ans/10219025/root_finding_scan_range_method.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cca11c7c",
   "metadata": {},
   "source": [
    "# root_finding_scan_range_method\n",
    "Jalankan kode pada sel-sel di bawah ini sehingga identitas Anda tertampilkan dengan benar dan perkirakan posisi rentang-rentang di mana akar dari suatu fungsi terdapat."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6366ac0c",
   "metadata": {},
   "source": [
    "## scan range method\n",
    "Ulas kembali secara singkat metode scan range method oleh Namir Shammas di http://h20331.www2.hp.com/hpsub/downloads/S06_Oct12_Multi-Root%20Solver%20Ver%201a.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "264a9720",
   "metadata": {},
   "source": [
    "## identity\n",
    "Koreksi kode pada sel berikut sehingga identitas Anda benar tertampilkan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e2d555c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10219025 Asla Roudhatu Rohmah https://github.com/AslaRoudhatuRohmah\n"
     ]
    }
   ],
   "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 = 10219025\n",
    "print(nim, end=' ')\n",
    "print(stu[nim]['name'], end=' ')\n",
    "print(\"https://github.com/\", stu[nim]['github'], sep='')"
   ]
  },
  {
   "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 - 2.5 e^{-1.5(x - 2.4)} \\right] \\sin \\tfrac12 \\pi (x-1.25)\n",
    "\\end{equation}\n",
    "\n",
    "dapat digambarkan dengan dalam rentang $x \\in [x_{\\rm beg}, x_{\\rm end}]$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "91d18261",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "# define a function of x\n",
    "def f(x):\n",
    "    y1 = (x -  2.5 * math.exp(-1.5*(x - 2.4)))\n",
    "    y2 = math.sin(2 * math.pi * (x - 1.25) / 4)\n",
    "    y = y1 * y2\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "id": "86684f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jtlwxBJoILBHniiYtIUZ3F6spbTnUQm5qPMvz0qwO5S1dlJ1MTmqcTg9NQ22Lr7WEHfcQgCYCy2Qmx+rUkHqTodFxXq7tYGNJtq2nhcDXjfTyBVm8eqQDr7abeEvVE4lARwTq72Sl6KYy9WZ/O9rBqdFxNpXYe1rI79L5s+g9NXr6B52aWk2Lh4yk2NNLx+1GE4FFMpPjdPmoepMth1pJjnOxbl6G1aFMy9p5swDYUddpcST2VtPqYVFOim1HeZoILKIjAnUmr9ewtaqNKxdmEeeyV1Oys8lzJ5CfnsDOY5oIzsbrNdS2emw7LQSaCCyTmRxH//AYp0a0zYTyOdDQQ7tnmE0lOVaHcl7WFs9i17EurROcRUP3KQZHxm25o9hPE4FF/JvKtGCs/LYcaiU6Srh6UbbVoZyXtfMy6B4c5XBbv9Wh2FL1RGuJhZoIzk5ECkTkBRE5JCKVIvJxq2MKhayJTWW6hFT5ba1qZU1RBmkT51o7xbpiX51Ap4emVtvqK6TbdTMZ2CARAGPAp4wxJcA64F9EpMTimILu9O5irRMofEdS1rb2O25aCKAgI4E5afHs1AZ0U6pp7acgI4HkOJfVoZyV5YnAGNNsjNk78XsPUAXkWRtV8J3uN6QjAgVsrWoDYOMS5yUCEWHtvFnsPNaJMVonOFNti2/FkJ1ZnggmE5EioBTYOcWf3Sci5SJS3t7u/I6Hb7SZ0CWkCrZVtbIgO5nCWfbrTDkda4sz6Ogf4Wi71gkmGxnzcrS939bTQmCjRCAiycAfgE8YY/rO/HNjzIPGmDJjTFlWVlboAwywmOgo0hNjaO8fsjoUZbG+oVF2HeviWgeOBvze2E+g00OTHe8cYMxrbL10FGySCEQkBl8SeNgY84TV8YRKZnKcjggUL9e2M+Y1bFzirNVCkxXNSiQ7JY6dxzQRTFbTYv9CMdggEYhvq91DQJUx5ttWxxNKmclxWiNQbKtqIz0xhtLCdKtDuWCn6wR1WieYrLbVQ3SUMC/LfseNTmZ5IgA2APcA14jI/omPt1kdVChkpcTpPoIINzbu5YWaNq5elE20DQ8sOR9rizNo8wxT36XnE/jVtHgozkyy/U5xy9czGWNeBZz9P+ACZSZrm4lIt+9kDz2Do46uD/iVFflGNOXHu5k7y97vgEOlttXD0jn2bicO9hgRRKyslDgGR8YZHBmzOhRlka1VrbiihMsXZlodyowtzE4hJd5F+Yluq0OxhVMj45zoGrR9fQA0EVgqU5eQRrxtVW2snZdBaryzdhNPJSpKWFWYzp4TWjAGONLWjzGwKNee505PponAQpmnN5XpEtJIVN85yJG2fq5d7PxpIb+yuenUtvbTOzhqdSiWq3FAawk/TQQWOt1vSEcEEWlrVSsA1zp42eiZVk/UCfbW6/RQbauHWFeUI+olmggspG0mItu26lYuyk52xA+K6VpZ4CY6SijX6SFqWjwsyE52xGowTQQWykyOwxUlNPecsjoUFWKeoVF21nWF1WgAIDHWxdI5qew+riOC2lb79xjys3z5aCSLjhLmuBNo6HZeIhgb97K9rpOnDzSx50Q3mclxzHEnUDQribvWFp4e7aipvVzbwZjXhFV9wG/13HR+t7OekTEvsa7IfK/Ze2qU5t4hW59BMFlkfpdsJD89gYZuZ23AeWJvA2u/vI17HtrFX15voTgzCWNg17Euvretlqu+8QLf33ZYl8W+hW1VrbgTY1hV6LY6lIArm5vB8JiXyqZeq0OxzOGJQrGOCNS05Kcn8GKNM7qpjo17+fJfqvnla8e4pCid//MPy7lqURbxMW/smqxr7+frm2v49pZaHt55gp/dU8bKArd1QdvQuNec3k3sig6/92L+jWV7TnQ7um3GTJxeMaQjAjUd+emJtHmGGRq199nFPYMj3PurXfzytWO8f0MRv/vQOm5Ylvt3SQBgXlYyP71nNY9/eD2xrije8/Md7KzTk6sm21ffTffgKNcsDq/6gF9Oajz56QmUR3CdoKbFQ3Kcizlp8VaHMi2aCCyWn54AQJONC8Zj414+/Ns97D7WzTfftYIHbllKzDneyZYVZfDYP19Kblo89/5qFy/VOmPUEwpbq9pwRQlXLnJ+O/WzuaQog/IT3RHbgK6mxcPCnGR8PTXtTxOBxfLTfQeR2Llg/JW/VrOjrouvvmM571ydP+2vy02L5/f/vJ55mcn80693s/2ojgzAVx9YUxweu4nPZtXcdDr6hznZZd9/18FijKGm1cOi3FSrQ5k2TQQW848I7JoIntzXyEOv+qaDbl81/STgNys5jkc+tI6CjETuf2QfbX2RvYu6vnOQw239YTst5OcvgkfixrJ2zzA9g6MsyrF/awk/TQQWy0mNxxUltlw5dKipj889cZC1xRl84W1LLvg6aYkx/PTu1QwMj/Gvv9vH6Lg3gFE6i383sRPPJj4fi3JSSIyNZl8EJoLqicNodESgps2uewnGvYbPPXGQlPgYfvSeVeesCZzLwpwUvnL7cnYd7+Ibz9YEKErn2XLIdzZxUWb47Caeiis6ihX5bvbW91gdSsjV+peOOmTFENgkEYjIDSJSIyJHRORzVscTavnpCZy02Yjg0d31HGzo5Us3LSEzOTCbw24rzePudYU8+HIdL9S0BeSaTtIzOMKu411sKgnv0YBfaaGbquY+To3Ye0VcoNW0eMhKiSMjKdbqUKbN8kQgItHAj4AbgRLgThEpsTaq0PJtKrPPiKBrYISvb65h3bwM3r5iTkCv/e83l3BRdjJf+mNFxG04e6GmjXGviZhEsKownTGv4fXGyNpYVuOg1hJ+50wEIrJFRFYEMYY1wBFjTJ0xZgR4FLg1iPeznfz0RNpttJfg65urGRge4z9vXRbw5W9xrmi+cvtyGntO8Z0ttQG9tt1tOdRKdkocK/LdVocSEqURWDD2eg21rR5HtJ6ebDo7iz8LfFdEjgNfMMY0BziGPODkpM8bgLVnPklE7gPuAygsLLzgm1VWVnLo0KHTn2/cuBGArVu3nn6spKSEpUuX8vTTTzM05Fvl4na72bRpE+Xl5Rw7duz0c2+++Wa6u7t57bXXTj+2evVq5s2bx2OPPXb6sdmzZ3PZZZfx6quv0tz8xl/hu971LuZE9/GN1YM8/eQTAGzYsIH09HSeeeaZ088rLi6mrKyMLVu20NPTA0B8fDy33HJLwF/TXw7Gc/+6bA68vJkDF/ia6urq2LNnz+nHznxN31gNO+trqGjMo/nQrqC/pkB8n871mt7q+3TdjW9Duhv49MIR/vCHx8PiNU3n+/SN1XC0sQ6YHzavCc7+fVpQeik3zT7FkqFKHnusMiivyX/vgDLGTOsDeAdwAHgASJju103juu8EfjHp83uAH77V16xevdqEk511nWbuZ58xL9a0WR2KueNnfzNl/73FeIZGg3qfnoERs/q/tpibv/+KGRv3BvVedvBCdauZ+9lnzLaqFqtDCalPPLrPlP33FuP1hv/32BhjNlc0m7mffcbsq++2OpQpAeVmip+p06oRiG9+oAb4CXA/cFhE7glQLmoECiZ9nj/xWMR4Yy+BtQXjHXWd7Kjr4iNXzic5LrhtqNISY/iPW0p4vbGX3+08EdR72cGWQ60kxkZz6Xznn018PlYVumn3DNuqBhZMNS3+U8mcs4cAplcjeA3fD+bv4JvGeR9wFbBGRB4MQAy7gQUiUiwiscC7gT8F4LqOkZMaT0y0WP6f5XtbD5OVEsdday986u183HLxbNYUZ/DdrYfxDIXv0YZer2FrVStXLMh6U2+mcOdvOhcpdYKaVg+FGYkkxjqrn+d0RgT3AXnGmE3GmH83xjxjjDlijLkfuHymARhjxoB/BZ4FqoDfG2MqZ3pdJ7HDXoIddZ1sr+vkw1fOD9kPKxHhi29bQufACD996WhI7mmF1xt7ae0bjpjVQpMtzk0hISaafRGyn8DXY8hZhWKYRiIwxlROzC1N5aZABGGM+YsxZqExZr4x5v8E4ppOY/W5BP7RwHtCNBrwW1Hg5taVc/jFK8do7g3P6YPNlS1ER0nYt5WYiis6iovz0yJih/Hw2DjHOgZY7KCNZH4z2kdgjKkLVCCRLt+daNmIYKcFo4HJ/u26RRgD33w2/JaTGmPYXNHC+nmzSHfQBqNAWjU3ncqmPtssjw6WuvYBxr3GMWcQTGb5hjLlk5+eYNlegh+/eJTM5FjuWhPa0YBfQUYi799QxBP7GsLuVKuaVg/HOga4YVmu1aFYprTAzZjXUBHmG8v8heKIGxGowMnP8K0cagzxuQRH2jy8VNvOe9cXkRBrXSHzo1dfRGp8DN9+LrxGBX99vQURuH5p5CaClRMby8K9TlDT6iEmWih2YB8pTQQ2YdW5BA+9epw4V1TIawNnSkuI4b4r5rGtui2sVphsrmjhkqIMslIC06/JibJTfCeW7T/ZY3UoQVXd3Mf8rOQZN2i0gvMiDlNW7CXoGhjhib0N3L4qj1kBaiw3E++7tIiMpNiwaT1xtL2fmlYPN0bwtJBfaWF62BeMq1s8LJntnNbTk2kisInslHjiXFHUtQ+E7J4P7zjB8JiXD2woDtk930pSnIsPXzmPVw53sOtYl9XhzNjmihaAiK4P+JUWuGnqHaKlNzwPJuoZHKG5d8iR9QHQRGAb0VHC4twUqpr7QnK/4bFxfr39BFctymKBjdY937OuiKyUOL71XI3jz7v9a0UzKwvczE5LsDoUy/kb0O0/GZ6jAv9hNIt1RKBmasnsVKqa+0LyA/DpA8109A/zwcvsMRrwS4iN5l+ums/OY138zcFnHJ/sGqSisU+nhSaUzEklNjoqbAvG1RNv4JboiEDN1JLZqXQPjtLaNxzU+xhj+NVrx1iYk8xlF9mv982dawvJTY3nu1trHTsq+PPrvu6RNy6bbXEk9hDniqZkTmr4JoIWDxlJsY5dFKCJwEb8haZDzcFdb723vofKpj7eu74o4OcNBEKcK5qPXDWf3ce72V7nzFHBU/ubKC10Uzgr0epQbKO00M3Bxh7GwvDM6qoWD4tzU2z5/2k6NBHYyOLZvmFlVbMnqPf53+3HSYlz8Q+leUG9z0zccUkB2SlxfG/rYatDOW+1rR6qmvu4NcCnuzldaWE6Q6Pe0/Pp4WLca6ht8bDYQYfVn0kTgY2kxseQn57AoSAWjNs9w/z59WbeWZZPUpBbTc9EfEw0H77SVyvY4bBRwVP7G4mOEm66WBPBZKUFbgD2hdl+gvquQU6Njp9+I+dEmghspmSiYBwsj+6qZ3TccM+6uUG7R6DctbaQzOQ4vr/NOaMCYwxP7W9iw0WZjp0vDpb89AQyk+PCbj/BG4ViHRGoAFkyO5XjHQOcGgl8z6GxcS8P76zn8gWZzMuy/8EZvlHBPP52tJPdx52xr2BvfTcN3ae4baWOBs4kIpQWutkfZgXjqhYPUQILHHYYzWSaCGxmyexUvMbXtyTQthxqpaVviHvXFwX82sHynrVzyUyOdcyo4Kn9TcS5orgugnsLvZXSQjd1HQN0D4xYHUrAVDf3UZyZ5OhDhyxNBCLyDRGpFpGDIvJHEXFbGY8dlEysHArG9NCvtx8nz53A1Q7qi58QG819V/h2G+85Ye9Rwei4l2cONrOxJCfoR306VWmB78Sy/Q091gYSQNUtHsduJPOzekSwBVhmjLkYqAU+b3E8lstPTyA5zhXwRFDV3MeOui7eu34u0VHOWuJ297q5zEqK5bs2X0H06uEOugZGuG2lfVdjWe3i/DSiBPadCI86Qf/wGPVdg47dSOZnaSIwxjw3cVQlwA58B9dHtKggtZr4zfbjxMdEccclBQG9bigkxrr40OlRgX1/gPy+/CQZSbFcuTDL6lBsKynOxeLc1LBZOfTGGQQ6IgiUDwB/Pdsfish9IlIuIuXt7e0hDCv0lsxOpbrZE7BdtT2DI/xxXyO3rczDnejMU7LuWTeXjKRYvmfTWkG7Z5gth1p55+p8Yl12+m9lP/6CsdfrzF3jk1W3+N6wOXnpKIQgEYjIVhGpmOLj1knP+SIwBjx8tusYYx40xpQZY8qyssL7HVfJnFQ8w2MBO5vg/+4+ydCol3svLQrI9ayQFOfiQ5fP4+XadlueV/D4ngbGvMaRI65QW1WYjmd4jCPt/VaHMmPVzR5S4lzkuZ3dWDDoicAYs9EYs2yKj6cAROR9wM3Ae4xTG8sE2ButJmY+PTTuNfxm+wnWFGc4tle633vXzyU9McZ25xV4vYZHd9eztjiD+Q5Ylmu10tMnltkvoZ+vyqZelsxOdWxrCT+rVw3dAHwGeLsxJnQnstjcopwUooSAnPG6taqVxp5TvN/BowG/pDgXH7lqPq8c7mCnjXYb76jr5ETnIHdadOaz0xRnJuFOjGHviR6rQ5mRca+hqtlDyRxnv8EC62sEPwRSgC0isl9EfmpxPLaQEBtNaWE6L9bMvBbyy1ePMTstnk0lOQGIzHrvXV9Edkoc37TReQW/21VPWkKMHkAzTSJCaYGbfQ4/m+B45wCnRsdZqolgZowxFxljCowxKyc+PmxlPHaycUkOrzf2zuhEpz0nutl5rIsPXlaMy4HnqE4lPiaa+69dwO7j3bxUa/2igc7+YZ6rbOX2VXmO3lAUaqsK0znc1k/f0KjVoVywyibf1O3SOWkWRzJz4fHTIQxtXOLb9LWtuvWCr/GTF4/gTowJuymLO8oKyE9P4FvPWX9ewe/LGxgZ94bd33GwlRamYwwccPAy0sqmXmKjoxzdWsJPE4FNXZSdzNxZiWw9dGGJoKbFw9aqNt53aZGtu4xeiFhXFJ/YuJDXG3t5trLFsjiGx8b51WvH2HDRLBba6LhPJ1hRkIYIjq4THGrqY2FuMjFhMNp2/isIUyLCxiU5vHa0k4HhsXN/wRl+8uIREmOjeV8YFImn8g+leSzITuZrm2sYGbPmoJMn9zXS5hnmw1fOt+T+TpYSH8PC7BTH1gmMMVQ29Z1uCeN0mghsbOOSHEbGvLxyuOO8vq6+c5CnDzZz15pCx24gO5foKOELNy3hWMcAv9l+POT393oNP3u5jqVzUm153KcTrJrrZp9DN5a19A3RNTASFvUB0ERga2VF6aQlxLC16vymh3768lGiRfiny+cFKTJ7uHpRNlcszOL72w7TFeJulluqWqlrH+Cfr5zv+DXkViktSKf31Ch1HQNWh3LeKhv9hWIdEaggi4mO4upFWTxf3cb4NN81VTb18uiueu5cU0BuWnyQI7Tel25awsDION/dGrpNZsYYfvrSUQoyEnibLhm9YKvm+jqR7rVx/6izqWzqQwTHb9L000RgcxtLcugaGJnWLkyv1/ClJyvISIrl/9m0KATRWW9hTgp3rSnk4Z31HA7CGQ5T2XWsi331Pdx3+bywWZZrhXkTG8vs3EjwbCqbeimelRQ2CzH0X7HNXbEwi5ho4Q97G8/53Mf2nGRffQ+fv3EJaYkxIYjOHj65aSGJsdH8x1OVQV9Oaozha5uryUyO411l2ldoJqKihNWF6ZTb/JyJqRxq7guLHcV+mghsLjU+hvesncuju+vf8rjG7oERvvrXatYUZXD7qsjqh5+RFMvnb1zC9rpOHtl1Mqj3evpgM3vre/jM9Yt0A1kArJqbztF2Z51Y1js4SkP3KU0EKrQ+ff0i8twJfPbxgwyNvvks43Gv4YE/VdI3NMZ/3bYsIouXd64pYP28WXz5L1U09wama+uZTo2M89W/VLF0TirvWB3xR2cERJm/TuCgBnSVzb4eYOGyYgg0EThCUpyLr73jYuo6BvjOGUXR4bFxPvboPv50oIlPXLuARQ4/KelCiQhffcdyxr2GL/6xIihTRD9/pY6m3iH+4+YSx53yZlcrCty4ooRyB9UJDjWF14oh0ETgGBsuyuTdlxTw85fr+MOeBg63eujoH+b9v9rNnw8284W3Leb+axdYHaal5s5K4t+uX8Tz1W38cd+5ayrno6V3iJ+8eJS3Lc9l7bxZAb12JIuPiWZpXpqjCsaVTX3kpMaRmRxndSgBEx4l7wjxhZuW8NrRDj712IHTj0VHCd961wqdqpjwvkuL2FzRzJeerKBkTmpAjhD0eg1f+OPrjBvD529cEoAo1WRlc9P57Y4TjIx5HXG624GGHpbnua0OI6Ds/7euTkuNj+HZT1zBHz96Kd9790o+tWkhj3xonSaBSaKjhB/dtYqUeBcf+k15QIqQP3npKM9Xt/Glm5ZQkJEYgCjVZKvnpjM85qWyaebnbwRb76lR6toHWJEfPvUB0ETgOImxLkoL07l1ZR73X7uANcUZVodkO9mp8fz07tW09g7zL7/by9j4hfci2n60k289V8MtK+Zwz7q5AYxS+a2eKBg7YXrIf1jUigK3tYEEmC0SgYh8SkSMiGjTFhUQpYXpfPn25fztaCdferJi2juzJ2vrG+L+R/ZRlJnEV25fHpGrsUIhJzWe/PQERySC/RNtsy8OsxGB5TUCESkArgPqrY5FhZd3rs7neMcAP3zhCF0DI3z/ztJpr/2v7xzk/f+zi/7hUR7+p7Ukh8kOUrsqm5vOa0c7McbYOuEebOihaFZi2DVztMOI4Dv4zi12XgtCZXv/dv0iHrilhC1Vrdz18x3Tak6350QXt/34NToHRvif96+J2CW5obR6bjrtnmEauoOzByRQDjb0cnG+2+owAs7qw+tvBRqNMQem8dz7RKRcRMrb260/olA5x/s3FPPju1ZR0dTHxm+/xI9fPEL/FGc8dPQP8+MXj3Dnz3eSlhDDHz+6gXW6VDQkyop8ta5dx+zbbqKtb4jm3qGwqw9ACKaGRGQrMFWLxi8CX8A3LXROxpgHgQcBysrKdPSgzsuNy2dTkJHIN56t4euba/jZS3VcV5KDOzGGlPgYqlv62HKoldFxw1WLsvjOP64kPSm8hv92tignhbSEGHYe67TtKrgDDROF4jCrD0AIEoExZuNUj4vIcqAYODAxJ5gP7BWRNcYY684fVGFrWV4av/7AGg6c7OFHLxzhxdp2PEOjDI16SU+M4d71RdxxSQEL9NjJkIuKEi4pyrD1iODAyR6ioySsWkv4WVYBM8a8DmT7PxeR40CZMeb8juNS6jytKHDz4HvLTn8+Ou4lSkTbRlhs3bwMtla10to3RE6q/c7SONDQw8KcFBJiw6/ZoB2KxUpZKiY6SpOADawt9tVjdtR1WhzJmxljONjQy8qC8BsNgI0SgTGmSEcDSkWuJbNTSI5z2XJ66ETnIL2nRsNyxRDYKBEopSKbKzqKsqJ0dtowERxo6AFghSYCpZQKrjXFGRxp66ejf9jqUP7OgZO9xMdEsTAn2epQgkITgVLKNvx1gt02GxXsre9m2Zy0sD2jOjxflVLKkZbnpZEQE22r6aFTI+NUNPZySRg3eNREoJSyjVhXFKvmum2VCPad7GbMa7ikKN3qUIJGE4FSylbWFs+iuqWP3sFRq0MBoPx4NyKweq6OCJRSKiTWFmdgDOw4Zo/9BLuPd51ugRGuNBEopWyltDCdxNhoXjti/baisXEve090c0lR+I4GQBOBUspmYl1RrC3O4JXD1ieCqmYPAyPjYV0oBk0ESikbumxBFsc6BmjoHrQ0jl3HfUXrcC4UgyYCpZQNXbHAd2rtqxaPCsqPd5GfnsDstARL4wg2TQRKKdu5KDuZnNQ4S6eHjDHsPt7FmjCvD4AmAqWUDYkIl12UxWtHOxj3WnMO1bGOATr6R06fnhbONBEopWzpioWZ9AyOUtHYa8n9y493A7CmOLzrA6CJQCllUxsumqgTWLSMdNfxLtITY5ifFZ6N5iazPBGIyP0iUi0ilSLydavjUUrZQ2ZyHEtmp/LK4faQ39sYw9+OdLCmOIOJo3TDmqWJQESuBm4FVhhjlgLftDIepZS9XLEgkz0nuhkYHgvpfY+299PUO8SVC7PP/eQwYPWI4CPAV40xwwDGmDaL41FK2chlCzIZHTchP77yxRrfKOSKhZkhva9VrE4EC4HLRWSniLwkIpdYHI9SykbWFGeQFBvN1qrWkN735cMdzM9KIj89MaT3tUrQE4GIbBWRiik+bgVcQAawDvg08Hs5y4SciNwnIuUiUt7eHvo5Q6VU6MW5orlqcTZbDrWGbBnp0Og4O+s6I2ZaCEKQCIwxG40xy6b4eApoAJ4wPrsALzDlWMwY86AxpswYU5aVlRXssJVSNnFdSQ4d/SPsP9kdkvvtPNbF8JiXKxdFzs8Zq6eGngSuBhCRhUAsYH2nKaWUbVy9OJuYaOG5ytBMD71U007cROO7SGF1IvglME9EKoBHgXuNMdZsI1RK2VJqfAzr52fybGULofjx8FJtG2vnzSI+Jjro97ILSxOBMWbEGHP3xFTRKmPM81bGo5Syp+tKcjjeOciRtv6g3qehe5Cj7QNcuTBypoXA+hGBUkqd06aSHACerWwJ6n1ervXNTF8ZIctG/TQRKKVsLyc1npUFbp47FNw6wUu1beS5EyKircRkmgiUUo5w3dIcDjb00tRzKijX7x8e46Xadq5dkh0RbSUm00SglHKE65fmAvCX15uDcv0th1oYGvVy68o5Qbm+nWkiUEo5wvysZFYUuPl9+cmgrB56an8T+ekJrCoM/7bTZ9JEoJRyjDvKCqht7edgQ2DPKOjsH+aVwx3csmJOxE0LgSYCpZSD3LxiNvExUfzf8pMBve5fXm9m3GsicloINBEopRwkNT6Gty2fzdP7mzg1Mh6w6z61v4lFOSkszk0N2DWdRBOBUspR/rGsAM/wGH+tCEzR+GTXIOUnunl7hI4GQBOBUsph1hZnUDQrkd8HaHro6YNNALx9hSYCpZRyBBHhXWUF7Kjr4kTnwIyu5fUaHt/TwKpCNwUZkXH2wFQ0ESilHOcdq/JxRQkPvXpsRtd5trKFuvYB7r20KDCBOZQmAqWU4+SmxfOusgIe2VVPQ/fgBV3DGMMPnj9CcWYSN18cudNCoIlAKeVQH7v2IkSE7287fEFf/0JNG4ea+/joVfOJjoq8vQOTaSJQSjnS7LQE7l47lz/sbaSu/fzaUxtj+P62I+S5E7itNC9IETqHJgKllGN99Or5xLmi+M7W8xsV/O1oJ/tP9vCRq+YTE60/Bi39GxCRlSKyQ0T2TxxMv8bKeJRSzpKZHMcHNhTz9IEmKpum13Zi3Gv49pZaclLjeOfq/CBH6AxWp8KvA/+fMWYl8B8Tnyul1LR96Ip5zEqK5V9/t4+ewZFzPv/rz1az50Q3n75+cUQdR/lWrE4EBvDv6U4DmiyMRSnlQGkJMfzsntU0dp/iI7/dy8iY96zPfeZgEz97qY73rC3U0cAkVieCTwDfEJGTwDeBz5/tiSJy38T0UXl7e3uo4lNKOUBZUQZfe+dyttd18u9PVkzZprqquY9PP3aQsrnpPHDLUguitC9XsG8gIluB3Cn+6IvAtcAnjTF/EJF/BB4CNk51HWPMg8CDAGVlZYFvRq6UcrR/KM2nrn2AHzx/hI7+Ye5eN5crFmbR7hnmdztP8L87TpCa4OLHd68i1mX1e2B7kWAc8DDtm4v0Am5jjBFfE/BeY8w52/+VlZWZ8vLy4AeolHIUr9e3Sex/dxyno3+ErJQ4ugZG8BrDNYuy+cwNi1mUm2J1mJYRkT3GmLIzHw/6iOAcmoArgReBa4AL2xmilFJAVJTw8Y0L+MhV89lW1cpT+5soyEjgnnVFFM6K3F5C52J1IvgQ8D0RcQFDwH0Wx6OUCgOxrihuXD6bG5fPtjoUR7A0ERhjXgVWWxmDUkpFOq2YKKVUhNNEoJRSEU4TgVJKRThNBEopFeE0ESilVITTRKCUUhHO0p3FF0pE2oETF/jlmUBHAMNxAn3NkUFfc2SYyWuea4zJOvNBRyaCmRCR8qm2WIczfc2RQV9zZAjGa9apIaWUinCaCJRSKsJFYiJ40OoALKCvOTLoa44MAX/NEVcjUEop9fcicUSglFJqEk0ESikV4SIqEYjIDSJSIyJHRORzVscTbCJSICIviMghEakUkY9bHVMoiEi0iOwTkWesjiUURMQtIo+LSLWIVInIeqtjCjYR+eTEv+kKEXlEROKtjinQROSXItImIhWTHssQkS0icnji1/RA3CtiEoGIRAM/Am4ESoA7RaTE2qiCbgz4lDGmBFgH/EsEvGaAjwNVVgcRQt8DNhtjFgMrCPPXLiJ5wMeAMmPMMiAaeLe1UQXF/wA3nPHY54BtxpgFwLaJz2csYhIBsAY4YoypM8aMAI8Ct1ocU1AZY5qNMXsnfu/B9wMiz9qogktE8oGbgF9YHUsoiEgacAXwEIAxZsQY02NpUKHhAhImTjdMxHfsbVgxxrwMdJ3x8K3Aryd+/2vgtkDcK5ISQR5wctLnDYT5D8XJRKQIKAV2WhxKsH0X+AzgtTiOUCkG2oFfTUyH/UJEkqwOKpiMMY3AN4F6oBnoNcY8Z21UIZNjjGme+H0LkBOIi0ZSIohYIpIM/AH4hDGmz+p4gkVEbgbajDF7rI4lhFzAKuAnxphSYIAATRfY1cS8+K34kuAcIElE7rY2qtAzvrX/AVn/H0mJoBEomPR5/sRjYU1EYvAlgYeNMU9YHU+QbQDeLiLH8U39XSMiv7U2pKBrABqMMf6R3uP4EkM42wgcM8a0G2NGgSeASy2OKVRaRWQ2wMSvbYG4aCQlgt3AAhEpFpFYfMWlP1kcU1CJiOCbO64yxnzb6niCzRjzeWNMvjGmCN/393ljTFi/UzTGtAAnRWTRxEPXAocsDCkU6oF1IpI48W/8WsK8QD7Jn4B7J35/L/BUIC7qCsRFnMAYMyYi/wo8i2+VwS+NMZUWhxVsG4B7gNdFZP/EY18wxvzFupBUENwPPDzxBqcOeL/F8QSVMWaniDwO7MW3Mm4fYdhqQkQeAa4CMkWkAXgA+CrwexH5IL5W/P8YkHtpiwmllIpskTQ1pJRSagqaCJRSKsJpIlBKqQiniUAppSKcJgKllIpwmgiUUirCaSJQSqkIp4lAqQCYOPdh08Tv/1tEfmB1TEpNV8TsLFYqyB4A/lNEsvF1eX27xfEoNW26s1ipABGRl4Bk4KqJ8x+UcgSdGlIqAERkOTAbGNEkoJxGE4FSMzTRDvhhfD3y+0XkzOMFlbI1TQRKzYCIJOLrh/8pY0wV8F/46gVKOYbWCJRSKsLpiEAppSKcJgKllIpwmgiUUirCaSJQSqkIp4lAKaUinCYCpZSKcJoIlFIqwv3/awN91eZRxb4AAAAASUVORK5CYII=\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",
    "ax.set_ylim([-9, 9])\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": "1bf9a1a4",
   "metadata": {},
   "source": [
    "## scan range method\n",
    "Suatu rentang $x \\in [x_i, x_{i+1}]$ dengan\n",
    "\n",
    "<a name='eqn2'></a>\n",
    "\\begin{equation}\\tag{2}\n",
    "f(x_i) f(x_{i+1}) < 0,\n",
    "\\end{equation}\n",
    "\n",
    "memiliki setidaknya satu akar.\n",
    "\n",
    "Kode <a name='cod1'>1</a>. Mencari rentang-rentang terdapatnya akar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3f7cb0c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root range\n",
      "[[6, 9]]\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "def f(x):\n",
    "    y1 = (x -  2.5 * math.exp(-1.5*(x - 2.4)))\n",
    "    y2 = math.sin(2 * math.pi * (x - 1.25) / 4)\n",
    "    y = y1 * y2\n",
    "    return y\n",
    "\n",
    "xbeg = 0\n",
    "xend = 10\n",
    "N = 100\n",
    "dx = (xend - xbeg) / N\n",
    "dx = 3\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",
    "\n",
    "root_range = []\n",
    "\n",
    "x = xbeg\n",
    "while x <= xend:\n",
    "    f1 = f(x)\n",
    "    f2 = f(x + dx)\n",
    "    \n",
    "    if f1 * f2 < 0 and x + dx <= xend:\n",
    "        a_range = [x, x + dx]\n",
    "        root_range.append(a_range)\n",
    "        \n",
    "    x += dx\n",
    "\n",
    "print(\"Root range\")\n",
    "print(root_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7050f55",
   "metadata": {},
   "source": [
    "Rentang-rentang yang di dalamnya terdapat akar dapat digambarkan pada fungsi yang sebelumnya pada Persamaan ([1](#eqn1)) seperti pada gambar berikut ini.\n",
    "\n",
    "Kode <a name='cod2'>2</a>. Menampilkan kurva dan rentang akar-akarnya."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e0a6ffc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0aMySTRn8Nfkwk3u3YWL5GoI0fhLxC/oJFO/4xk9t16/ngcahvLEth4Q9JzhyspS7yitopvGTiOc0bhJv+cZPjaZMYv7ILswd0oEj+SUsXp3Oof0aP4l4TSsJ8Q+9emHR0dzsGz8tTcngufWHubVvW8Zp/CTiGf3Eif/wjZ/ar1/PgxFhLN+azQe7j3Mkv4T55y8QceIETJ2q8ZNIHdK4SfyLb/wUPmUS99zSldmDYjiYV8Li1Qc4ejBb4yeROqaSEP/UqxcWH8/IYT342vjuBAcF8UzSIdal5eLWrIG1a6GiwuuUIvWexk3iv3zjp47JyTwYEcayLZm8u+sYh0+WcOf5izS5dO2nli29TipSb2klIf4tNBQmTKDJrZO5d3R3bh8Yw75jp3li9QEyD+XAihWwb5/XKUXqLZWEBIaePbG5cxkT14Ovje+GA/6SdJANe4/h1q6FNWvg/HmvU4rUOxo3SeBo0QLmziV2/XoebtqIV1OzeGtHLodPljC34iKNL22+0/hJpNb4xUrCzKab2T4zSzez73udR/xYSIhv/DSF+8Z0Z0b/dqTlFPPk6nSyD+dq/CRSyzwvCTMLBp4EZgB9gQVm1tfbVOL3evYkaN5cxo3oyVfHduP8hYs8nXiQTQeOa/wkUouuWhJmttLMBt3ADMOBdOfcIedcOfAyMPsGfj2pL1q0gPh4uowawsOTetA1OoIVW3N4JTWTsrS9lXsqCgq8TikS0KpzTOJ7wB/M7AiV957OreUMHYDMT72fBYz47JPMbBGwCKBr164ArFy5kqKiIgDCw8OZNWsWu3fvJi0t7ZPXTZkyBYCEhIRPHuvbty/9+vXjrbfe4ty5cwBERUUxdepUUlNTOXz48CfPnTlzJoWFhSQnJ3/y2LBhw+jWrRuvvvrqJ4/FxMQwZswY1q9fT27u/32L5s+fz6FDh9i8efMnj40ePZoWLVrw9ttvf/JY165diYuL0+/pGn9PsyaMZU75Sg6FFwB57NyRR4voGLqfOcP7xcWc7tAh4H5P9fH/08yZM2ncuDESOMw5V70nms0D/gtYBvzGOXe2VgKY3QlMd859xff+fcAI59xDVb0mLi7Opaam1saXl/qksBASEji4P5NXPs7k3PkL3DGoPXFdWsJNN1Ve+yk01OuUIp4xs83OubiavKZaxyTMzIB9wJ+Bh4EDvr/Ma0M20OlT73f0PSZSM77xU/cxw3hoUg86tWjC61uyeXVzJuV79mn8JHINqnNMIpnKv7R/T+Vo6H5gAjDczJ6phQwfAzeZWVczCwPuAd6shc8rDVFICIwfT/PpU/nyhJuY1LsNWzOKeGptOicyjlUWxd69XqcUCRjVOSaxCEhz/z6XetjM9lxvAOdchZk9BHwABAN/dc7tvt7PKw1cz54ERUczNTKBLq2a8EpqJk+uTWfO4A4MuZAEubkaP4lUQ7WPSVz2xWbdnHOHajFPteiYhFRbRQUkJ3Nq2y5eTsngyMlSbu7SklmDYght1VKb76RBuZZjEte149qLghCpEd/4KTImhq9EJLFyRw6J+/PIKixl4YjztF6+HEaPht69vU4q4pd0WQ5pGHr2JDg6mumRCXRp3YRXU7N4YnU6c4d2YOCFJMjJgbFjNX4S+QyVhDQcvrOferdJ5qHm4SxNyWBpSiZH8kuYceEiofn5Gj+JfIZKQhoW3/ipRUwMiyKS+GB7FuvTT5JZeJYFwytoqfGTyL/w/NpNIp7o2ZOQ+Xdy+/h+fG5kLPlnyli8+gC7MwsgKQlWr9a1n0TQSkIasqgoiI+nX9sNxEQ2ZmlKBv/YmMHoHq2YfuEiIXl5MHWqxk/SoGklIQ1bSAiMG0fLmdNYNKkno7q3Ijn9JM+sO0Rhbl7l5rs9170dSCRgqSREAG66idD5dzJrYn8WDI/lRHEZT6xOZ292Iaxbp/GTNFgaN4lcEhUFc+YwsM0G2keFs2RTBi9uOMq4ntHcevEiwZfufNeqlddJReqMVhIin+YbP7WeNZ1vTOnF8K4tSdqfx3PrDnPqWH7lne80fpIGRCUhcjm+8VP85AHcFdeRnFNnWbz6APtzijR+kgZF4yaRqvjGT0PabKBD1DaWpGTwwoYjTOwVzeSLjiCNn6QB0EpC5Ep846c2c27jgSm9GRobxeq9eTy//jDFxzV+kvpPJSFSHT16EHbXndw5dRDzhnYgs7CUxavSOXjsVOX4adUqKC/3OqVIrVNJiFSXb/wUN30UD0zoQXhYEM+vP8zqPce5eCC9ck/FyZNepxSpVZ6WhJn9j5ntNbMdZrbczKK8zCNyVb7xU7v423hoSm8Gdoxk5Z4TvLDhCGcujZ/S0rxOKVJrvF5JrAT6O+cGAvuBH3icR6R6evSg0d3zufvWQcwZ0oHD+SUsXp3OkePFsH69xk9Sb3haEs65D51zFb53NwIdvcwjUiNRUVh8PCNuG803JnQnNDiIZ9cdInF/XuX4adkyyM/3OqXIdfF6JfFpXwLeq+qDZrbIzFLNLDUvL68OY4lcQUgIjB1L+7m38+C0PvRt35z3dx3j7xuPUpJXAG+8ofGTBLTrusd1tb6AWQLQ7jIf+pFz7g3fc34ExAFzXTUC6R7X4peKinArV/JR6gHe3ZlLs/BQ7hneic4tI6B798o734WFeZ1SGrA6v8d1dTjnplzp42Z2PzATmFydghDxW77x06h2HxHbcjNLNmXwTOIhZgyIYbRz2KXNd61be51UpNq8PrtpOvBd4A7nXKmXWURqhW/81HHeTB66tS+92jXjnR25vLTpKKX5hTr7SQLODR83XfGLm6UDjYBLJ5dvdM59/Wqv07hJAsKpU7iVK1m/aR/v7zpGZJNQFg7vTMcWjaFbNxg3TuMnqVN+OW66EudcDy+/vsgNFRmJzZnD2LYfEdsylZdTMvhL4kFuG9COkc5h+fkaP4nf86ezm0TqH9/4qfP8WTw0rS/doyN4c3suSz/O5NxJjZ/E/+kqsCJ1oUcPIqKj+XyrlSRt3MfKtGPkFp1lwfBY2q9fDzk5Gj+JX9JKQqSuREYSNDeeCfHj+fKYrpRVXOTPaw+ScrgAd/CgNt+JX1JJiNSl4GAYM4Zu98zm4Wl96No6guVbs3k1NYsyjZ/ED2ncJOKF7t1ptrA1X2i9krUb9rBq7wmyi86ycEQsbTV+Ej+ilYSIVyIjCZ4bz+R5E/nS6K6Ullfw5Jp0Nh8thEOHNH4Sv6CSEPGSb/zUY8FsHp7Wl04tGvPa5ixe35xJeUFR5fhp926vU0oDpnGTiD/o3p3m97bmS9Efsip5D2v35ZFVeJYFI2Jpk5wMubkaP4kntJIQ8ReRkQTPncutd07i/lFdOF1WwVNr0tmWqfGTeEclIeJPfOOnnvfG89CtfWgX2ZhXPs5ixdZszhcWafwkdU4lIeKPunUj6r4FfCX+Zsb1bM2mwwU8nXiQ/OKzkJwMCQmgiyZLHVBJiPir5s0JiY9nxl2T+fwtnSksOc8Tqw+w8WA+FyOjwMzrhNIAqCRE/Jlv/NTnvrk8NK03sS0jeOZgGXenlpF+4ozX6aQBUEmIBIJu3Wj5+YV88Z6xzPr259h/ooTb/riOxasOUF5x0et0Uo95ej+Ja6X7SUhDl3e6jJ++tZt3duTSu10zfj1vIIM6RXkdS/zctdxPwi9WEmb2HTNzZqYL64tUQ3SzRjy5cCjPfj6OwtJy4p9K5udvp1FaXuF1NKlnPC8JM+sE3ApkeJ1FJNBM7duWlY+OZ8HwWJ5ff5hpf0hi3YE8r2NJPeJ5SQC/p/I+14E39xLxA83DQ3ksfgCvLBpJaFAQ9z2fwnf+uZ2i0nKvo0k94GlJmNlsINs5t70az11kZqlmlpqXp38piXzWiG6tePeRsTw4sTtvbMtmyuOJvL0jh0A87ij+44YfuDazBKDdZT70I+CHwK3OuVNmdgSIc85d9boDOnAtcmVpOcV87/Ud7Mw+xZQ+bfj5nP7ERDb2OpZ47FoOXHt2dpOZDQBWAaW+hzoCOcBw59yxK71WJSFydRUXLvK35CP8buU+QoOC+N6M3iwcHktQkDbhNVQBdXaTc26nc66Nc66Lc64LkAUMvVpBiEj1hAQH8dVx3fjgW+MY2CmSH6/YxT3PbuRgnjbhSfX5w4FrEbmBOreK4B9fHsFv7hzI3txiZvxxHU+uSef8BW3Ck6vzm5LwrSh0HWSRG8DMuCuuEwnfGc+UPm34nw/2cccTyezIKvI6mvg5vykJEbnx2jQL56l7h/GX+4Zx8kwZc55M5rF30jhbfsHraOKnVBIiDdC0fu1Y+eh47r45lmfXVW7CS07XQl7+nUpCpIGKbBzKL+cO4OVFIwkOMu59bhP/+ep2TpWe9zqa+BGVhEgDN7JbK957ZCzfmNCdZVuzmfx4Iu/uzNUmPAFUEiIChIcG873pvXnjwdG0i2zEAy9tYdHfN3O8+JzX0cRjKgkR+UT/DpGseGA0P5jRm6T9eUz5XSJLNmVw8aJWFQ2VSkJE/kVIcBBfG9+dD741jv4dIvnh8p0seHYjh/NLvI4mHlBJiMhldWkdwZKvjuBXcweQllvMtD8k8dRabcJraFQSIlIlM+Oe4bGsenQ8k3q14Tfv72P2E8nsyj7ldTSpIyoJEbmqNs3Defq+YTz9uaHknSlj9pPJ/PLdPdqE1wCoJESk2qb3jyHh0fHMH9aRvyQdYvofk9igTXj1mkpCRGoksnEov5o3kCVfHQHAwuc28b3XdmgTXj2lkhCRazKqe2s++NY4vja+G69tyWLK7xN5f1eu17GklqkkROSahYcG84MZfXjjwdFEN23E1/+xha//fTMntAmv3lBJiMh1698hkjceGs33pvdmzb4TTH48kZdTMnRpj3rA85Iws4fNbK+Z7Taz33idR0SuTWhwEN+Y0J33vzWOvjHN+f6ynSx8dhNHtAkvoHlaEmY2EZgNDHLO9QN+62UeEbl+XVtHsPSrI/nl3AHsyj7FtD8k8XTiQSq0CS8geb2S+AbwK+dcGYBz7oTHeUSkFgQFGQuGx5LwnfGM7xnNr97by+wntQkvEHldEj2BsWa2ycwSzexmj/OISC1q2zycv9w3jD/fO5TjxZWb8H713l7OndcmvEARcqO/gJklAO0u86Ef+b5+S2AkcDPwTzPr5i5ztMvMFgGLAGJjY29cYBGpVWbGjAExjOremsfeTePpxIO8vyuXX84dyC3dW3kdT67CvDz7wMzeB37tnFvje/8gMNI5l3el18XFxbnU1NS6iCgitSw5PZ8fLNtJRkEpC4Z34vsz+hDZONTrWA2CmW12zsXV5DVej5tWABMBzKwnEAZoj79IPTa6R+UmvEXjuvHKx5lMfTyRD3Yf8zqWVMHrkvgr0M3MdgEvA1+43KhJROqXxmHB/PC2Pqx4cDQtI8L42t8388BLmzlxWpvw/I2n46ZrpXGTSP1x/sJFnkk6xB9XHSA8JIgf396X+XEdMTOvo9U7gThuEpEGLjQ4iAcn9uC9R8bSu11zvvv6Du59bhNHT2oTnj9QSYiIX+ge3ZSXF43ksfj+7Myq3IT3TJI24XlNJSEifiMoyLh3RGdWPjqeMT2i+cW7e4l/agO7c7QJzysqCRHxO+0iw3n288N4cuFQck+d5Y4nkvnN+9qE5wWVhIj4JTPj9oGVd8KLH9KBp9Ye5LY/rmPToZNeR2tQVBIi4teimoTx2/mD+MeXR3D+4kXufmYjP1y+k+JzuhNeXVBJiEhAGHNT5Sa8r4zpysspGdz6eBIr0457HaveU0mISMBoEhbCj2f2ZdkDo4lqEspX/zeVB5dsIe90mdfR6i2VhIgEnMGdonjzoTH8x609Wbn7OFMeT+TV1EzdCe8GUEmISEAKCwnioUk38e4jY+nZtin/+doO7ns+hYyTpV5Hq1dUEiIS0Hq0acori27h53P6sy2ziGl/SOK5dYe4cFGritqgkhCRgBcUZNw3sjMffnsco7q34r/f2cPcp5LZk1vsdbSAp5IQkXqjfVRjnvtCHIsXDCGr8CyzFq/ntx/s0ya866CSEJF6xcyYNag9CY+O547B7XliTTq3/WkdKYcLvI4WkFQSIlIvtYgI4/G7BvPil4ZTdv4id/3lI368YientQmvRlQSIlKvje8ZzYffHseXRnflpU0Z3Pr7JFbt0Sa86vK0JMxssJltNLNtZpZqZsO9zCMi9VNEoxD+a1Zfln1jFM3DQ/nyi6k8vHQr+We0Ce9qvF5J/Ab4mXNuMPBfvvdFRG6IIbEteOvhMTw6tScf7DrGlMcTeX1zljbhXYHXJeGA5r63I4EcD7OISAMQFhLENyffxDvfHEP36KZ859XtfP6vKWQWaBPe5Xh6j2sz6wN8ABiVhTXKOXe0iucuAhYBxMbGDjt69LJPExGptosXHf/YdJRfv7eXiw7+Y1ov7h/VheCg+nl/7Wu5x/UNLwkzSwDaXeZDPwImA4nOudfN7C5gkXNuytU+Z1xcnEtNTa3lpCLSUGUXneXHy3eyZl8egzpF8et5A+jdrvnVXxhg/LIkrvjFzU4BUc45Z2YGnHLOXfX/jEpCRGqbc443t+fws7fSKD57ngcmdOfBST1oFBLsdbRacy0l4fUxiRxgvO/tScABD7OISANmZswe3IGER8cza1B7/rQ6ndv/tJ7NRxv2JjyvS+KrwO/MbDvwC3zHHEREvNIyIozf3z2Yv33xZs6WX+DOpz/iJ2/s4kxZhdfRPOHpuOlaadwkInXhTFkFv/1gHy9+dISY5uE8Fj+Aib3beB3rmgXiuElExG81bRTCT+/ox2tfH0VEoxC++MLHPPLyVk42oE14KgkRkasY1rkFb39zDN+achPv7sxlyuOJLN/aMDbhqSRERKqhUUgw35rSk3e+OZYurSP49ivbuf9vH5NVWL834akkRERqoGfbZrz29VH8dFZfPj5SwK2/T+JvyYfr7Z3wVBIiIjUUHGTcP7orH357HDd3acnP3krjzqc3sP/4aa+j1TqVhIjINerYogkvfPFm/nD3YI7kl3D7n9bx+5X7KauoP3fCC8hTYM3sNLDP6xzXoTWQ73WI6xDI+QM5Oyi/1wI9fy/nXLOavCDkRiW5wfbV9Fxff2JmqcrvjUDODsrvtfqQv6av0bhJRESqpJIQEZEqBWpJPON1gOuk/N4J5Oyg/F5rcPkD8sC1iIjUjUBdSYiISB1QSYiISJUCriTM7IiZ7TSzbddyOpeXzCzKzF4zs71mtsfMbvE6U3WZWS/f9/zSf8Vm9i2vc9WEmX3bzHab2S4zW2pm4V5nqgkze8SXfXcgfO/N7K9mdsLMdn3qsZZmttLMDvh+beFlxiupIv983/f/opn59amwVeT/H9/fPzvMbLmZRV3t8wRcSfhMdM4NDsDzlf8IvO+c6w0MAvZ4nKfanHP7fN/zwcAwoBRY7m2q6jOzDsA3gTjnXH8gGLjH21TVZ2b9qbxJ13Aq/+zMNLMe3qa6qheA6Z957PvAKufcTcAq3/v+6gX+Pf8uYC6QVOdpau4F/j3/SqC/c24gsB/4wdU+SaCWRMAxs0hgHPA8gHOu3DlX5GmoazcZOOicO+p1kBoKARqbWQjQhMrb5waKPsAm51ypc64CSKTyLyu/5ZxLAj5778/ZwIu+t18E5tRlppq4XH7n3B7nXEBc7aGK/B/6/vwAbAQ6Xu3zBGJJOOBDM9tsZoF0u9OuQB7wNzPbambPmVmE16Gu0T3AUq9D1IRzLhv4LZAB5AKnnHMfepuqRnYBY82slZk1AW4DOnmc6Vq0dc7l+t4+BrT1MkwD9yXgvas9KRBLYoxzbigwA3jQzMZ5HaiaQoChwJ+dc0OAEvx7qX1ZZhYG3AG86nWWmvDNvmdTWdbtgQgz+5y3qarPObcH+DXwIfA+sA0I6KvIucrz73UOvgfM7EdABfDS1Z4bcCXh+xchzrkTVM7Eh3ubqNqygCzn3Cbf+69RWRqBZgawxTl33OsgNTQFOOycy3POnQeWAaM8zlQjzrnnnXPDnHPjgEIqZ8qB5riZxQD4fj3hcZ4Gx8zuB2YC97pqbJQLqJIwswgza3bpbeBWKpfhfs85dwzINLNevocmA2keRrpWCwiwUZNPBjDSzJqYmVH5/Q+YEwcAzKyN79dYKo9HLPE20TV5E/iC7+0vAG94mKXBMbPpwHeBO5xz1bqlXkDtuDazbvzfGTUhwBLn3GMeRqoRMxsMPAeEAYeALzrnCj0NVQO+Ys4AujnnTnmdp6bM7GfA3VQus7cCX3HOBcwd7c1sHdAKOA886pxb5XGkKzKzpcAEKi+vfRz4CbAC+CcQCxwF7nLOffbgtl+oIn8BsBiIBoqAbc65aR5FvKIq8v8AaASc9D1to3Pu61f8PIFUEiIiUrcCatwkIiJ1SyUhIiJVUkmIiEiVVBIiIlIllYSIiFRJJSEiIlVSSYiISJVUEiK1wMzWmNlU39v/bWaLvc4kUhtCvA4gUk/8BPh/vktnDKHyIogiAU87rkVqiZklAk2BCc65017nEakNGjeJ1AIzGwDEAOUqCKlPVBIi18l3yeuXqLxfxRnflTZF6gWVhMh18N0lbhnwHd+NgX5O5fEJkXpBxyRERKRKWkmIiEiVVBIiIlIllYSIiFRJJSEiIlVSSYiISJVUEiIiUiWVhIiIVOn/A63xeFPnFszVAAAAAElFTkSuQmCC\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_xlim([5,12])\n",
    "ax.set_ylabel(\"$y$\")\n",
    "ax.set_ylim([-9, 9])\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",
    "# draw root range\n",
    "for r in root_range:\n",
    "    x1 = r[0]\n",
    "    x2 = r[1]\n",
    "    line = plt.Line2D((x1, x2), (5.5, -3.4), color='#f00', alpha=0.4, lw=4)\n",
    "    plt.gca().add_line(line)\n",
    "\n",
    "# show plot result\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bcc9419",
   "metadata": {},
   "source": [
    "Modifikasi Kode [1](#cod1) dan [2](#cod2) yang diberikan sehingga penggambaran rentang-rentang terdapatnya akar menjadi lebih tepat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a33174e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}