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All Journal Bina Insani ICT Journal Insect (Informatics and Security) : Jurnal Teknik Informatika JURNAL MAHAJANA INFORMASI Journal of Robotics and Control (JRC) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Abdimas: Papua Journal Of Community Service Jurnal Media Informatika TRANSFORMASI : JURNAL PENGABDIAN PADA MASYARAKAT Journal of Industrial System Engineering and Management AMMA : Jurnal Pengabdian Masyarakat
Muzakkir Pangri
Universitas Muhammadiyah Sorong
Aerospace Engineering Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Social Sciences Transportation Other

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Analisis Komparatif Kinerja Algoritma Support Vector Machine (SVM) dan LightGBM untuk Klasifikasi Penyakit Jantung Pangri, Muzakkir; Yusuf, Muhammad; Sari, Indah Purnama; N, Waode Faizah Zahra
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 1 (2026): JANUARY 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i1.4900

Abstract

Heart disease remains one of the leading causes of death worldwide, making early detection and accurate classification essential to mitigating greater health risks. This study presents a comparative analysis of two machine learning algorithms Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM) for heart disease classification. The dataset used is the UCI Heart Disease dataset, comprising 920 patient records. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the SVM model achieved the highest accuracy at 84%, along with an F1-score of 86% for the positive class and a recall of 92%. In contrast, LightGBM demonstrated balanced performance, achieving 83% accuracy and an F1-score of 85%. These findings suggest that SVM holds a slight advantage in identifying heart disease cases, particularly in minimizing false negatives an aspect that is critical yet often overlooked in prior comparative studies.
Co-Authors Ahistasari, Asih Aras, Suhardi Asno Azzawagama Firdaus Baehaqi Daniel Santoso Dewi Astria Faroek Elisabeth Serlina Elensia Bupu Evandi, Petrus Fajar Rahardika Bahari Putra Fajar, Fajar Rahardika Bahari Putra Fitryani Tella Haris Haris, Haris Ibnu Subrata Ajid Irnawati Ismail Sangadji Kamarudin, Karlina Maipauw, Niny J. Mariana Anggraini Suwardi Ningsih Masniar Muhajir Yunus Muhammad Jundullah Muhammad Jundullah Muhammad Rizki Setyawan Muhammad Yusuf Musiin, Faujan D.Z N, Waode Faizah Zahra Nur Hidaya Nur Miswar Rusydi Umar Sunardi, Sunardi Surahmanto Wader, Elisabeth Weyai, Saldriani R M