hestiwahyuningsih · December 4, 2025 18:51
diff --git a/24325003.ipynb b/24325003.ipynb
 {
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dc06e253-f1ec-421b-9d8a-279f7d65b5c9",
   "metadata": {},
   "source": [
    "# midterm sk5002"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfdfdb86-a493-4c0f-8c45-1e31898457da",
   "metadata": {},
   "source": [
    "### instruction\n",
    "1. Download this notebook from GitHub Gist.\n",
    "2. Rename it to e.g. `20025000.ipynb`, where `20025000.ipynb` is your student ID.\n",
    "3. Upload it later to GitHub Gist after completing all tasks provided below.\n",
    "4. Record link to the uploaded notebook on GitHub Gist.\n",
    "5. Report the link on Edunex (and GitHub Issues in case of emergency)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3265d3d3-3675-4f9a-bde2-ffec6ea67d7c",
   "metadata": {},
   "source": [
    "## part-1 (point 20)\n",
    "+ Read [Back to basics — Jupyter notebooks](https://medium.com/p/dfcdc19c54bc).\n",
    "+ Get familiar with the two modes, Edit and Command.\n",
    "+ Write short answer indicating that you are already familiar with the modes.\n",
    "\n",
    "### answer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "011b1e88-38b3-4d69-ace6-119c744fcbb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Dalam jupyter notebook ada 2 mode utama untuk berinteraksi dengan cell yaitu Edit mode dan Command mode. \n",
    "    1. Edit Mode\n",
    "        -Fungsi untuk mengedit isi cell baik itu kode Python maupun teks Markdown.\n",
    "        -Ciri-ciri : border cell berwarna hijau, kursor teks aktif muncul di dalam cell, kita dapat mengetik, menghapus dan memodifikasi isi cell.\n",
    "        -Cara masuk ke Edit mode : tekan tombol Enter saat berada di sebuah cell atau klik dua kali pada cell yang ingin diedit\n",
    "    2. Command Mode\n",
    "        -Fungsi untuk mengelola cell(menjalankan, menambah, menghapus, menyalin, memindahkan, dan lain-lain)\n",
    "        -Ciri-ciri : border berwarna biru, tidak ada kursor di dalam cell, tekan tombol keyboard untuk menjalankan perintah notebook, bukan mengetik teks\n",
    "        -cara masuk ke Command mode : tekan tombol Esc saat berada di Edit mode. Berikut ini contoh-contoh perintah pada mode Command :\n",
    "           Tombol C untuk menyalin/Copy Cell\n",
    "           Tombol X untuk memotong/Cut Cell"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26541c5d-9d00-4131-be8e-c8c2020f13c7",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## part-2 (point 20)\n",
    "+ Read [The Ultimate Markdown Guide (for Jupyter Notebook)](https://medium.com/p/d5e5abf728fd)\n",
    "+ Get familiar with Markdown syntax.\n",
    "+ Write short answer indicating that you are already familiar with the syntax.\n",
    "\n",
    "### answer\n",
    "Markdown syntax adalah bahasa penanda ringan(lightweight markup language) yang digunakan untuk memformat teks agar terlihat rapi seperti membuat judul, daftar, tabel, rumus dan gambar tanpa menuliskan kode HTML yang rumit. Contoh Markdown :\n",
    "1. Heading/Judul\n",
    "   Gunakan tanda pagar # di awal baris. semakin banyak tanda pagar semakin kecil ukuran judul\n",
    "2. Format teks\n",
    "**tebal** atau __tebal__ untuk huruf tebal\n",
    "*miring* atau _miring_ untuk huruf miring\n",
    "3. Daftar list\n",
    "   Gunakan tanda -, * atau + untuk list tidak berurutan. Gunakan angka diikuti titik untuk list yang berurutan.\n",
    "4. Hyperlink\n",
    "   Formatnya [teks yang ditampilkan](url)\n",
    "5. Gambar\n",
    "   Formatnya ![teks yang ingin ditampilkan](url)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6b700b-ed7f-4466-b9a6-ab2a53594f2e",
   "metadata": {},
   "source": [
    "## part-3 (point 20)\n",
    "+ Explain steps how to find Python installer, download it, and then install it.\n",
    "+ Write the answer below and add as many steps required.\n",
    "\n",
    "### answer\n",
    "Langkah-langkah find, download dan menginstall Python\n",
    "1. Buka browser (Chrome, Edge, Firefox, dll).\n",
    "2. ketik di kolom pencarian : download python\n",
    "3. Klik situs resmi\n",
    "4. Pilih versi python\n",
    "5. Download/unduh\n",
    "6. Untuk menginstallnya klik dua kali file hasil unduhan akan muncul jendela installer, ikuti perintah di jendela installer\n",
    "7. untuk menguji instalasinya\n",
    "   Buka CMD di windows ketik py--version, jika instalasi berhasil akan muncul versi python nya\n",
    "8. untuk mencoba menjalankan python\n",
    "   di CMD ketik python\n",
    "   muncul interaktif shell >>>print(\"Hello, world\")\n",
    "                              Hello, world\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b57b1336-fd55-4e36-a71a-f381f311c3a6",
   "metadata": {},
   "source": [
    "## part-4 (point 20)\n",
    "+ Explain what is the purpose of using virtual environment in Python.\n",
    "+ Write the answer below in 1-2 paragraphs using items.\n",
    "\n",
    "### answer\n",
    "    Tujuan menggunakan Virtual Environment untuk memisahkan proyek, karena setiap proyek Python bisa membutuhkan versi library yang berbeda. Dengan virtual environment kita bisa membuat ruang kerja terpisah agar satu proyek tidak mengganggu proyek lainnya contoh Proyek A memerlukan numpy versi 1.21 dan proyek B memerlukan numpy versi 1.25. Dengan virtual Environment kedua proyek tidak akan saling bertabrakan.\n",
    "    Tujuan lainnya adalah menjaga sistem tetap bersih. Jika banyak library yang kita masukkan ke sistem global, maka dapat membuat python utama menjadi berantakan. Dengan Virtual Environment kita dapat menyimpan semua library proyek ke dalam folder yang terpisah."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6653336-cb88-4efe-8a3c-78a392100d9b",
   "metadata": {},
   "source": [
    "## part-5 (point 20)\n",
    "+ Correct following code.\n",
    "+ Correct only one part in each correction.\n",
    "+ Give short explanation what is corrected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82a2d8dc-d2c8-4200-833b-6c72d761f219",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [range(11)]\n",
    "y = [25 - (5 - i)**2 for i in x\n",
    "\n",
    "plt.plot(x, y, 'r:o)\n",
    "plt.xlabel('x')\n",
    "plt.ylable('y')\n",
    "plt.grid()\n",
    "plt.sohw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d877f2-838e-47f5-87ac-ccaddc91d084",
   "metadata": {},
   "source": [
    "### correction 1\n",
    "Kesalahan 1 di line 3\n",
    "x = [range(11)]\n",
    "penjelasan : range (11) sudah menghasilkan deretan angka 0-10, tetapi adanya tanda [] membuat x menjadi list berisi satu elemen, yaitu objek range bukan daftar angka"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75c632ef-7b2b-42a3-ba4c-4c5b82fb1026",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the code after correction 1\n",
    "x = range(11)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee9abd16-86dc-44fd-9eee-e669a97cd7eb",
   "metadata": {},
   "source": [
    "### correction 2\n",
    "kesalahan 2 di line 8 dan line 10\n",
    "plt.ylable('y')\n",
    "plt.sohw()\n",
    "penjelasan : kesalahan pengetikan perintah atau fungsi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81c8cb82-87b8-4978-93af-2e686f904b97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the code after correction 2\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f2c916c-9687-4e7d-afd9-02a6ffad1748",
   "metadata": {},
   "source": [
    ".."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "482f4522-be9e-4706-8b58-0e1b7a8318ae",
   "metadata": {},
   "source": [
    "### corection n\n",
    "+ .. (short explanation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3677fe04-646f-4583-8be9-6a92fda69a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the code after correction n\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = range (11)\n",
    "y = [25 - (5 - i)**2 for i in x]\n",
    "\n",
    "plt.plot(x, y, ':ro')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a94f756-2305-462b-a06c-e3fe0df7e866",
   "metadata": {},
   "source": [
    "### summaries\n",
    "kesimpulan : kode ini akan menampilkan grafik parabola dengan titik-titk berwarna merah, label sumbu x dan y, dan grid (garis bantu)"
   ]
  }
 ],
 "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": "3.13.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
 }
No results found