{"id":2080,"date":"2023-07-23T19:27:32","date_gmt":"2023-07-23T19:27:32","guid":{"rendered":"https:\/\/statorials.org\/id\/normalisasi-data-dengan-python\/"},"modified":"2023-07-23T19:27:32","modified_gmt":"2023-07-23T19:27:32","slug":"normalisasi-data-dengan-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/normalisasi-data-dengan-python\/","title":{"rendered":"Cara menormalkan data dengan python"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Seringkali dalam statistik dan pembelajaran mesin kita <strong>menormalkan<\/strong> variabel sedemikian rupa sehingga rentang nilainya antara 0 dan 1.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Alasan paling umum untuk melakukan normalisasi variabel adalah ketika kita melakukan beberapa jenis analisis multivariat (yaitu kita ingin memahami hubungan antara beberapa variabel prediktor dan variabel respons) dan kita ingin setiap variabel memberikan kontribusi yang sama terhadap analisis.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ketika variabel diukur pada skala yang berbeda, seringkali variabel tersebut tidak memberikan kontribusi yang sama terhadap analisis. Misalnya, jika nilai suatu variabel berkisar antara 0 hingga 100.000 dan nilai variabel lain berkisar antara 0 hingga 100, maka variabel dengan rentang yang lebih besar akan diberi bobot yang lebih besar dalam analisis.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Dengan melakukan standarisasi variabel, kita dapat yakin bahwa setiap variabel memberikan kontribusi yang sama terhadap analisis.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Untuk menormalkan nilai antara 0 dan 1, kita dapat menggunakan rumus berikut:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>x <sub>norma<\/sub> = ( <sub>xi<\/sub> \u2013 x <sub>menit<\/sub> ) \/ (x <sub>maks<\/sub> \u2013 x <sub>menit<\/sub> )<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Emas:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>x <sub>norm<\/sub> :<\/strong> nilai normalisasi <sup>ke-i<\/sup> dalam kumpulan data<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>x <sub>i<\/sub> :<\/strong> nilai <sup>ke-i<\/sup> dari kumpulan data<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>x <sub>max<\/sub><\/strong> : Nilai minimum dalam kumpulan data<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>x <sub>min<\/sub> :<\/strong> Nilai maksimum dalam kumpulan data<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Contoh berikut menunjukkan cara menormalkan satu atau lebih variabel dengan Python.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 1: menormalkan array NumPy<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menormalkan semua nilai dalam array NumPy:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np \n\n<span style=\"color: #008080;\">#create NumPy array\n<\/span>data = np. <span style=\"color: #3366ff;\">array<\/span> ([[13, 16, 19, 22, 23, 38, 47, 56, 58, 63, 65, 70, 71]])\n\n<span style=\"color: #008080;\">#normalize all values in array\n<\/span>data_norm = (data - data. <span style=\"color: #3366ff;\">min<\/span> ())\/ (data. <span style=\"color: #3366ff;\">max<\/span> () - data. <span style=\"color: #3366ff;\">min<\/span> ())\n\n<span style=\"color: #008080;\">#view normalized values\n<\/span>data_norm\n\narray([[0. , 0.05172414, 0.10344828, 0.15517241, 0.17241379,\n        0.43103448, 0.5862069, 0.74137931, 0.77586207, 0.86206897,\n        0.89655172, 0.98275862, 1. ]])<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Setiap nilai dalam array yang dinormalisasi sekarang berada di antara 0 dan 1.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 2: Normalisasikan semua variabel di Pandas DataFrame<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menormalkan semua variabel di pandas DataFrame:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n<span style=\"color: #008080;\">#normalize values in every column\n<\/span>df_norm = (df-df. <span style=\"color: #3366ff;\">min<\/span> ())\/ (df. <span style=\"color: #3366ff;\">max<\/span> () - df. <span style=\"color: #3366ff;\">min<\/span> ())\n\n<span style=\"color: #008080;\">#view normalized DataFrame\n<\/span>df_norm\n\n        points assists rebounds\n0 0.764706 0.125 0.857143\n1 0.000000 0.375 0.428571\n2 0.176471 0.375 0.714286\n3 0.117647 0.625 0.142857\n4 0.411765 1.000 0.142857\n5 0.647059 0.625 0.000000\n6 0.764706 0.625 0.571429\n7 1.000000 0.000 1.000000\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Masing-masing nilai di setiap kolom kini berada di antara 0 dan 1.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 3: Normalisasikan variabel tertentu di Pandas DataFrame<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menormalkan variabel tertentu di pandas DataFrame:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n<span style=\"color: #008080;\">define columns to normalize<\/span>\nx = df. <span style=\"color: #3366ff;\">iloc<\/span> [:,0:2]\n\n<span style=\"color: #008080;\">#normalize values in first two columns only<\/span>\ndf. <span style=\"color: #3366ff;\">iloc<\/span> [:,0:2] = (xx. <span style=\"color: #3366ff;\">min<\/span> ())\/ (x. <span style=\"color: #3366ff;\">max<\/span> () - x. <span style=\"color: #3366ff;\">min<\/span> ())\n\n<span style=\"color: #008080;\">#view normalized DataFrame<\/span>\ndf\n\n\tpoints assists rebounds\n0 0.764706 0.125 11\n1 0.000000 0.375 8\n2 0.176471 0.375 10\n3 0.117647 0.625 6\n4 0.411765 1.000 6\n5 0.647059 0.625 5\n6 0.764706 0.625 9\n7 1.000000 0.000 12\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Perhatikan bahwa hanya nilai di dua kolom pertama yang dinormalisasi.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Sumber daya tambahan<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Tutorial berikut memberikan informasi tambahan tentang normalisasi data:<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/id\/menormalkan-data-antara-0-dan-1\/\" target=\"_blank\" rel=\"noopener\">Cara menormalkan data antara 0 dan 1<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/menormalkan-data-antara-0-dan-100\/\" target=\"_blank\" rel=\"noopener\">Cara menormalkan data antara 0 dan 100<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/standardisasi-vs-normalisasi\/\" target=\"_blank\" rel=\"noopener\">Standardisasi atau normalisasi: apa bedanya?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Seringkali dalam statistik dan pembelajaran mesin kita menormalkan variabel sedemikian rupa sehingga rentang nilainya antara 0 dan 1. Alasan paling umum untuk melakukan normalisasi variabel adalah ketika kita melakukan beberapa jenis analisis multivariat (yaitu kita ingin memahami hubungan antara beberapa variabel prediktor dan variabel respons) dan kita ingin setiap variabel memberikan kontribusi yang sama terhadap [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Cara Menormalkan Data dengan Python - Statologi<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara menormalkan data dengan Python, dengan beberapa contoh.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/id\/normalisasi-data-dengan-python\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara Menormalkan Data dengan Python - Statologi\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara menormalkan data dengan Python, dengan beberapa contoh.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/normalisasi-data-dengan-python\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-23T19:27:32+00:00\" \/>\n<meta name=\"author\" content=\"Benjamin anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Ditulis oleh\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimasi waktu membaca\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 menit\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/id\/normalisasi-data-dengan-python\/\",\"url\":\"https:\/\/statorials.org\/id\/normalisasi-data-dengan-python\/\",\"name\":\"Cara Menormalkan Data dengan Python - Statologi\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-23T19:27:32+00:00\",\"dateModified\":\"2023-07-23T19:27:32+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara menormalkan data dengan Python, dengan beberapa contoh.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/normalisasi-data-dengan-python\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/normalisasi-data-dengan-python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/normalisasi-data-dengan-python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menormalkan data dengan python\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/id\/#website\",\"url\":\"https:\/\/statorials.org\/id\/\",\"name\":\"Statorials\",\"description\":\"Panduan anda untuk kompetensi statistik!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/id\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"id\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\",\"name\":\"Benjamin anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"id\",\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/image\/\",\"url\":\"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"Benjamin anderson\"},\"description\":\"Halo, saya Benjamin, pensiunan profesor statistika yang menjadi guru Statorial yang berdedikasi. 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