[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124513-id":3,"doc-seo-124513-113":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},124513,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",54,"Penelitian & Laporan","Penerapan Machine Learning untuk Memprediksi Resiko Pengidap Penyakit Jantung menggunakan Algoritma Decision Tree","Penyakit jantung menjadi penyebab kematian utama sehingga diperlukan cara yang lebih efektif untuk meningkatkan diagnosis dan pengelolaan risiko. Penelitian ini menerapkan machine learning dengan fokus pada algoritma decision tree untuk memprediksi risiko penyakit jantung. Dataset publik berisi 303 entri dengan 14 fitur faktor risiko seperti usia, kadar kolesterol, tekanan darah, dan hasil elektrokardiogram. Data dipraproses melalui normalisasi, penanganan outlier, dan standardisasi. Model dilatih pada 80% data dan diuji pada 20%, memperoleh akurasi 80% serta F1-score 0,82, sehingga interpretabilitas model mendukung penilaian risiko yang lebih transparan dan intervensi dini.","FORMAT: Jurnal Ilmiah Teknik Informatika  \nAkreditasi Sinta 5 RISTEKBRIN No. 164/E/KPT/2021; E-ISSN 2722-7162 | P-ISSN 2089-5615  \nVol. 14, No. 1, January 2025, 73-81. 2025  \nPenerapan Machine Learning untuk memprediksi Resiko Pengidap Penyakit Jantung menggunakan  \nAlgoritma decision tree  \nInna Sabily Karima  \nFakultas Ilmu Komputer, Universitas Mercu Buana, Jl. Meruya Selatan No.1, RT.4/RW.1, Joglo, Kec. Kembangan, Kota Jakarta Barat,  \nDaerah Khusus Ibukota Jakarta 11650  \n[inna.sabily@mercubuana.ac.id](inna.sabily@mercubuana.ac.id)  \nKata kunci:  \nDecision Tree ; risk prediction; medical diagnostics; feature importance.  \nAbstract  \nHeart disease remains a leading cause of mortality worldwide, necessitating innovative approaches to improve diagnosis and management. This study aims to enhance the prediction of heart disease risk using machine learning, particularly the Decision Tree algorithm. A publicly available dataset containing 303 entries with 14 features related to heart disease risk factors, such as age, cholesterol levels, blood pressure, and electrocardiogram results, was utilized. The data underwent preprocessing steps, including normalization, handling outliers, and standardization, to ensure optimal model performance. The Decision Tree algorithm was trained on 80% of the dataset and evaluated on the remaining 20% . The model achieved an accuracy of 80%, with a balanced F1-score of 0.82, demonstrating its effectiveness in predicting heart disease risk. Feature importance analysis revealed that cholesterol levels, age, and resting blood pressure were the most influential predictors. The Decision Tree's interpretability provides valuable insights for medical practitioners, enabling more accurate and transparent risk assessments. This study highlights the potential of machine learning in medical diagnostics, particularly in identifying high-risk individuals for early intervention and better patient outcomes.  \nPendahuluan  \nPenyakit jantung merupakan salah satu penyakit yang menjadi perhatian serius di bidang kesehatan global [1], [2] . Menurut Organisasi Kesehatan Dunia (WHO), penyakit jantung dan pembuluh darah adalah penyebab kematian nomor satu di dunia, menyebabkan sekitar 17,9 juta kematian setiap tahunnya [3], [4] . Di Indonesia, penyakit jantung juga merupakan penyebab kematian utama, menyumbang sekitar 37% dari total kematian [5], [6], [7] .  \nTingginya angka kematian dan prevalensi penyakit jantung ini menunjukkan perlunya peningkatan dalam upaya pencegahan, diagnosis, dan pengelolaan penyakit ini. Namun, prediksi risiko individu terkena penyakit jantung masih menjadi tantangan yang kompleks [8], [9] . Faktor-faktor seperti riwayat kesehatan, gaya hidup, dan genetik dapat mempengaruhi risiko seseorang terkenapenyakit jantung, dan memprediksi risiko secara tepat dan akurat memerlukan analisis yang mendalam dan komprehensif .  \nDalam konteks ini, teknologi Machine Learning menawarkan potensi besar untuk meningkatkan prediksi risiko penyakit jantung [10] . Algoritma Machine Learning, seperti decision tree, memiliki kemampuan untuk memproses dan menganalisis datakesehatan secara cepat dan akurat, sehingga dapat mengidentifikasi pola-pola yang kompleks dan hubungan yang tersembunyi diantara faktor-faktor risiko [11], [12], [13] . Dengan demikian, penggunaan Machine Learning dalam memprediksi risiko penyakit jantung dapat memberikan kontribusi yang signifikan dalam meningkatkan diagnosis dini, pengelolaan penyakit, dan upaya pencegahan [14], [15], [16] .  \nDalam menanggapi permasalahan yang diuraikan sebelumnya, pendekatan pemecahan masalah yang dapat diambil adalah sebagai berikut:  \n• Pengumpulan Data: Langkah pertama adalah mengumpulkan data kesehatan yang relevan dari berbagai sumber, termasuk riwayat medis pasien, data laboratorium, dan faktor-faktor risiko potensial seperti kebiasaan merokok, aktivitas fisik, dan riwayat keluarga.  \n• Persiapan Data: Data yang telah dikumpulkan kemudian akan dipersiapkan","cbCaieGK61htDSGD","https://ap.wps.com/l/cbCaieGK61htDSGD","pdf",655846,4,1,8,"Indonesian","id",113,"# Pendahuluan\n## Tantangan prediksi risiko penyakit jantung\n## Potensi machine learning dan decision tree\n## Kerangka pemecahan masalah\n# Metode Penelitian\n## Tahapan penelitian dengan decision tree\n## Pengumpulan data\n## Persiapan data","[{\"question\":\"Penelitian ini menggunakan algoritma apa untuk memprediksi risiko penyakit jantung?\",\"answer\":\"Penelitian ini menggunakan algoritma decision tree untuk memprediksi risiko pengidap penyakit jantung berdasarkan fitur faktor risiko.\"},{\"question\":\"Bagaimana proses evaluasi model dilakukan pada dataset penelitian?\",\"answer\":\"Data dipraproses lalu dibagi menjadi 80% data pelatihan dan 20% data pengujian. Kinerja dievaluasi menggunakan metrik seperti akurasi dan F1-score, yang dilaporkan mencapai akurasi 80% dan F1-score 0,82.\"},{\"question\":\"Faktor apa yang paling berpengaruh menurut hasil feature importance?\",\"answer\":\"Analisis feature importance menunjukkan bahwa kadar kolesterol, usia, dan tekanan darah istirahat merupakan prediktor yang paling berpengaruh.\"}]","Penerapan Machine Learning untuk Memprediksi Resiko Pengidap Penyakit Jantung menggunakan Algoritma Decision Tree | PDF",1785822844,12,{"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},"application-of-machine-learning-to-predict-heart-disease-risk-using-decision-tree-algorithm","",{"@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/application-of-machine-learning-to-predict-heart-disease-risk-using-decision-tree-algorithm/124513/",{"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},"Penelitian ini menggunakan algoritma apa untuk memprediksi risiko penyakit jantung?","Question",{"text":76,"@type":77},"Penelitian ini menggunakan algoritma decision tree untuk memprediksi risiko pengidap penyakit jantung berdasarkan fitur faktor risiko.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Bagaimana proses evaluasi model dilakukan pada dataset penelitian?",{"text":81,"@type":77},"Data dipraproses lalu dibagi menjadi 80% data pelatihan dan 20% data pengujian. Kinerja dievaluasi menggunakan metrik seperti akurasi dan F1-score, yang dilaporkan mencapai akurasi 80% dan F1-score 0,82.",{"name":83,"@type":74,"acceptedAnswer":84},"Faktor apa yang paling berpengaruh menurut hasil feature importance?",{"text":85,"@type":77},"Analisis feature importance menunjukkan bahwa kadar kolesterol, usia, dan tekanan darah istirahat merupakan prediktor yang paling berpengaruh.","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"]