ans/10219017/Answer_root_finding_graphical_method.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cca11c7c",
   "metadata": {},
   "source": [
    "# root_finding_graphical_method\n",
    "Jalankan kode pada sel-sel di bawah ini sehingga identitas Anda tertampilkan dengan benar dan perkirakan posisi akar dari grafik fungsi yang dibuat."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6366ac0c",
   "metadata": {},
   "source": [
    "## graphical method\n",
    "Ulas kembali secara singkat metode grafik dalam memperkirakan posisi akar suatu fungsi dengan menonton video berikut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bfbbc22f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"600\"\n",
       "            height=\"340\"\n",
       "            src=\"https://www.youtube.com/embed/9xRkoYbXOaU\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x159cd2983a0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('9xRkoYbXOaU', width=600, height=340)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b33b9bfe",
   "metadata": {},
   "source": [
    "Bila video tidak muncul dapat dilihat di https://www.youtube.com/watch?v=9xRkoYbXOaU"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "264a9720",
   "metadata": {},
   "source": [
    "## identity\n",
    "Koreksi kode pada sel berikut sehingga identitas Anda benar tertampilkan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0385abcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.display import HTML\n",
    "HTML(\"<script>Jupyter.notebook.kernel.restart()</script>\")\n",
    "\n",
    "import sys, os\n",
    "sys.path.insert(1, os.path.join(sys.path[0], '../..'))\n",
    "\n",
    "from src.student import Students as stu\n",
    "nim = 10219017\n",
    "print(nim, end=' ')\n",
    "print(stu[nim]['name'], end=' ')\n",
    "print(\"https://github.com/\", stu[nim]['github'], sep='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2006c61",
   "metadata": {},
   "source": [
    "<img src=\"Answer for Identity.jpeg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4d42d14",
   "metadata": {},
   "source": [
    "## function\n",
    "Sebuah fungsi matematika, misalnya saja\n",
    "\n",
    "<a name='eqn1'></a>\n",
    "\\begin{equation}\\tag{1}\n",
    "f(x) = \\left[ x - 2.4 e^{-(x - 2.7)} \\right] \\sin \\tfrac23 \\pi (x-1.35)\n",
    "\\end{equation}\n",
    "\n",
    "dapat digambarkan dengan dalam rentang $x \\in [x_{\\rm beg}, x_{\\rm end}]$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "91d18261",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "# define a function of x\n",
    "def f(x):\n",
    "    y1 = (x -  2.4 * math.exp(-(x - 2.7)))\n",
    "    y2 = math.sin(2 * math.pi * (x - 1.35) / 3)\n",
    "    y = y1 * y2\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "50160c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create data\n",
    "xbeg = 0\n",
    "xend = 10\n",
    "N = 100\n",
    "dx = (xend - xbeg) / N\n",
    "\n",
    "xx = []\n",
    "yy = []\n",
    "\n",
    "for i in range(0, N + 1):\n",
    "    x = xbeg + i * dx\n",
    "    y = f(x)\n",
    "    \n",
    "    xx.append(x)\n",
    "    yy.append(y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "86684f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot data\n",
    "plt.ion()\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_xlabel(\"$x$\")\n",
    "ax.set_ylabel(\"$y$\")\n",
    "plt.plot(xx, yy)\n",
    "\n",
    "# draw horizontal line\n",
    "line = plt.Line2D((xbeg, xend), (0, 0), color='#aaa', lw=1, ls='dashed')\n",
    "plt.gca().add_line(line)\n",
    "\n",
    "# show plot result\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b48cf370",
   "metadata": {},
   "source": [
    "## a root\n",
    "Dengan memilih rentang antara 5 dan 6, perkirakan posisi akarnya dan tunjukkan melalui penggambaran sebuah lingkaran pada posisi tersebut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "967cc9a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xroot = 5.5\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Ellipse\n",
    "\n",
    "# plot data\n",
    "plt.ion()\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_xlabel(\"$x$\")\n",
    "ax.set_ylabel(\"$y$\")\n",
    "plt.plot(xx, yy)\n",
    "\n",
    "# draw horizontal line\n",
    "line = plt.Line2D((xbeg, xend), (0, 0), color='#aaa', lw=1, ls='dashed')\n",
    "plt.gca().add_line(line)\n",
    "\n",
    "# zoom\n",
    "plt.xlim([3, 7])\n",
    "plt.ylim([-7, 7])\n",
    "\n",
    "\n",
    "# draw a cricle\n",
    "xroot = 5.5\n",
    "point = Ellipse(\n",
    "    xy=(xroot, 0), width=0.1, height=0.6, \n",
    "    edgecolor='r', fc='None', lw=1\n",
    ")\n",
    "plt.gca().add_patch(point)\n",
    "\n",
    "# show plot result\n",
    "plt.show()\n",
    "\n",
    "print(\"xroot = \", xroot, sep='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a0dc96a",
   "metadata": {},
   "source": [
    "Modifikasi kode di atas sehingga lingkaran merah tepat menunjukkan akar yang terletak antara 5 dan 6."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c66e91c6",
   "metadata": {},
   "source": [
    "## Jawaban \n",
    "\n",
    "Untuk mendapatkan nilai akar yang terletak antara 5 dan 6, perlu dilakukan zoom dengan `plt.xlim([3, 7])` menjadi `plt.xlim([5.7, 6])` agar koordinat akar antara 5 dan 6 lebih terlihat jelas. Hasil dari zoom menunjukkan akar antara 5 dan 6 bernilai 5.85 sehingga perlu modifikasi nilai akar `xroot = 5.85` dan merubah zoomnya ke semula yaitu `plt.xlim([3, 7])`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7fa96f36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xroot = 5.5\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Ellipse\n",
    "\n",
    "# plot data\n",
    "plt.ion()\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_xlabel(\"$x$\")\n",
    "ax.set_ylabel(\"$y$\")\n",
    "plt.plot(xx, yy)\n",
    "\n",
    "# draw horizontal line\n",
    "line = plt.Line2D((xbeg, xend), (0, 0), color='#aaa', lw=1, ls='dashed')\n",
    "plt.gca().add_line(line)\n",
    "\n",
    "# zoom\n",
    "plt.xlim([5.7, 6])\n",
    "plt.ylim([-7, 7])\n",
    "\n",
    "\n",
    "# draw a cricle\n",
    "xroot = 5.5\n",
    "point = Ellipse(\n",
    "    xy=(xroot, 0), width=0.1, height=0.6, \n",
    "    edgecolor='r', fc='None', lw=1\n",
    ")\n",
    "plt.gca().add_patch(point)\n",
    "\n",
    "# show plot result\n",
    "plt.show()\n",
    "\n",
    "print(\"xroot = \", xroot, sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7480d728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xroot = 5.85\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Ellipse\n",
    "\n",
    "# plot data\n",
    "plt.ion()\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_xlabel(\"$x$\")\n",
    "ax.set_ylabel(\"$y$\")\n",
    "plt.plot(xx, yy)\n",
    "\n",
    "# draw horizontal line\n",
    "line = plt.Line2D((xbeg, xend), (0, 0), color='#aaa', lw=1, ls='dashed')\n",
    "plt.gca().add_line(line)\n",
    "\n",
    "# zoom\n",
    "plt.xlim([3, 7])\n",
    "plt.ylim([-7, 7])\n",
    "\n",
    "\n",
    "# draw a cricle\n",
    "xroot = 5.85\n",
    "point = Ellipse(\n",
    "    xy=(xroot, 0), width=0.1, height=0.6, \n",
    "    edgecolor='r', fc='None', lw=1\n",
    ")\n",
    "plt.gca().add_patch(point)\n",
    "\n",
    "# show plot result\n",
    "plt.show()\n",
    "\n",
    "print(\"xroot = \", xroot, sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61a4cf81",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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