Mencari Keuntungan Tertinggi.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mencari Keuntungan Tertinggi"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sebagai perhatian, seluruh kode ini dijalankan menggunakan backend Python 3.10 dan Scikit Learn 1.3.0."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soal"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sebuah perusahaan memiliki 2 produk yakni Produk A dan Produk B. \n",
    "\n",
    "Berikut keuntungan yang didapat dari tiap produk tersebut:\n",
    "- Keuntungan Produk A = Rp. 3000\n",
    "- Keuntungan Produk B = Rp. 5000\n",
    "\n",
    "Perusahaan tersebut memiliki 3 Pabrik dengan kapasitas produksi sebagai berikut:\n",
    "| Pabrik | Waktu Produksi<br>Produk A (Jam) | Waktu Produksi<br>Produk B (Jam) | Total Waktu Produksi<br>yang Tersedia (Jam) |\n",
    "| --- | --- | --- | --- |\n",
    "| 1 | 1 | 0 | 4 |\n",
    "| 2 | 0 | 2 | 12 |\n",
    "| 3 | 3 | 2 | 18 |\n",
    "\n",
    "Sebagai Data Scientist, bantulah perusahaan dalam menentukan berapa jumlah produksi optimal untuk produk A dan produk B yang menghasilkan keuntungan paling tinggi."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Jawab\n",
    "---"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diketahui batasan masing-masing pabrik adalah sebagai berikut:\n",
    "\n",
    "1. x ≤ 4 (Pabrik 1 hanya dapat memproduksi Produk A hingga 4 unit)\n",
    "2. 2y ≤ 12 atau y ≤ 6 (Pabrik 2 hanya dapat memproduksi Produk B hingga 6 unit)\n",
    "3. 3x + 2y ≤ 18 (Pabrik 3 dapat memproduksi Produk A dalam waktu 3 jam dan Produk B dalam waktu 2 jam)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fungsi utama adalah untuk menghitung keuntungan paling tinggi dari produk A dan B:\n",
    "\n",
    "Profit = 3000x + 5000y"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Terdapat dua solusi dalam menyelesaikan masalah ini, yaitu dengan mengabaikan atau mengikutsertakan Pabrik 1 dan Pabrik 2."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Solusi 1: Mengabaikan Pabrik 1 dan Pabrik 2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jika mengabaikan Pabrik 1 dan Pabrik 2, maka batasannya adalah:\n",
    "\n",
    "3x + 2y ≤ 18\n",
    "\n",
    "x ≥ 0\n",
    "\n",
    "y ≥ 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 400)\n",
    "\n",
    "# Menghitung y berdasarkan batasan \n",
    "y = (18 - 3*x) / 2  # 3x + 2y <= 18 maka y <= (18 - 3x) / 2\n",
    "\n",
    "# Plot batasan\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.grid(True)\n",
    "plt.plot(x, y, label=r'$3x + 2y \\leq 18$')\n",
    "plt.axhline(0, color='black',linewidth=0.5)\n",
    "plt.axvline(0, color='black',linewidth=0.5)\n",
    "plt.xlim((0, 10))\n",
    "plt.ylim((0, 10))\n",
    "plt.xticks(np.arange(0, 11, 1))\n",
    "plt.yticks(np.arange(0, 11, 1))\n",
    "plt.xlabel(r'$x$ (Product A)')\n",
    "plt.ylabel(r'$y$ (Product B)')\n",
    "\n",
    "# Mengisi daerah feasible\n",
    "plt.fill_between(x, 0, y, where=(y>=0), color='gray', alpha=0.5)\n",
    "\n",
    "for i in range(7):\n",
    "    for j in range(10):\n",
    "        if 3*i + 2*j <= 18:\n",
    "            plt.scatter(i, j, color='red')\n",
    "\n",
    "plt.legend()\n",
    "plt.title('Feasible Area')\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setelah mengetahui titik-titik feasible, maka dapat dicari keuntungan maksimum dari produk A dan B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Produksi optimal untuk memaksimalkan profit adalah:\n",
      "Produk A (x): 0\n",
      "Produk B (y): 9\n",
      "Profit maksimum (Rp): 45000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Produk A (x)</th>\n",
       "      <th>Produk B (y)</th>\n",
       "      <th>Keuntungan (Rp)</th>\n",
       "      <th>Sisa Waktu (Jam)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>45000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>40000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>38000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>36000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>35000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>33000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>31000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>30000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>29000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>28000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>26000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>25000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>24000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>23000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>22000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>21000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>20000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>18000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>18000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>16000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>15000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13000</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10000</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8000</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5000</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Produk A (x)  Produk B (y)  Keuntungan (Rp)  Sisa Waktu (Jam)\n",
       "0              0             9            45000                 0\n",
       "1              0             8            40000                 2\n",
       "2              1             7            38000                 1\n",
       "3              2             6            36000                 0\n",
       "4              0             7            35000                 4\n",
       "5              1             6            33000                 3\n",
       "6              2             5            31000                 2\n",
       "7              0             6            30000                 6\n",
       "8              3             4            29000                 1\n",
       "9              1             5            28000                 5\n",
       "10             4             3            27000                 0\n",
       "11             2             4            26000                 4\n",
       "12             0             5            25000                 8\n",
       "13             3             3            24000                 3\n",
       "14             1             4            23000                 7\n",
       "15             4             2            22000                 2\n",
       "16             2             3            21000                 6\n",
       "17             0             4            20000                10\n",
       "18             5             1            20000                 1\n",
       "19             3             2            19000                 5\n",
       "20             1             3            18000                 9\n",
       "21             6             0            18000                 0\n",
       "22             4             1            17000                 4\n",
       "23             2             2            16000                 8\n",
       "24             5             0            15000                 3\n",
       "25             0             3            15000                12\n",
       "26             3             1            14000                 7\n",
       "27             1             2            13000                11\n",
       "28             4             0            12000                 6\n",
       "29             2             1            11000                10\n",
       "30             0             2            10000                14\n",
       "31             3             0             9000                 9\n",
       "32             1             1             8000                13\n",
       "33             2             0             6000                12\n",
       "34             0             1             5000                16\n",
       "35             1             0             3000                15\n",
       "36             0             0                0                18"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit_values = []\n",
    "coordinates = []\n",
    "\n",
    "for i in range(10):\n",
    "    for j in range(10):\n",
    "        if 3*i + 2*j <= 18:\n",
    "            profit = 3000*i + 5000*j\n",
    "            profit_values.append(profit)\n",
    "            coordinates.append((i,j))\n",
    "\n",
    "max_profit = max(profit_values)\n",
    "optimal_coordinates = coordinates[profit_values.index(max_profit)]\n",
    "print(\"Produksi optimal untuk memaksimalkan profit adalah:\")\n",
    "print(\"Produk A (x):\", optimal_coordinates[0])\n",
    "print(\"Produk B (y):\", optimal_coordinates[1])\n",
    "print(\"Profit maksimum (Rp):\", max_profit)\n",
    "\n",
    "df_profit = pd.DataFrame({\n",
    "    'Produk A (x)': [coord[0] for coord in coordinates],\n",
    "    'Produk B (y)': [coord[1] for coord in coordinates],\n",
    "    'Keuntungan (Rp)': profit_values,\n",
    "    'Sisa Waktu (Jam)': [18 - (3*coord[0] + 2*coord[1]) for coord in coordinates]\n",
    "})\n",
    "df_profit_sorted = df_profit.sort_values(by='Keuntungan (Rp)', ascending=False).reset_index(drop=True)\n",
    "df_profit_sorted"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dengan begitu, total keuntungan dari masing-masing pabrik adalah:\n",
    "\n",
    "Pabrik 1: 4 * Rp. 3000 + 0 * Rp. 5000 = Rp. 12.000\n",
    "\n",
    "Pabrik 2: 0 * Rp. 3000 + 6 * Rp. 5000 = Rp. 30.000\n",
    "\n",
    "Pabrik 3: 0 * Rp. 3000 + 9 * Rp. 5000 = Rp. 45.000\n",
    "\n",
    "Total keuntungan: Rp. 12.000 + Rp. 30.000 + Rp. 45.000 = Rp. 87.000"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Solusi 2: Mengikutsertakan Pabrik 1 dan Pabrik 2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jika mengikutsertakan Pabrik 1 dan Pabrik 2, maka batasannya bertambah menjadi:\n",
    "\n",
    "3x + 2y ≤ 18\n",
    "\n",
    "x ≤ 4\n",
    "\n",
    "y ≤ 6\n",
    "\n",
    "x ≥ 0\n",
    "\n",
    "y ≥ 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 400)\n",
    "\n",
    "# Menghitung y berdasarkan batasan \n",
    "y1 = 6 * np.ones_like(x)  # 2y <= 12 maka y <= 6\n",
    "y2 = (18 - 3*x) / 2       # 3x + 2y <= 18 maka y <= (18 - 3x) / 2\n",
    "\n",
    "# Plot batasan\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.grid(True)\n",
    "plt.plot(x, y1, label=r'$2y \\leq 12$')\n",
    "plt.plot(x, y2, label=r'$3x + 2y \\leq 18$')\n",
    "plt.axhline(0, color='black',linewidth=0.5)\n",
    "plt.axvline(0, color='black',linewidth=0.5)\n",
    "plt.axvline(x=4, color='red', label=r'$x \\leq 4$')\n",
    "plt.xlim((0, 10))\n",
    "plt.ylim((0, 10))\n",
    "plt.xticks(np.arange(0, 11, 1))\n",
    "plt.yticks(np.arange(0, 11, 1))\n",
    "plt.xlabel(r'$x$ (Product A)')\n",
    "plt.ylabel(r'$y$ (Product B)')\n",
    "\n",
    "# Mengisi daerah feasible\n",
    "plt.fill_between(x, 0, y1, where=(x<=4), color='gray', alpha=0.5)\n",
    "\n",
    "for i in range(7):\n",
    "    for j in range(10):\n",
    "        if 3*i + 2*j <= 18:\n",
    "            if i <= 4:\n",
    "                if j <= 6:\n",
    "                    plt.scatter(i, j, color='red')\n",
    "\n",
    "plt.legend()\n",
    "plt.title('Feasible Area')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Produksi optimal untuk memaksimalkan profit adalah:\n",
      "Produk A (x): 2\n",
      "Produk B (y): 6\n",
      "Profit maksimum (Rp): 36000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Produk A (x)</th>\n",
       "      <th>Produk B (y)</th>\n",
       "      <th>Keuntungan (Rp)</th>\n",
       "      <th>Sisa Waktu (Jam)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>36000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>33000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>31000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>30000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>29000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>28000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>26000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>25000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>24000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>23000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>22000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>21000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>20000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>18000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>17000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>16000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13000</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10000</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8000</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5000</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Produk A (x)  Produk B (y)  Keuntungan (Rp)  Sisa Waktu (Jam)\n",
       "0              2             6            36000                 0\n",
       "1              1             6            33000                 3\n",
       "2              2             5            31000                 2\n",
       "3              0             6            30000                 6\n",
       "4              3             4            29000                 1\n",
       "5              1             5            28000                 5\n",
       "6              4             3            27000                 0\n",
       "7              2             4            26000                 4\n",
       "8              0             5            25000                 8\n",
       "9              3             3            24000                 3\n",
       "10             1             4            23000                 7\n",
       "11             4             2            22000                 2\n",
       "12             2             3            21000                 6\n",
       "13             0             4            20000                10\n",
       "14             3             2            19000                 5\n",
       "15             1             3            18000                 9\n",
       "16             4             1            17000                 4\n",
       "17             2             2            16000                 8\n",
       "18             0             3            15000                12\n",
       "19             3             1            14000                 7\n",
       "20             1             2            13000                11\n",
       "21             4             0            12000                 6\n",
       "22             2             1            11000                10\n",
       "23             0             2            10000                14\n",
       "24             3             0             9000                 9\n",
       "25             1             1             8000                13\n",
       "26             2             0             6000                12\n",
       "27             0             1             5000                16\n",
       "28             1             0             3000                15\n",
       "29             0             0                0                18"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit_values = []\n",
    "coordinates = []\n",
    "\n",
    "for i in range(10):\n",
    "    for j in range(10):\n",
    "        if 3*i + 2*j <= 18:\n",
    "            if i <= 4:\n",
    "                if j <= 6:\n",
    "                    profit = 3000*i + 5000*j\n",
    "                    profit_values.append(profit)\n",
    "                    coordinates.append((i,j))\n",
    "\n",
    "max_profit = max(profit_values)\n",
    "optimal_coordinates = coordinates[profit_values.index(max_profit)]\n",
    "print(\"Produksi optimal untuk memaksimalkan profit adalah:\")\n",
    "print(\"Produk A (x):\", optimal_coordinates[0])\n",
    "print(\"Produk B (y):\", optimal_coordinates[1])\n",
    "print(\"Profit maksimum (Rp):\", max_profit)\n",
    "\n",
    "df_profit = pd.DataFrame({\n",
    "    'Produk A (x)': [coord[0] for coord in coordinates],\n",
    "    'Produk B (y)': [coord[1] for coord in coordinates],\n",
    "    'Keuntungan (Rp)': profit_values,\n",
    "    'Sisa Waktu (Jam)': [18 - (3*coord[0] + 2*coord[1]) for coord in coordinates]\n",
    "})\n",
    "df_profit_sorted = df_profit.sort_values(by='Keuntungan (Rp)', ascending=False).reset_index(drop=True)\n",
    "df_profit_sorted"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dengan begitu, total keuntungan dari masing-masing pabrik adalah:\n",
    "\n",
    "Pabrik 1: 4 * Rp. 3000 + 0 * Rp. 5000 = Rp. 12.000\n",
    "\n",
    "Pabrik 2: 0 * Rp. 3000 + 6 * Rp. 5000 = Rp. 30.000\n",
    "\n",
    "Pabrik 3: 2 * Rp. 3000 + 6 * Rp. 5000 = Rp. 36.000\n",
    "\n",
    "Total keuntungan: Rp. 12.000 + Rp. 30.000 + Rp. 36.000 = Rp. 78.000"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Kesimpulan"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Walaupun Solusi 1 memberikan keuntungan yang lebih tinggi, Solusi 2 lebih optimal karena mempertimbangkan batasan produksi dari Pabrik 1 dan Pabrik 2. \n",
    "\n",
    "Dalam mencari solusi keseluruhan, penting untuk mempertimbangkan keseluruhan batasan, bukan hanya untuk satu pabrik. \n",
    "\n",
    "Oleh karena itu, Solusi 2 merupakan pilihan yang paling sesuai dan optimal.\n"
   ]
  }
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