[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123478-id":3,"doc-seo-123478-113":31,"detail-sidebar-cat-0-id-113":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},123478,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",54,"Penelitian & Laporan","Perbandingan Algoritma Machine Learning Menggunakan Pemilihan Fitur Chi-square dalam Pengklasifikasian Penyakit Jantung - Artikel","Penyakit jantung merupakan salah satu penyebab kematian tertinggi di seluruh dunia, dengan gejala yang sering tidak langsung menimbulkan dampak berat sehingga antisipasi dini menjadi krusial. Penelitian ini membangun sistem untuk mengidentifikasi penyebab/fitur utama agar faktor berpengaruh dapat diminimalkan. Metode Chi-square digunakan untuk pemilihan fitur dan dibandingkan pada K-Nearest Neighbor, Naïve Bayes, Logistic Regression, Support Vector Machine, serta Random Forest. Hasil menunjukkan delapan fitur terpilih memberikan akurasi tertinggi 93,51% pada KNN, lebih baik daripada memakai semua fitur, sehingga meningkatkan akurasi dan efisiensi klasifikasi.","ISSN 2088-1770 (Print) ISSN 2503-3247 (Online)  \nVol. 15 No.1 (2025) 15-28  \nPerbandingan Algoritma Machine Learning Menggunakan Pemilihan Fitur Chi-square dalam Pengklasifikasian Penyakit  \nJantung  \n*Hirmayanti 1, Ema Utami2  \n1,2)Magister Teknik Informatika, Universitas Amikom Yogyakarta  \nJl. Ring Road Utara, Condong Catur, Sleman, Yogyakarta  \n[Email:](Email:1 hirmayanti@students.amikom.ac.id)[1](Email:1 hirmayanti@students.amikom.ac.id)[ hirmayanti@students.amikom.ac.id](Email:1 hirmayanti@students.amikom.ac.id), [2](2 ema.u@amikom.ac.id)[ ema.u@amikom.ac.id](2 ema.u@amikom.ac.id)  \nABSTRACT  \nHeart disease is one of the deadliest diseases worldwide. This condition often presents symptoms that do not immediately cause severe effects on the sufferer, making early anticipation crucial. To reduce fatalities caused by heart disease or cardiovascular disorders, a system is required to identify its primary causes so that these factors can be minimized. Therefore, this study applies the Chi-square feature selection method to determine the key features influencing the accuracy of Machine Learning models. A comparison is conducted between K-Nearest Neighbor, Naïve Bayes, Logistic Regression, Support Vector Machine, and Random Forest algorithms. This comparison aims to obtain the most accurate results, as a higher algorithm accuracy leads to a more precise classification system for heart disease. The study’s findings indicate that eight key features selected using the Chi-square method yield the highest accuracy, specifically 93.51% with the KNN algorithm. These results demonstrate that using relevant features improves classification accuracy and system efficiency compared to utilizing all available features. Consequently, this research contributes to the selection of essential features in Machine Learning algorithms through the Chi-square technique, ensuring a more effective and optimized heart disease classification system.  \nKeywords : heart disease; cardiovascular; feature selection; chi-square; hyperparameter  \nABSTRAK  \nPenyakit jantung termasuk penyakit yang mematikan di seluruh dunia. Penyakit ini seringkali gejalanya tidak langsung memberikan dampak yang begitu parah terhadap si penderita, oleh karena itu sangat perlu untuk diantisipasi. Untuk mengurangi korban akibat penyakit jantung atau cardiovasculas ini, dibutuhkan adanya sistem yang mampu mengidentifikasi penyebab utama dari penyakit ini sehingga faktoratau penyebab tersebut bisa diminimalisir. Oleh karena itu, pada penelitian ini menggunakan featureselection Chi-square untuk memilih fitur utama yang berpengaruh terhadap akurasi model Machine Learning, dengan membandingkan algoritma K-Nearest Neighbor, Naïve Bayes, Logistic Regression, Support Vector Machine, dan Random Forest. Perbandingan ini dilakukan untuk memperoleh hasil yang akurat, semakin tinggi akurasi algoritma maka semakin tinggi pula keakuratan sistem yang dihasilkandalam mengklasifikasikan penyakit jantung. Berdasarkan hasil penelitian, diketahui bahwa ada 8 fitur utama dari Chi-square yang menghasilkan akurasi tertinggi yaitu 93.51% dari algoritma KNN. Berdasarkan hasil penelitian ini, penggunaan fitur relevan atau utama mampu menghasilkan sistem yang akurat danefisien dalam mengklasifikasikan penyakit jantung, bila dibandingkan dengan menggunakan semua fitur. Oleh karena itu, penelitian ini berkontribusi pada pemilihan fitur penting dalam algoritma Machine Learning melalui teknik Chi-square, yang memastikan sistem klasifikasi penyakit jantung yang lebih efektif dan optimal.  \nKata kunci : penyakit jantung; cardiovascular; pemilihan fitur; chi-square; hyperparameter  \n1. PENDAHULUAN  \nPenyakit jantung merupakan salah satu penyakit yang banyak memakan korban di seluruh dunia (Khan et al., 2023) . Diperkirakan sekitar 17,9 juta orang yang menderita penyakit cardiovascular berdasarkan data dari WHO (World Health Organization, 2021) . Penyakit jantung atau cardiovascular tidak langsung menyerang atau memb","cbCailskNoynlVVs","https://ap.wps.com/l/cbCailskNoynlVVs","pdf",616583,4,1,15,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang penyakit jantung\n## Penelitian terkait prediksi penyakit jantung\n## Kesenjangan penelitian dan kebutuhan pemilihan fitur","[{\"question\":\"Mengapa pemilihan fitur penting dalam klasifikasi penyakit jantung pada penelitian ini?\",\"answer\":\"Pemilihan fitur membantu meningkatkan akurasi dan efisiensi dibanding memakai semua fitur. Tanpa pemilihan yang tepat, proses klasifikasi dapat kurang efektif dan lebih berpotensi menghasilkan kesalahan.\"},{\"question\":\"Metode apa yang digunakan untuk menentukan fitur utama?\",\"answer\":\"Penelitian menggunakan metode pemilihan fitur Chi-square untuk mengidentifikasi fitur yang paling berpengaruh terhadap akurasi model.\"},{\"question\":\"Algoritma mana yang menghasilkan akurasi tertinggi dan berapa nilainya?\",\"answer\":\"Algoritma K-Nearest Neighbor (KNN) menghasilkan akurasi tertinggi sebesar 93,51% menggunakan delapan fitur terpilih dari metode Chi-square.\"}]","Perbandingan Algoritma Machine Learning Menggunakan Pemilihan Fitur Chi-square dalam Pengklasifikasian Penyakit Jantung - Artikel | PDF",1785816743,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"comparison-of-machine-learning-algorithms-using-chi-square-feature-selection-in-heart-disease-classification-article","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/id/document/comparison-of-machine-learning-algorithms-using-chi-square-feature-selection-in-heart-disease-classification-article/123478/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-15","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Mengapa pemilihan fitur penting dalam klasifikasi penyakit jantung pada penelitian ini?","Question",{"text":76,"@type":77},"Pemilihan fitur membantu meningkatkan akurasi dan efisiensi dibanding memakai semua fitur. Tanpa pemilihan yang tepat, proses klasifikasi dapat kurang efektif dan lebih berpotensi menghasilkan kesalahan.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Metode apa yang digunakan untuk menentukan fitur utama?",{"text":81,"@type":77},"Penelitian menggunakan metode pemilihan fitur Chi-square untuk mengidentifikasi fitur yang paling berpengaruh terhadap akurasi model.",{"name":83,"@type":74,"acceptedAnswer":84},"Algoritma mana yang menghasilkan akurasi tertinggi dan berapa nilainya?",{"text":85,"@type":77},"Algoritma K-Nearest Neighbor (KNN) menghasilkan akurasi tertinggi sebesar 93,51% menggunakan delapan fitur terpilih dari metode Chi-square.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]