[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121524-id":3,"doc-seo-121524-113":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},121524,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",54,"Penelitian & Laporan","Performance Analysis and Accuracy of Machine Learning Algorithms for Heart Disease Prediction - Evaluasi Kinerja dan Akurasi Algoritma Machine Learning untuk Prediksi Penyakit Jantung","Penelitian ini mengevaluasi kinerja dan akurasi berbagai algoritma machine learning untuk memprediksi penyakit jantung sebagai masalah kesehatan utama penyebab kematian. Pendekatan menggunakan dataset publik catatan medis pasien melalui tahapan pengumpulan data, Exploratory Data Analysis (EDA), pelatihan model, dan evaluasi performa. Hasil menunjukkan Random Forest memperoleh akurasi tertinggi 90,16%, diikuti Logistic Regression dan Naive Bayes (85,25%). KNN mencatat akurasi terendah 67,21%, sehingga algoritma tertentu berpotensi mendukung deteksi dini di bidang kesehatan.","Performance Analysis and Accuracy of Machine Learning Algorithms for Heart Disease Prediction  \nEvaluasi Kinerja dan Akurasi Algoritma Machine Learning untuk Prediksi Penyakit Jantung  \nSilpani Yuliasari, Alam Rahmatulloh*  \nInformatika, Fakultas Teknik, Universitas Siliwangi  \n[217006101@student.unsil.ac.id](217006101@student.unsil.ac.id), [alam@unsil.ac.id](alam@unsil.ac.id)  \nInformasi Artikel  \nReceived: November 2025  \nRevised: November 2025  \nAccepted: October 2025  \nPublished: October 2025  \nKeywords: Heart Disease Prediction; K-Nearest Neighbors; Logistic Regression; Neural Network; Random Forest  \nKata kunci: K-Nearest Neighbors; Neural Network; Prediksi Penyakit Jantung; Random Forest; Regresi Logistik;  \nAbstract  \nPurpose: This research aims to analyze the performance and accuracy of machine learning algorithms in predicting heart disease, which is a cause of death throughout the world.  \nDesign/methodology/approach: The algorithms analyzed include Logistic Regression, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, XGBoost, and Neural Network. A publicly available dataset containing patients' medical records was utilized, with the methodology encompassing data collection, Exploratory Data Analysis (EDA), model training, and performance evaluation.  \nFindings/result: The results indicate that the Random Forest algorithm achieved the highest accuracy with an accuracy of 90.16%, followed by Logistic Regression and Naive Bayes with accuracies of 85.25%. The K-Nearest Neighbors algorithm exhibits the lowest accuracy at 67.21%. Originality/value/state of the art: This research highlights the advantages of certain machine learning algorithms in predicting heart disease and contributes knowledge to early detection technology in the health sector.  \nAbstrak  \nTujuan: Penelitian ini bertujuan untuk mengevaluasiefektivitas berbagai algoritma machine learning dalam memprediksi penyakit jantung yang menjadi salah satu masalah kesehatan utama di dunia.  \nPerancangan/metode/pendekatan: Algoritma yang dibandingkan meliputi Logistic Regression, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, XGBoost, dan Neural Network.  \n Dataset publik yang berisi catatan medis pasien digunakan   \ndalam penelitian ini, dengan metodologi yang mencakuppengumpulan data, Exploratory Data Analysis (EDA), pelatihan model, dan evaluasi kinerja.  \nHasil: Hasil menunjukkan bahwa algoritma Random Forest mencapai akurasi tertinggi dengan akurasi 90.16%, diikuti Logistic Regression dan Naive Bayes dengan akurasi 85.25% . Algoritma K-Nearest Neighbors menunjukkanakurasi terendah sebesar 67.21% .  \nKeaslian/ state of the art: Penelitian ini menyoroti keunggulan algoritma machine learning tertentu dalamprediksi penyakit jantung serta memberikan kontribusipengetahuan bagi teknologi deteksi dini di bidang kesehatan.  \n1. Pendahuluan  \nPenyakit jantung merupakan salah satu topik kesehatan yang paling krusial dikarenakan tingginya tingkat mortalitas yang diakibatkan olehnya. Data dari World Health Organization (WHO) memaparkan bahwa penyakit kardiovaskular, termasuk hipertensi, penyakit jantung, dan stroke, berkontribusi hingga 45% terhadap total kematian di seluruh dunia [1] . Faktor seperti gaya hidup tidak sehat, rendahnya aktivitas fisik, dan konsumsi makanan tinggi lemak serta gulamenjadi pemicu utama peningkatan prevalensi penyakit ini dalam beberapa tahun terakhir [2] . Oleh karena itu, diperlukan metode yang efektifatau efisien untuk mendeteksi penyakit jantung secara dini untuk meningkatkan peluang keberhasilan pengobatan dan mengurangi angkakematian.  \nPenggunaan machine learning dalam bidang kesehatan berkembang pesat sebagai solusiinovatif untuk berbagai tantangan klinis, termasuk deteksi dini penyakit [3], [4] . Algoritmaseperti Logistic Regression [5], Support Vector Machine (SVM) [6],[7], Decision Tree [8], dan Random Forest [9], [10] telah banyak diterapkan untuk mengan","cbCaibW3vfrOgz6k","https://ap.wps.com/l/cbCaibW3vfrOgz6k","pdf",346927,3,1,9,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang dan urgensi deteksi dini\n# Metode\n## Data collection\n## Exploratory Data Analysis (EDA)\n## Pelatihan model dan evaluasi kinerja\n# Hasil dan Pembahasan\n## Perbandingan akurasi antar algoritma\n# Kesimpulan","[{\"question\":\"Algoritma apa saja yang dibandingkan dalam penelitian ini untuk prediksi penyakit jantung?\",\"answer\":\"Penelitian membandingkan Logistic Regression, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, XGBoost, dan Neural Network.\"},{\"question\":\"Bagaimana dataset yang digunakan dalam penelitian diperoleh?\",\"answer\":\"Dataset yang dipakai merupakan kumpulan data publik untuk prediksi penyakit jantung yang bersumber dari Kaggle dan berisi atribut informasi pasien.\"},{\"question\":\"Algoritma mana yang mencapai akurasi tertinggi dan terendah?\",\"answer\":\"Random Forest mencapai akurasi tertinggi 90,16%, sedangkan K-Nearest Neighbors menunjukkan akurasi terendah 67,21%.\"}]","Performance Analysis and Accuracy of Machine Learning Algorithms for Heart Disease Prediction - Evaluasi Kinerja dan Akurasi Algoritma Machine Learning untuk Prediksi Penyakit Jantung | PDF",1785736082,14,{"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},"performance-analysis-and-accuracy-of-machine-learning-algorithms-for-heart-disease-prediction-evaluation-of-performance-and-accuracy-of-machine-learning-algorithms-for-heart-disease-prediction","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"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":20},"https://docshare.wps.com/id/document/penelitian-laporan/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/performance-analysis-and-accuracy-of-machine-learning-algorithms-for-heart-disease-prediction-evaluation-of-performance-and-accuracy-of-machine-learning-algorithms-for-heart-disease-prediction/121524/",4,{"url":52,"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","2026-08-16","2026-08-03",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},"Algoritma apa saja yang dibandingkan dalam penelitian ini untuk prediksi penyakit jantung?","Question",{"text":76,"@type":77},"Penelitian membandingkan Logistic Regression, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, XGBoost, dan Neural Network.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Bagaimana dataset yang digunakan dalam penelitian diperoleh?",{"text":81,"@type":77},"Dataset yang dipakai merupakan kumpulan data publik untuk prediksi penyakit jantung yang bersumber dari Kaggle dan berisi atribut informasi pasien.",{"name":83,"@type":74,"acceptedAnswer":84},"Algoritma mana yang mencapai akurasi tertinggi dan terendah?",{"text":85,"@type":77},"Random Forest mencapai akurasi tertinggi 90,16%, sedangkan K-Nearest Neighbors menunjukkan akurasi terendah 67,21%.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"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"]