DS_TK 3_RESNA FEBRIANTO_21181229

DS_TK 3_RESNA FEBRIANTO_21181229.ipynb

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 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "id": "22b8ae6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "##1\n",
    "#gambarkan dalam suatu piramida tingkatan dan keberagaman antara data,informasi,pengetahuan,dan kearifan,serta jelaskan arah waktu dari masa lalu (pengalaman) dan masa depan (kebaruan)\n",
    "\n",
    "#Piramida Tingkatan dan Keberagaman Data, Informasi, Pengetahuan, dan Kearifan\n",
    "#Berikut adalah representasi piramida tingkatan dan keberagaman antara data, informasi, pengetahuan, dan kearifan, beserta arah waktu dari masa lalu (pengalaman) dan masa depan (kebaruan):\n",
    "\n",
    "#Tingkatan:\n",
    "#-Data: Fakta dan observasi mentah yang belum diolah. (Contoh: suhu, curah hujan, penjualan)\n",
    "#-Informasi: Data yang telah diolah dan memiliki makna. (Contoh: rata-rata suhu, tren penjualan, prediksi cuaca)\n",
    "#-Pengetahuan: Pemahaman yang lebih dalam tentang informasi dan hubungannya. (Contoh: faktor yang mempengaruhi suhu, strategi meningkatkan penjualan, dampak perubahan iklim)\n",
    "#-Kearifan: Kemampuan untuk menerapkan pengetahuan secara praktis dan bijaksana. (Contoh: kebijakan untuk mengelola sumber daya alam, solusi inovatif untuk masalah sosial)\n",
    "\n",
    "#Arah Waktu:\n",
    "#-Masa Lalu (Pengalaman): Berasal dari data dan informasi yang dikumpulkan di masa lalu.\n",
    "#-Masa Depan (Kebaruan): Ditujukan untuk menghasilkan pengetahuan dan kearifan yang bermanfaat untuk masa depan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bbe0d9a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "##2.\n",
    "#Dari sisi nilai kemanfaatannya manakah yang jauh lebih berguna, data atau metadata? lalu mengapa perlu ada metadata? jelaskan dengan singkat. Berikan setidaknya tiga jenis metadata dan penjelasan singkatnya\n",
    "\n",
    "# Jawaban :\n",
    "# Data dan metadata memiliki nilai kemanfaatan yang berbeda, namun saling melengkapi.\n",
    "# Data: \n",
    "#-Lebih langsung: Berisi informasi mentah yang dapat diolah dan dianalisis untuk menghasilkan insights. \n",
    "#-Contoh: Hasil penjualan, data sensor, data survei.\n",
    "\n",
    "    \n",
    "#Metadata:\n",
    "#-Lebih tidak langsung: Memberikan informasi tentang data, seperti asal-usul, struktur, dan makna.\n",
    "#-Contoh: Nama file, tanggal pembuatan, format data, deskripsi variabel.\n",
    "\n",
    "# Metadata diperlukan karena :\n",
    "#-Membuat data lebih mudah ditemukan dan dipahami: Memberikan konteks dan informasi tambahan tentang data.\n",
    "#-Meningkatkan interoperabilitas: Memungkinkan data dari berbagai sumber untuk diintegrasikan dan dianalisis bersama.\n",
    "#-Meningkatkan kualitas data: Membantu dalam identifikasi dan koreksi data yang tidak akurat atau tidak lengkap.\n",
    "#-Mempermudah pengelolaan data: Membantu dalam melacak dan mengatur data.\n",
    "\n",
    "#Terdapat jenis jenis MetaData yaitu :\n",
    "#-Metadata deskriptif: Menjelaskan isi data, seperti judul, abstrak, dan kata kunci.\n",
    "#-Metadata struktural: Menjelaskan struktur data, seperti format file, nama variabel, dan tipe data.\n",
    "#-Metadata administratif: Menjelaskan informasi tentang pengelolaan data, seperti asal-usul, tanggal pembuatan, dan hak akses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb0dcaaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "##3 Bila anda seorang data scientist, gambarkan pekerjaan anda dan tujuan dari ilmu anda tersebut. Jelaskan secara singkat aspek fundamental dari data science\n",
    "#Sebagai seorang Data Scientist, pekerjaan saya berfokus pada pengumpulan, pembersihan, analisis, dan interpretasi data untuk membantu organisasi mencapai tujuan mereka.\n",
    "\n",
    "#Tujuan utama ilmu data adalah untuk menemukan pola dan wawasan tersembunyi dalam data yang dapat digunakan untuk:\n",
    "\n",
    "#-Meningkatkan efisiensi\n",
    "#-Mengoptimalkan proses\n",
    "#-Mengambil keputusan yang lebih baik\n",
    "#-Memprediksi tren\n",
    "#-Mengembangkan produk dan layanan baru\n",
    "\n",
    "#Aspek fundamental dari data science meliputi:\n",
    "#-Matematika dan Statistik: Untuk memahami dan memanipulasi data, serta membangun model statistik.\n",
    "#-Pemrograman: Untuk mengolah data dalam skala besar dan membangun aplikasi data science.\n",
    "#-Komunikasi: Untuk menyampaikan hasil analisis data dan wawasan kepada stakeholders secara efektif.\n",
    "#-Domain Knowledge: Memahami industri dan konteks bisnis untuk menginterpretasikan data secara tepat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ce8a1c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "##4\n",
    "# Reprodusibilitas suatu program yang kompleks dan menggunakan banyak library dan package versi tertentu dapat dipastikan dengan memanfaatkan fitur virtual environment di python. Jelaskan mengenai hal ini\n",
    "\n",
    "#Jawaban :\n",
    "\n",
    "#Virtual environment merupakan fitur penting dalam Python yang membantu memastikan reprodusibilitas program yang kompleks dengan banyak library dan package.\n",
    "#Reprodusibilitas berarti program dapat dijalankan pada komputer lain dengan hasil yang sama, tanpa modifikasi. Hal ini penting untuk:\n",
    "##-Memastikan keandalan dan konsistensi program.\n",
    "##-Memudahkan kolaborasi dengan tim lain.\n",
    "##-Mempermudah deployment program ke production environment.\n",
    "\n",
    "#Virtual environment bekerja dengan:\n",
    "#-Membuat folder terisolasi yang berisi library dan package yang dibutuhkan program.\n",
    "#-Menginstal library dan package dengan versi spesifik di dalam virtual environment.\n",
    "#-Mengisolasi program dan dependensi dari environment Python global.\n",
    "\n",
    "#-Manfaat Virtual Environment:\n",
    "#-Reprodusibilitas: Program dapat dijalankan pada komputer lain dengan hasil yang sama.\n",
    "#-Keandalan: Program terhindar dari konflik library dan package yang berbeda versi.\n",
    "#-Keteraturan: Memudahkan pengelolaan library dan package yang berbeda untuk proyek yang berbeda.\n",
    "#-Keamanan: Meminimalisir risiko keamanan dan bug dari library dan package pihak ketiga."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "21c0130e",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         LAT        LONG  GDPACMP20  GDPACMP21  GDPACMP22  GDPACMP23     POP10     POP20   UR20  UR21  UR22  UR23   EI10   EI11   EI12   EI13   EI14   EI15   EI16   EI17   EI18  ID       AREA     MW22     MW23                       PROVINCE  Perubahan Populasi\n",
      "32 -4.269928  138.080353       2.39      15.16       8.97       5.22   2833381   4303707   4.28  3.33  2.83  2.67  42.45  43.43  44.41  45.76  46.82  47.60  48.92  50.18  51.82  33  309934.40  3561932  3864696                          Papua           51.892986\n",
      "31 -1.336115  133.174716      -0.76      -0.51       2.01       3.91    760422   1134068   6.80  5.84  5.37  5.38  53.41  53.87  54.70  55.46  56.17  56.86  57.59  58.47  59.04  32  114566.40  3200000  3282000                    Papua Barat           49.136664\n",
      "19 -0.278781  111.475285      -1.82       4.80       5.07       4.46   4395983   5414390   5.81  5.82  5.11  5.05  50.87  51.06  52.92  54.54  55.78  57.12  57.63  58.22  58.59  20  120114.32  2434328  2608601               Kalimantan Barat           23.166764\n",
      "9  -4.558585  105.406808      -3.80       3.43       5.09       5.20   1679163   2064564  10.34  9.91  8.23  6.80  63.26  63.76  65.02  66.14  66.89  67.17  67.40  68.22  68.31  10    8084.01  3050172  3279194                 Kepulauan Riau           22.951971\n",
      "29 -2.844137  119.232078      -2.34       2.57       2.31       5.25   1158651   1419229   3.32  3.13  2.34  2.27  51.48  53.32  53.87  54.73  55.63  57.06  58.08  59.03  59.97  30   16787.19  2678863  2871794                 Sulawesi Barat           22.489775\n",
      "20 -1.681488  113.382355      -1.41       3.59       6.45       4.14   2212089   2669969   4.58  4.53  4.26  4.10  56.18  56.56  56.94  58.52  59.23  60.72  61.35  62.22  62.76  21  153564.50  2922516  3181013              Kalimantan Tengah           20.698986\n",
      "30  1.570999  127.808769      -0.91       3.63       5.31       5.21   1533506   1848923   7.57  6.93  6.88  6.31  63.86  64.75  65.33  66.47  68.10  68.20  69.04  69.91  70.60  31   47350.42  2619312  2812827                         Maluku           20.568358\n",
      "8   3.945651  108.142867      -2.29       5.05       4.40       4.38   1223296   1455678   5.25  5.03  4.77  4.56  52.69  53.67  54.16  54.83  55.54  57.09  57.93  58.79  59.11   9   16424.14  3264884  3498479      Kepulauan Bangka Belitung           18.996384\n",
      "7  -3.238462  130.145273      -1.66       2.77       4.28       4.55   7608405   9007848   4.67  4.69  4.52  4.23  54.41  54.92  55.90  57.43  58.93  59.23  59.74  60.58  61.09   8   37735.15  2440486  2633284                        Lampung           18.393382\n",
      "17 -8.652933  117.361648      -0.62       2.30       6.95       1.80   4500212   5320092   4.22  3.01  2.89  2.80  51.50  53.49  55.03  56.41  57.60  58.60  59.19  60.39  60.85  18   19708.79  2207212  2371407            Nusa Tenggara Barat           18.218697\n",
      "27 -4.144910  122.174605      -0.65       4.10       5.53       5.35   2232586   2624875   4.58  3.92  3.36  3.15  58.98  59.73  60.45  61.01  62.24  63.57  64.51  65.31  66.55  28   36757.45  2576016  2758948              Sulawesi Tenggara           17.571059\n",
      "0   4.695135   96.749399      -0.37       2.81       4.21       4.23   4494410   5274871   6.59  6.30  6.17  6.03  63.42  63.92  64.49  65.23  66.60  67.37  68.12  69.18  69.94   1   56500.51  3166460  3413666                           Aceh           17.365149\n",
      "6  -3.577847  102.346388      -0.02       3.27       4.31       4.26   1715518   2010670   4.07  3.65  3.59  3.42  58.35  59.42  60.61  62.45  63.75  64.22  65.07  65.93  66.42   7   19795.15  2238094  2418280                       Bengkulu           17.204833\n",
      "24  0.624693  123.975002      -0.99       4.16       5.42       5.48   2270596   2621923   7.37  7.06  6.61  6.10  60.35  60.89  61.74  62.30  63.33  64.14  64.73  65.63  66.02  25   13930.73  3310723  3485000                 Sulawesi Utara           15.472898\n",
      "3   0.293347  101.706829      -1.13       3.36       4.55       4.21   5538367   6394087   6.32  4.42  4.37  4.23  60.18  60.35  60.53  62.02  62.82  63.71  64.36  65.39  66.15   4   87844.23  2938564  3191662                           Riau           15.450764\n",
      "4  -1.485183  102.438058      -0.51       3.70       5.12       4.66   3092265   3548228   5.13  5.09  4.59  4.53  55.96  57.17  58.21  59.81  60.77  61.45  62.23  62.92  63.27   5   45348.49  2649034  2943000                          Jambi           14.745276\n",
      "2  -0.739940  100.800005      -1.61       3.29       4.36       4.62   4846909   5534472   6.88  6.52  6.28  5.94  61.06  62.11  63.15  64.14  65.08  65.84  66.94  67.79  67.95   3   42224.65  2512539  2742476                 Sumatera Barat           14.185597\n",
      "1   2.115355   99.545097      -1.07       2.61       4.73       5.01  12982204  14799361   6.91  6.33  6.16  5.89  61.20  61.57  62.32  63.75  64.78  65.73  66.51  67.22  67.63   2   72427.81  2522609  2710493                 Sumatera Utara           13.997292\n",
      "18 -8.657382  121.079370      -0.84       2.52       3.05       3.52   4683827   5325566   4.28  3.77  3.54  3.14  51.81  54.09  54.94  56.61  57.98  58.76  59.43  60.14  60.72  19   46137.87  1975000  2123994            Nusa Tenggara Timur           13.701168\n",
      "5  -3.319437  103.914399      -0.11       3.58       5.23       5.08   7450394   8467432   5.51  4.98  4.63  4.11  55.09  55.87  56.71  56.94  58.16  59.31  60.07  60.94  61.00   6   91592.43  3144446  3404177               Sumatera Selatan           13.650795\n",
      "25 -1.430025  121.445618       4.86      11.68      15.22      11.91   2635009   2985734   3.77  3.75  3.00  2.95  56.54  58.48  59.36  60.40  61.63  61.89  62.96  63.86  64.87  26   68089.83  2390739  2599546                Sulawesi Tengah           13.310201\n",
      "26 -3.668799  119.974053      -0.71       4.64       5.10       4.51   8034776   9073509   6.31  5.72  4.51  4.33  56.16  57.25  58.34  59.64  60.79  61.55  62.39  63.39  63.79  27   46116.45  3165876  3385145               Sulawesi Selatan           12.927965\n",
      "12 -7.150975  110.140259      -2.65       3.33       5.31       4.98  32382657  36516035   6.48  5.95  5.57  5.13  53.18  53.53  54.21  55.68  56.88  57.82  58.42  59.15  59.58  13   32799.71  1841487  1986670                    Jawa Tengah           12.764172\n",
      "28  0.699937  122.446724      -0.02       2.40       4.04       4.50   1040164   1171681   4.28  3.01  2.58  3.06  53.73  55.42  55.82  56.89  57.92  58.78  59.51  60.41  61.06  29   12165.44  2800580  2989350                      Gorontalo           12.643872\n",
      "21 -3.092642  115.283759      -1.82       3.48       5.11       4.84   3626616   4073584   4.74  4.95  4.74  4.31  54.35  55.51  56.98  57.73  58.55  59.77  60.44  61.24  61.39  22   37530.52  2906473  3149977             Kalimantan Selatan           12.324657\n",
      "11 -7.090911  107.668887      -2.52       3.74       5.45       5.00  43053732  48274162  10.46  9.82  8.31  7.44  54.34  55.15  56.27  58.08  59.26  59.95  60.67  61.63  61.75  12   36925.05  2501203  2661280                     Jawa Barat           12.125383\n",
      "15 -6.405817  106.064018      -3.39       4.49       5.03       4.81  10632166  11904562  10.64  8.98  8.09  7.52  57.02  58.20  59.61  60.68  61.48  61.86  63.18  63.93  64.43  16    9018.64  1891567  2040244                         Banten           11.967420\n",
      "16 -8.409518  115.188916      -9.34      -2.46       4.84       5.71   3890757   4317404   5.63  5.37  4.80  2.69  58.30  59.57  60.87  61.46  62.16  63.55  64.09  65.19  65.58  17    5449.37  2516971  2713672                           Bali           10.965655\n",
      "10 -6.211544  106.845172      -2.39       3.55       5.25       4.96   9607787  10562088  10.95  8.50  7.18  6.53  67.51  67.74  67.99  68.91  69.52  70.66  71.63  72.46  72.81  11     740.29  4641854  4901798  Daerah Khusus Ibukota Jakarta            9.932579\n",
      "14 -7.536064  112.238402      -2.33       3.56       5.34       4.95  37476757  40665696   5.84  5.74  5.49  4.88  54.36  54.90  55.43  56.79  58.09  58.98  60.16  60.83  61.02  15   46689.64  1840915  1981782                     Jawa Timur            8.509111\n",
      "13 -7.875385  110.426209      -2.67       5.58       5.15       5.07   3457491   3668719   4.57  4.56  4.06  3.69  67.67  69.01  69.44  69.82  70.70  71.75  72.71  73.47  74.29  14    3133.15  1812935  1958169     Daerah Istimewa Yogyakarta            6.109286\n",
      "22  1.640630  116.419389      -2.90       2.55       4.48       6.22   3553143   3766039   6.87  6.83  5.71  5.31  61.50  62.79  64.04  65.27  66.70  67.10  67.88  68.67  69.57  23  194849.08  3014497  3201396               Kalimantan Timur            5.991766\n",
      "23 -2.741051  106.440587      -1.09       3.99       5.32       4.94    738163    701814   4.97  4.58  4.33  4.01   0.00   0.00   0.00  61.19  62.62  62.68  63.27  64.26  65.18  24   72567.49  3016738  3251702               Kalimantan Utara           -4.924251\n"
     ]
    }
   ],
   "source": [
    "## 5 \n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Membaca data dari file CSV\n",
    "data = pd.read_csv(\"data17.csv\")\n",
    "\n",
    "# Menghitung persentase perubahan populasi\n",
    "data[\"Perubahan Populasi\"] = (data[\"POP20\"] - data[\"POP10\"]) / data[\"POP10\"] * 100\n",
    "\n",
    "# Mengurutkan data berdasarkan persentase perubahan populasi\n",
    "data = data.sort_values(\"Perubahan Populasi\", ascending=False)\n",
    "\n",
    "# Memvisualisasikan perbandingan populasi\n",
    "plt.bar(data[\"PROVINCE\"], data[\"POP10\"], label=\"Populasi 2010\")\n",
    "plt.bar(data[\"PROVINCE\"], data[\"POP20\"], bottom=data[\"POP10\"], label=\"Populasi 2020\")\n",
    "plt.xlabel(\"Provinsi\")\n",
    "plt.ylabel(\"Jumlah Penduduk\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Menampilkan tabel data\n",
    "print(data.to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3a659a8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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