{"id":2498,"date":"2023-07-22T00:36:10","date_gmt":"2023-07-22T00:36:10","guid":{"rendered":"https:\/\/statorials.org\/id\/matriks-normalisasi-numpy\/"},"modified":"2023-07-22T00:36:10","modified_gmt":"2023-07-22T00:36:10","slug":"matriks-normalisasi-numpy","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/matriks-normalisasi-numpy\/","title":{"rendered":"Cara menormalkan matriks numpy: beserta contoh"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Normalisasi<\/strong> suatu matriks berarti menskalakan nilai sedemikian rupa sehingga rentang nilai baris atau kolom berada antara 0 dan 1.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Cara termudah untuk menormalkan nilai matriks NumPy adalah dengan menggunakan fungsi <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.preprocessing.normalize.html\" target=\"_blank\" rel=\"noopener\">normalize()<\/a> dari paket sklearn, yang menggunakan sintaks dasar berikut:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">preprocessing<\/span> <span style=\"color: #008000;\">import<\/span> normalize\n\n<span style=\"color: #008080;\">#normalize rows of matrix\n<\/span>normalize(x, axis= <span style=\"color: #008000;\">1<\/span> , norm=' <span style=\"color: #ff0000;\">l1<\/span> ')\n\n<span style=\"color: #008080;\">#normalize columns of matrix\n<span style=\"color: #000000;\">normalize(x, axis= <span style=\"color: #008000;\">0<\/span> , norm=' <span style=\"color: #ff0000;\">l1<\/span> ')<\/span>\n<\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Contoh berikut menunjukkan cara menggunakan sintaksis ini dalam praktiknya.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 1: normalisasi baris matriks NumPy<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Misalkan kita memiliki matriks NumPy berikut:<\/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;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#create matrix\n<\/span>x = np. <span style=\"color: #3366ff;\">arange<\/span> (0, 36, 4). <span style=\"color: #3366ff;\">reshape<\/span> (3,3)\n\n<span style=\"color: #008080;\">#view matrix\n<\/span><span style=\"color: #008000;\">print<\/span> (x)\n\n[[ 0 4 8]\n [12 16 20]\n [24 28 32]]\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menormalkan baris matriks NumPy:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">preprocessing<\/span> <span style=\"color: #008000;\">import<\/span> normalize\n\n<span style=\"color: #008080;\">#normalize matrix by rows\n<\/span>x_normed = normalize(x, axis= <span style=\"color: #008000;\">1<\/span> , norm=' <span style=\"color: #ff0000;\">l1<\/span> ')\n\n<span style=\"color: #008080;\">#view normalized matrix\n<\/span><span style=\"color: #008000;\">print<\/span> (x_normed)\n\n[[0. 0.33333333 0.66666667]\n [0.25 0.33333333 0.41666667]\n [0.28571429 0.33333333 0.38095238]]<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Perhatikan bahwa nilai di setiap baris sekarang berjumlah satu.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Jumlah baris pertama: 0 + 0,33 + 0,67 = <strong>1<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">Jumlah baris kedua: 0,25 + 0,33 + 0,417 = <strong>1<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">Jumlah baris ketiga: 0,2857 + 0,3333 + 0,3809 = <strong>1<\/strong><\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 2: normalisasi kolom matriks NumPy<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Misalkan kita memiliki matriks NumPy berikut:<\/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;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#create matrix\n<\/span>x = np. <span style=\"color: #3366ff;\">arange<\/span> (0, 36, 4). <span style=\"color: #3366ff;\">reshape<\/span> (3,3)\n\n<span style=\"color: #008080;\">#view matrix\n<\/span><span style=\"color: #008000;\">print<\/span> (x)\n\n[[ 0 4 8]\n [12 16 20]\n [24 28 32]]\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menormalkan baris matriks NumPy:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">preprocessing<\/span> <span style=\"color: #008000;\">import<\/span> normalize\n\n<span style=\"color: #008080;\">#normalize matrix by columns\n<\/span>x_normed = normalize(x, axis= <span style=\"color: #008000;\">0<\/span> , norm=' <span style=\"color: #ff0000;\">l1<\/span> ')\n\n<span style=\"color: #008080;\">#view normalized matrix\n<\/span><span style=\"color: #008000;\">print<\/span> (x_normed)\n\n[[0. 0.08333333 0.13333333]\n [0.33333333 0.33333333 0.33333333]\n [0.66666667 0.58333333 0.53333333]]<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Perhatikan bahwa nilai di setiap kolom sekarang berjumlah satu.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Jumlah kolom pertama: 0 + 0,33 + 0,67 = <strong>1<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">Jumlah kolom kedua: 0,083 + 0,333 + 0,583 = <strong>1<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">Jumlah kolom ketiga: 0,133 + 0,333 + 0,5333 = <strong>1<\/strong><\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>Sumber daya tambahan<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Tutorial berikut menjelaskan cara melakukan operasi umum lainnya dengan Python:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/id\/normalisasi-data-dengan-python\/\" target=\"_blank\" rel=\"noopener\">Cara menormalkan array dengan Python<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/menormalkan-kolom-kerangka-data-panda\/\" target=\"_blank\" rel=\"noopener\">Cara menormalkan kolom di Pandas DataFrame<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Normalisasi suatu matriks berarti menskalakan nilai sedemikian rupa sehingga rentang nilai baris atau kolom berada antara 0 dan 1. Cara termudah untuk menormalkan nilai matriks NumPy adalah dengan menggunakan fungsi normalize() dari paket sklearn, yang menggunakan sintaks dasar berikut: from sklearn. preprocessing import normalize #normalize rows of matrix normalize(x, axis= 1 , norm=&#8217; l1 &#8216;) [&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 matriks NumPy (dengan contoh) \u2013 Statologi<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara menormalkan matriks NumPy, 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\/matriks-normalisasi-numpy\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara menormalkan matriks NumPy (dengan contoh) \u2013 Statologi\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara menormalkan matriks NumPy, dengan beberapa contoh.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/matriks-normalisasi-numpy\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-22T00:36:10+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=\"1 menit\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/id\/matriks-normalisasi-numpy\/\",\"url\":\"https:\/\/statorials.org\/id\/matriks-normalisasi-numpy\/\",\"name\":\"Cara menormalkan matriks NumPy (dengan contoh) \u2013 Statologi\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-22T00:36:10+00:00\",\"dateModified\":\"2023-07-22T00:36:10+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara menormalkan matriks NumPy, dengan beberapa contoh.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/matriks-normalisasi-numpy\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/matriks-normalisasi-numpy\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/matriks-normalisasi-numpy\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menormalkan matriks numpy: beserta contoh\"}]},{\"@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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