/Dg8PT3Rq1cvTJ06FefPn8f27dvRqlUrXdRIRHrU3csRfp4OvEIxEVVYGoebxYsX4+HDhwCAOXPm4OHDh9i0aRM8PDzw/fffa71AItI/YyMZfN3tDF0GEVGpqHWFYinhFYqJiIgqHk1+vzUec1OrVi2kpaUVaX/w4AFq1aql6eqIiIiItErjcJOYmIj8/Pwi7Tk5Obh9+7ZWiiIiIiIqLbXH3OzatUv53/v27YNCoVA+z8/Px4EDB+Dq6qrV4oiIiIg0pXa46devH4Bnd/4uvM5NIVNTU7i6umLRokVaLY6IiIhIU2qHm4KCAgCAm5sbYmNjUbVqVZ0VRURERFRaGk8FT0hI0EUdRERERFqh8YBiAIiOjkafPn3g4eGB2rVrw9/fH0ePHtV2bUREREQa0zjchIWFoUuXLrC0tMTEiRMxfvx4WFhYoHPnzvjll190USMRERGR2jS+iF/9+vXx8ccfY/LkySrtixcvxqpVq/D3339rtUBt40X8iIiIKh6dXsTvxo0b6NOnT5F2f39/jschIiIig9M43Li4uODAgQNF2g8cOAAXFxetFEVERERUWmrPlhoxYgSWLl2KqVOnYuLEiYiPj0fr1q0hk8lw7NgxrFu3DkuXLtVlrURERESvpPaRm/Xr1+PJkycYM2YMIiIicP78eXzyySeYNGkSLly4gE2bNmHUqFEavfnKlSvh7e0NGxsb2NjYwNfXF7///vtLl4mOjkazZs1gbm6OWrVqISQkRKP3JCIiImlT+8jN8+OO33rrLbz11ltlfnNnZ2fMnz8fHh4eAJ4FqL59+yIuLg4NGjQo0j8hIQE9e/bERx99hLCwMBw/fhxjx45FtWrV0L9//zLXQ0RERBWf2rOljIyMcPfuXVSrVk2nBdna2uLbb7/FyJEji7z2+eefY9euXSozskaPHo2zZ8/i5MmTaq2fs6WIiIgqHk1+vzW6QnGdOnUgk8le2ic9PV2TVSrl5+djy5YtePToEXx9fYvtc/LkSXTt2lWlrVu3blizZg1yc3NhampaZJmcnBzk5OQon2dmZpaqPiIiIqoYNAo3QUFBKncD14bz58/D19cX2dnZsLKywo4dO+Dp6Vls35SUFFSvXl2lrXr16sjLy8P9+/fh6OhYZJng4GAEBQVptWYiIiIqvzQKN++99x7s7e21WkDdunURHx+PBw8eYNu2bQgICEB0dHSJAefFI0eFZ9VKOqIUGBiIKVOmKJ9nZmZyyjoREZGEqR1uXnU6qrTMzMyUA4p9fHwQGxuLpUuX4qeffirS18HBASkpKSptqampMDExgZ2dXbHrl8vlkMvl2i+ciIiIyiW1p4JreJeGUhNCqIyReZ6vry+ioqJU2v744w/4+PgUO96GiIiIXj9qH7kpKCjQ+ptPnz4dPXr0gIuLC7KyshAREYHDhw8jMjISwLNTSrdv38aGDRsAPJsZtXz5ckyZMgUfffQRTp48iTVr1iA8PFzrtREREVHFpNGYG227e/cuhgwZguTkZCgUCnh7eyMyMhJ+fn4AgOTkZCQlJSn7u7m5Ye/evZg8eTJ+/PFH1KhRA8uWLeM1boiIiEhJ47uCV3S8zg0REVHFo9O7ghMRERGVZww3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkGDTfBwcFo3rw5rK2tYW9vj379+uHKlSuvXG7jxo1o1KgRLC0t4ejoiOHDhyMtLU0PFRMREVF5Z9BwEx0djXHjxiEmJgZRUVHIy8tD165d8ejRoxKXOXbsGIYOHYqRI0fi4sWL2LJlC2JjY/Hhhx/qsXIiIiIqr0wM+eaRkZEqz0NDQ2Fvb4/Tp0+jXbt2xS4TExMDV1dXTJw4EQDg5uaGUaNGYeHChTqvl4iIiMq/cjXmJiMjAwBga2tbYp/WrVvj1q1b2Lt3L4QQuHv3LrZu3YpevXrpq0wiIiIqx2RCCGHoIgBACIG+ffviv//+w9GjR1/ad+vWrRg+fDiys7ORl5cHf39/bN26FaampkX65uTkICcnR/k8MzMTLi4uyMjIgI2Njda3g4iIiLQvMzMTCoVCrd/vcnPkZvz48Th37hzCw8Nf2u/SpUuYOHEiZs2ahdOnTyMyMhIJCQkYPXp0sf2Dg4OhUCiUDxcXF12UT0REROVEuThyM2HCBOzcuRNHjhyBm5vbS/sOGTIE2dnZ2LJli7Lt2LFjePPNN3Hnzh04Ojqq9OeRGyIioopPkyM3Bh1QLITAhAkTsGPHDhw+fPiVwQYAHj9+DBMT1bKNjY2V63uRXC6HXC7XTsFERERU7hn0tNS4ceMQFhaGX375BdbW1khJSUFKSgqePHmi7BMYGIihQ4cqn/fp0wfbt2/HypUrcePGDRw/fhwTJ05EixYtUKNGDUNsBhEREZUjBj1ys3LlSgBAhw4dVNpDQ0MxbNgwAEBycjKSkpKUrw0bNgxZWVlYvnw5pk6disqVK6NTp05YsGCBvsomIiKicqxcjLnRJ03O2REREVH5UCFnSxERERFpA8MNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSYqJoQsgInpd5RcI/JWQjtSsbNhbm6OFmy2MjWSGLouowjPokZvg4GA0b94c1tbWsLe3R79+/XDlypVXLpeTk4MZM2agZs2akMvlcHd3x9q1a/VQMRGRdkReSEbbBQcxaFUMJkXEY9CqGLRdcBCRF5INXRpRhWfQcBMdHY1x48YhJiYGUVFRyMvLQ9euXfHo0aOXLjdgwAAcOHAAa9aswZUrVxAeHo569erpqWoiorKJvJCMMWFnkJyRrdKekpGNMWFnGHCIykgmhBCGLqLQvXv3YG9vj+joaLRr167YPpGRkXjvvfdw48YN2NraavwemZmZUCgUyMjIgI2NTVlLJiLSSH6BQNsFB4sEm0IyAA4Kcxz7vBNPURE9R5Pf73I1oDgjIwMAXhpadu3aBR8fHyxcuBBOTk6oU6cOpk2bhidPnhTbPycnB5mZmSoPIiJD+SshvcRgAwACQHJGNv5KSNdfUUQSU24GFAshMGXKFLRt2xZeXl4l9rtx4waOHTsGc3Nz7NixA/fv38fYsWORnp5e7Lib4OBgBAUF6bJ0IiK1pWaVHGxK04+Iiio3R27Gjx+Pc+fOITw8/KX9CgoKIJPJsHHjRrRo0QI9e/bE4sWLsW7dumKP3gQGBiIjI0P5uHnzpq42gYjoleytzbXaj4iKKhfhZsKECdi1axcOHToEZ2fnl/Z1dHSEk5MTFAqFsq1+/foQQuDWrVtF+svlctjY2Kg8iIgMpYWbLRwV5ihpNI0MgKPi2bRwIiodg4YbIQTGjx+P7du34+DBg3Bzc3vlMm3atMGdO3fw8OFDZdvVq1dhZGT0ymBERGRoxkYyzO7jCQBFAk7h89l9PDmYmKgMDBpuxo0bh7CwMPzyyy+wtrZGSkoKUlJSVE4vBQYGYujQocrn77//Puzs7DB8+HBcunQJR44cwaeffooRI0bAwsLCEJtBRKSR7l6OWDm4KRwUqqeeHBTmWDm4Kbp7ORqoMiJpMOhUcJms+H+ZhIaGYtiwYQCAYcOGITExEYcPH1a+fvnyZUyYMAHHjx+HnZ0dBgwYgHnz5qkVbjgVnIjKC16hmEh9mvx+l6vr3OgDww0REVHFU2Gvc0NERERUVgw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpJoYuQN8KL8icmZlp4EqIiIhIXYW/2+rcWOG1CzdZWVkAABcXFwNXQkRERJrKysqCQqF4aZ/X7t5SBQUFuHPnDqytrUu8cWdpZWZmwsXFBTdv3uR9q3SI+1k/uJ/1g/tZf7iv9UNX+1kIgaysLNSoUQNGRi8fVfPaHbkxMjKCs7OzTt/DxsaGfzh6wP2sH9zP+sH9rD/c1/qhi/38qiM2hTigmIiIiCSF4YaIiIgkheFGi+RyOWbPng25XG7oUiSN+1k/uJ/1g/tZf7iv9aM87OfXbkAxERERSRuP3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNw8Jzg4GM2bN4e1tTXs7e3Rr18/XLlyRaWPEAJz5sxBjRo1YGFhgQ4dOuDixYsqfX7++Wd06NABNjY2kMlkePDgQZH38vf3xxtvvAFzc3M4OjpiyJAhuHPnji43r9zQ534ulJOTg8aNG0MmkyE+Pl4HW1U+6XNfu7q6QiaTqTy++OILXW5euaHvz/Rvv/2Gli1bwsLCAlWrVsXbb7+tq00rV/S1nw8fPlzks1z4iI2N1fVmGpw+P89Xr15F3759UbVqVdjY2KBNmzY4dOhQmbeB4eY50dHRGDduHGJiYhAVFYW8vDx07doVjx49UvZZuHAhFi9ejOXLlyM2NhYODg7w8/NT3rMKAB4/fozu3btj+vTpJb5Xx44dsXnzZly5cgXbtm3D9evX8c477+h0+8oLfe7nQp999hlq1Kihk+0pz/S9r+fOnYvk5GTlY+bMmTrbtvJEn/t527ZtGDJkCIYPH46zZ8/i+PHjeP/993W6feWFvvZz69atVT7HycnJ+PDDD+Hq6gofHx+db6eh6fPz3KtXL+Tl5eHgwYM4ffo0GjdujN69eyMlJaVsGyGoRKmpqQKAiI6OFkIIUVBQIBwcHMT8+fOVfbKzs4VCoRAhISFFlj906JAAIP77779Xvtevv/4qZDKZePr0qdbqryh0vZ/37t0r6tWrJy5evCgAiLi4OF1sRoWgy31ds2ZN8f333+uq9ApFV/s5NzdXODk5idWrV+u0/opCX9/RT58+Ffb29mLu3Llarb+i0NV+vnfvngAgjhw5omzLzMwUAMT+/fvLVDOP3LxERkYGAMDW1hYAkJCQgJSUFHTt2lXZRy6Xo3379jhx4kSp3yc9PR0bN25E69atYWpqWraiKyBd7ue7d+/io48+wv/+9z9YWlpqr+gKStef6QULFsDOzg6NGzfG119/jadPn2qn8ApGV/v5zJkzuH37NoyMjNCkSRM4OjqiR48eRU4HvC709R29a9cu3L9/H8OGDStTvRWVrvaznZ0d6tevjw0bNuDRo0fIy8vDTz/9hOrVq6NZs2ZlqpnhpgRCCEyZMgVt27aFl5cXACgPk1WvXl2lb/Xq1Ut1CO3zzz9HpUqVYGdnh6SkJPz6669lL7yC0eV+FkJg2LBhGD169GtxKPlVdP2ZnjRpEiIiInDo0CGMHz8eS5YswdixY7VTfAWiy/1848YNAMCcOXMwc+ZM7NmzB1WqVEH79u2Rnp6upS2oGPTxHV1ozZo16NatG1xcXEpfcAWly/0sk8kQFRWFuLg4WFtbw9zcHN9//z0iIyNRuXLlMtX92t0VXF3jx4/HuXPncOzYsSKvyWQyledCiCJt6vj0008xcuRI/PvvvwgKCsLQoUOxZ8+eUq2rotLlfv7hhx+QmZmJwMDAMtcpBbr+TE+ePFn5397e3qhSpQreeecd5dGc14Uu93NBQQEAYMaMGejfvz8AIDQ0FM7OztiyZQtGjRpVhsorFn18RwPArVu3sG/fPmzevLlUy1d0utzPQgiMHTsW9vb2OHr0KCwsLLB69Wr07t0bsbGxcHR0LHXdPHJTjAkTJmDXrl04dOgQnJ2dle0ODg4AUCSZpqamFkmw6qhatSrq1KkDPz8/REREYO/evYiJiSlb8RWIrvfzwYMHERMTA7lcDhMTE3h4eAAAfHx8EBAQoIUtqDj09Zl+XqtWrQAA//zzT5nWU5Hoej8Xftl7enoq2+RyOWrVqoWkpKSylF6h6PPzHBoaCjs7O/j7+5e+4ApKH9/Re/bsQUREBNq0aYOmTZtixYoVsLCwwPr168tUO8PNc4QQGD9+PLZv346DBw/Czc1N5XU3Nzc4ODggKipK2fb06VNER0ejdevWZX5v4NmUZanT135etmwZzp49i/j4eMTHx2Pv3r0AgE2bNuHrr7/WzsaUc4b8TMfFxQFAmf71VVHoaz83a9YMcrlcZVpubm4uEhMTUbNmzbJvSDmn78+zEAKhoaEYOnToazUeUl/7+fHjxwAAIyPVKGJkZKQ8SllqZRqOLDFjxowRCoVCHD58WCQnJysfjx8/VvaZP3++UCgUYvv27eL8+fNi0KBBwtHRUWRmZir7JCcni7i4OLFq1SrlSPC4uDiRlpYmhBDizz//FD/88IOIi4sTiYmJ4uDBg6Jt27bC3d1dZGdn63279U1f+/lFCQkJr91sKX3t6xMnTojFixeLuLg4cePGDbFp0yZRo0YN4e/vr/dtNgR9fqYnTZoknJycxL59+8Tly5fFyJEjhb29vUhPT9frNhuCvr879u/fLwCIS5cu6W0bywN97ed79+4JOzs78fbbb4v4+Hhx5coVMW3aNGFqairi4+PLtA0MN88BUOwjNDRU2aegoEDMnj1bODg4CLlcLtq1ayfOnz+vsp7Zs2e/dD3nzp0THTt2FLa2tkIulwtXV1cxevRocevWLT1ureHoaz+/6HUMN/ra16dPnxYtW7YUCoVCmJubi7p164rZs2eLR48e6XFrDUefn+mnT5+KqVOnCnt7e2FtbS26dOkiLly4oKctNSx9f3cMGjRItG7dWg9bVr7ocz/HxsaKrl27CltbW2FtbS1atWol9u7dW+ZtkP2/DSEiIiKSBI65ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCGiCq1Dhw745JNPDF0GEZUjDDdEZFAymeylj2HDhhm6RCKqYEwMXQARvd6Sk5OV/71p0ybMmjVL5caQFhYWhiiLiCowHrkhIoNycHBQPhQKBWQymfK5qakpRo8eDWdnZ1haWqJhw4YIDw8vso6CggJ89tlnsLW1hYODA+bMmaN8LTExETKZDPHx8cq2Bw8eQCaT4fDhwwCAw4cPQyaT4cCBA/Dx8YGlpSVat26tErKIqOJguCGicis7OxvNmjXDnj17cOHCBXz88ccYMmQI/vzzT5V+69evR6VKlfDnn39i4cKFmDt3LqKiojR+vxkzZmDRokU4deoUTExMMGLECG1tChHpEU9LEVG55eTkhGnTpimfT5gwAZGRkdiyZQtatmypbPf29sbs2bMBALVr18by5ctx4MAB+Pn5afR+X3/9Ndq3bw8A+OKLL9CrVy9kZ2fD3NxcC1tDRPrCcENE5VZ+fj7mz5+PTZs24fbt28jJyUFOTg4qVaqk0s/b21vluaOjI1JTUzV+v+fX4+joCABITU3FG2+8UYrqichQGG6IqNxatGgRvv/+eyxZsgQNGzZEpUqV8Mknn+Dp06cq/UxNTVWey2QyFBQUAACMjJ6dfRdCKF/Pzc0t9v2eX49MJgMA5XqIqOLgmBsiKreOHj2Kvn37YvDgwWjUqBFq1aqFa9euabSOatWqAVCdlfX84GIikh4euSGicsvDwwPbtm3DiRMnUKVKFSxevBgpKSmoX7++2uuwsLBAq1atMH/+fLi6uuL+/fuYOXOmDqsmIkPjkRsiKre+/PJLNG3aFN26dUOHDh3g4OCAfv36abyetWvXIjc3Fz4+Ppg0aRLmzZun/WKJqNyQiedPRBMRERFVcDxyQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREkvJ/P/GAa/Blyb0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dataset_tahun['tahun'], dataset_tahun['jumlah_produksi'])\n",
    "plt.xlabel('Tahun')\n",
    "plt.ylabel('Total Produksi Mangga')\n",
    "plt.title('Produksi Mangga Jawa Barat per Tahun')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f2f440d9-baa4-44ba-b739-9fa21b060cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_X',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">copy_X&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">1e-06</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('positive',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">positive&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "X = dataset_tahun[['tahun']]\n",
    "y = dataset_tahun['jumlah_produksi']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4ad12315-6165-4c8f-948d-9d4f8154333a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 111586438761613.0\n",
      "R2: -1428.2624491148133\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(\"MSE:\", mean_squared_error(y_test, y_pred))\n",
    "print(\"R2:\", r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "756062e0-e3b1-4e52-bb08-979d2e7076a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, y)\n",
    "plt.plot(X, model.predict(X), linewidth=2)\n",
    "plt.xlabel('Tahun')\n",
    "plt.ylabel('Total Produksi Mangga')\n",
    "plt.title('Regresi Linear Produksi Mangga Jawa Barat')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b7684b63-ae94-494f-93b0-b815757d3cf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Koefisien: [3483006.]\n",
      "Intercept: -6990973318.999999\n"
     ]
    }
   ],
   "source": [
    "print(\"Koefisien:\", model.coef_)\n",
    "print(\"Intercept:\", model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65b26a8d-3ccf-441a-a78d-0fdc06e2a6c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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