{"id":1576,"date":"2023-07-25T19:42:15","date_gmt":"2023-07-25T19:42:15","guid":{"rendered":"https:\/\/statorials.org\/id\/kebingungan-matriks-di-r\/"},"modified":"2023-07-25T19:42:15","modified_gmt":"2023-07-25T19:42:15","slug":"kebingungan-matriks-di-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/kebingungan-matriks-di-r\/","title":{"rendered":"Cara membuat matriks kebingungan di r (langkah demi langkah)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/id\/regresi-logistik-1\/\" target=\"_blank\" rel=\"noopener\">Regresi logistik<\/a> adalah jenis regresi yang dapat kita gunakan jika variabel responnya adalah biner.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Cara umum untuk menilai kualitas model regresi logistik adalah dengan membuat <strong>matriks konfusi<\/strong> , yaitu tabel berukuran 2 \u00d7 2 yang menunjukkan nilai prediksi model versus nilai sebenarnya dari kumpulan data pengujian.<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-15654 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/confusionr1.png\" alt=\"\" width=\"292\" height=\"129\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Contoh langkah demi langkah berikut menunjukkan cara membuat matriks konfusi di R.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 1: Sesuaikan model regresi logistik<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Untuk contoh ini, kita akan menggunakan dataset <strong>default<\/strong> dari paket <strong>ISLR<\/strong> . Kami akan menggunakan status pelajar, saldo bank, dan pendapatan tahunan untuk memprediksi kemungkinan seseorang akan gagal membayar pinjamannya.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menyesuaikan model regresi logistik dengan kumpulan data ini:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load necessary packages\n<\/span><span style=\"color: #993300;\">library<\/span> (caret)\n<span style=\"color: #993300;\">library<\/span> (InformationValue)\n<span style=\"color: #993300;\">library<\/span> (ISLR)\n\n<span style=\"color: #008080;\">#load dataset\n<\/span>data &lt;-Default\n\n<span style=\"color: #008080;\">#split dataset into training and testing set\n<\/span>set. <span style=\"color: #3366ff;\">seeds<\/span> (1)\nsample &lt;- sample(c(TRUE, FALSE), nrow(data), replace= <span style=\"color: #008000;\">TRUE<\/span> , prob=c(0.7,0.3))\ntrain &lt;- data[sample, ]\ntest &lt;- data[!sample, ]\n\n<span style=\"color: #008080;\">#fit logistic regression model\n<\/span>model &lt;- glm(default~student+balance+income, family=\" <span style=\"color: #008000;\">binomial<\/span> \", data=train)\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 2: Buat Matriks Kebingungan<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Selanjutnya, kita akan menggunakan fungsi <strong>ConfusionMatrix()<\/strong> dari paket <strong>caret<\/strong> untuk membuat matriks Confusion:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#use model to predict probability of default\n<span style=\"color: #000000;\">predicted &lt;- predict(model, test, type=\"response\")\n<\/span>\n#convert defaults from \"Yes\" and \"No\" to 1's and 0's\n<span style=\"color: #000000;\">test$default &lt;- ifelse(test$default==\" <span style=\"color: #008000;\">Yes<\/span> \", 1, 0)\n<\/span>\n#find optimal cutoff probability to use to maximize accuracy\n<span style=\"color: #000000;\">optimal &lt;- optimalCutoff(test$default, predicted)[1]\n<\/span>\n#create confusion matrix\n<span style=\"color: #000000;\">confusionMatrix(test$default, predicted)\n\n     0 1\n0 2912 64\n1 21 39\n<\/span><\/span><\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 3: Evaluasi matriks kebingungan<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kami juga dapat menghitung metrik berikut menggunakan matriks konfusi:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>Sensitivitas:<\/strong> \u201cTingkat kepositifan sebenarnya\u201d \u2013 persentase individu yang diprediksi dengan tepat oleh model akan mengalami gagal bayar.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>Kekhususan:<\/strong> \u201ctingkat negatif sebenarnya\u201d \u2013 persentase individu yang diprediksi dengan tepat oleh model <em>tidak<\/em> akan gagal bayar.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>Tingkat kesalahan klasifikasi total:<\/strong> Persentase total kesalahan klasifikasi yang dilakukan oleh model.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menghitung metrik ini:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#calculate sensitivity<\/span>\nsensitivity(test$default, predicted)\n\n[1] 0.3786408\n\n<span style=\"color: #008080;\">#calculate specificity\n<\/span>specificity(test$default, predicted)\n\n[1] 0.9928401\n\n<span style=\"color: #008080;\">#calculate total misclassification error rate\n<\/span>misClassError(test$default, predicted, <span style=\"color: #3366ff;\">threshold<\/span> =optimal)\n\n[1] 0.027<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Tingkat kesalahan klasifikasi total adalah <strong>2,7%<\/strong> untuk model ini.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Secara umum, semakin rendah angka ini, semakin baik model tersebut dalam memprediksi hasil. Oleh karena itu, model khusus ini terbukti sangat efektif dalam memprediksi apakah seseorang akan gagal bayar atau tidak.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Regresi logistik adalah jenis regresi yang dapat kita gunakan jika variabel responnya adalah biner. Cara umum untuk menilai kualitas model regresi logistik adalah dengan membuat matriks konfusi , yaitu tabel berukuran 2 \u00d7 2 yang menunjukkan nilai prediksi model versus nilai sebenarnya dari kumpulan data pengujian. Contoh langkah demi langkah berikut menunjukkan cara membuat matriks [&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 Membuat Matriks Kebingungan di R (Langkah demi Langkah)<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara membuat matriks konfusi di R, termasuk contoh langkah demi langkah.\" \/>\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\/kebingungan-matriks-di-r\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara Membuat Matriks Kebingungan di R (Langkah demi Langkah)\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara membuat matriks konfusi di R, termasuk contoh langkah demi langkah.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/kebingungan-matriks-di-r\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-25T19:42:15+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/confusionr1.png\" \/>\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\/kebingungan-matriks-di-r\/\",\"url\":\"https:\/\/statorials.org\/id\/kebingungan-matriks-di-r\/\",\"name\":\"Cara Membuat Matriks Kebingungan di R (Langkah demi Langkah)\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-25T19:42:15+00:00\",\"dateModified\":\"2023-07-25T19:42:15+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara membuat matriks konfusi di R, termasuk contoh langkah demi langkah.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/kebingungan-matriks-di-r\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/kebingungan-matriks-di-r\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/kebingungan-matriks-di-r\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara membuat matriks kebingungan di r (langkah demi langkah)\"}]},{\"@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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