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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Ardila, Niki
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Computer Science & IT

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APPLICATION OF RANDOM FOREST ALGORITHM FOR ARRHYTHMIA DETECTION BASED ON ELECTROCARDIOGRAM DATA Turnip, Mardi; Situmorang, Fransido; William, David; Patterson, Jennifer; Ardila, Niki
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7136

Abstract

Arrhythmia is a common cardiac disorder that requires early detection to prevent serious complications. This study applied the Random Forest algorithm to enhance electrocardiogram (ECG) analysis and enable accurate arrhythmia classification. Unlike prior studies that focused primarily on resting ECG signals, this research incorporated dynamic data collected from 26 participants performing three physical activities for three minutes each, capturing physiological variations across multiple activity states. The Random Forest model was constructed and evaluated using ECG-derived temporal and morphological features to detect potential arrhythmias. Experimental results showed that the model achieved an accuracy of 97.4%, with precision, recall, and F1-score each reaching 98%, and an AUC of 0.97. However, several limitations remain, including the relatively small and homogeneous sample, as well as the short recording duration. Nonetheless, the proposed approach demonstrates strong potential to support early cardiac screening and real-time monitoring, particularly in portable and resource-limited healthcare applications
Co-Authors Mardi Turnip, Mardi Patterson, Jennifer Situmorang, Fransido William, David