{"id":1586,"date":"2023-07-25T18:29:30","date_gmt":"2023-07-25T18:29:30","guid":{"rendered":"https:\/\/statorials.org\/id\/tidak-di-sungai\/"},"modified":"2023-07-25T18:29:30","modified_gmt":"2023-07-25T18:29:30","slug":"tidak-di-sungai","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/tidak-di-sungai\/","title":{"rendered":"Cara menghitung auc (area di bawah kurva) di r"},"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 noreferrer\">Regresi logistik<\/a> adalah metode statistik yang kami gunakan untuk menyesuaikan model regresi jika variabel responsnya biner. Untuk mengevaluasi seberapa cocok model regresi logistik dengan kumpulan data, kita dapat melihat dua metrik berikut:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>Sensitivitas:<\/strong> probabilitas model memprediksi hasil positif untuk suatu observasi padahal hasilnya benar-benar positif. Ini juga disebut \u201ctingkat positif sebenarnya\u201d.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>Kekhususan:<\/strong> probabilitas model memprediksi hasil negatif untuk suatu observasi padahal hasilnya sebenarnya negatif. Ini juga disebut \u201ctingkat negatif sebenarnya\u201d.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Salah satu cara untuk memvisualisasikan kedua pengukuran ini adalah dengan membuat <strong>kurva ROC<\/strong> , yang merupakan singkatan dari kurva \u201ckarakteristik pengoperasian penerima\u201d.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ini adalah grafik yang menampilkan sensitivitas sepanjang sumbu y dan (1 \u2013 spesifisitas) sepanjang sumbu x. Salah satu cara untuk mengukur efektivitas model regresi logistik dalam mengklasifikasikan data adalah dengan menghitung <strong>AUC<\/strong> , yang merupakan singkatan dari &#8220;area di bawah kurva&#8221;.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Semakin dekat AUC ke 1, maka semakin baik model tersebut.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Contoh langkah demi langkah berikut menunjukkan cara menghitung AUC untuk model regresi logistik di R.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 1: Muat data<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Pertama, kita akan memuat dataset <strong>default<\/strong> dari paket <strong>ISLR<\/strong> , yang berisi informasi tentang apakah beberapa orang telah gagal membayar pinjamannya atau tidak.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load dataset\n<\/span>data &lt;- ISLR::Default\n\n<span style=\"color: #008080;\">#view first six rows of dataset\n<\/span>head(data)\n\n  default student balance income\n1 No No 729.5265 44361.625\n2 No Yes 817.1804 12106.135\n3 No No 1073.5492 31767.139\n4 No No 529.2506 35704.494\n5 No No 785.6559 38463.496\n6 No Yes 919.5885 7491.559\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 2: Sesuaikan model regresi logistik<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Selanjutnya, kami akan menyesuaikan model regresi logistik untuk memprediksi kemungkinan seseorang akan gagal bayar:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#make this example reproducible\n<\/span>set. <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#Use 70% of dataset as training set and remaining 30% as testing set\n<\/span>sample &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)<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 3: Hitung Model AUC<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Selanjutnya, kita akan menggunakan fungsi <strong>auc()<\/strong> dari paket <strong>pROC<\/strong> untuk menghitung AUC model. Fungsi ini menggunakan sintaks berikut:<\/span><\/p>\n<p> <strong><span style=\"color: #000000;\">tidak ada (respon, prediksi)<\/span><\/strong><\/p>\n<p> <span style=\"color: #000000;\">Berikut cara menggunakan fungsi ini dalam contoh kita:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate probability of default for each individual in test dataset\n<\/span>predicted &lt;- predict(model, test, type=\" <span style=\"color: #008000;\">response<\/span> \")\n\n<span style=\"color: #008080;\">#calculate AUC\n<\/span><span style=\"color: #993300;\">library<\/span> (pROC)\nauc(test$default, predicted)\n\nSetting levels: control = No, case = Yes\nSetting direction: controls &lt; boxes\nArea under the curve: 0.9437\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">AUC model tersebut ternyata <strong>0,9437<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Karena nilai ini mendekati 1, hal ini menunjukkan bahwa model tersebut mampu memprediksi dengan baik apakah seseorang akan gagal membayar pinjamannya atau tidak.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Regresi logistik adalah metode statistik yang kami gunakan untuk menyesuaikan model regresi jika variabel responsnya biner. Untuk mengevaluasi seberapa cocok model regresi logistik dengan kumpulan data, kita dapat melihat dua metrik berikut: Sensitivitas: probabilitas model memprediksi hasil positif untuk suatu observasi padahal hasilnya benar-benar positif. Ini juga disebut \u201ctingkat positif sebenarnya\u201d. Kekhususan: probabilitas model memprediksi [&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 menghitung AUC (area di bawah kurva) di R - Statorials<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara menghitung AUC (area under curve) 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\/tidak-di-sungai\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara menghitung AUC (area di bawah kurva) di R - Statorials\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara menghitung AUC (area under curve) di R, termasuk contoh langkah demi langkah.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/tidak-di-sungai\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-25T18:29:30+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\/tidak-di-sungai\/\",\"url\":\"https:\/\/statorials.org\/id\/tidak-di-sungai\/\",\"name\":\"Cara menghitung AUC (area di bawah kurva) di R - Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-25T18:29:30+00:00\",\"dateModified\":\"2023-07-25T18:29:30+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara menghitung AUC (area under curve) di R, termasuk contoh langkah demi langkah.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/tidak-di-sungai\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/tidak-di-sungai\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/tidak-di-sungai\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menghitung auc (area di bawah kurva) di r\"}]},{\"@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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