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All Journal Journal of Innovative and Creativity
Devanta Abraham Tarigan
Politeknik Negeri Medan
Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : Journal of Innovative and Creativity

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Implementasi dan Perbandingan Algoritma Random Forest dan Support Vector Machine untuk Prediksi Penyakit Jantung menggunakan Dataset Heart Disease UCI Devanta Abraham Tarigan; Zakaria Sembiring; Efrata Tarigan; Alexander Sebayang
Journal of Innovative and Creativity Vol. 5 No. 3 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i3.6942

Abstract

Heart disease is a leading cause of death in Indonesia, with a national prevalence of 0.85% in 2025 and cardiovascular-related deaths reaching 651,481 annually. This study aims to implement and compare Random Forest (RF) and Support Vector Machine (SVM) algorithms for heart disease prediction using the UCI Heart Disease dataset (Cleveland subset, 303 instances). Methods include preprocessing (missing value imputation, one-hot encoding, scaling), model training, and evaluation using accuracy, precision, recall, F1-score, and 5-fold cross-validation. Results show RF achieving 86.89% test set accuracy and 89% recall for Presence, while SVM reaches 85.25%. In cross-validation, SVM is more stable with a mean accuracy of 81.84% compared to RF's 80.18%. Feature importance from RF highlights thal_7.0 (0.129), thalach (0.126), and ca (0.110) as key predictors. Conclusion: RF excels in reducing false negatives for early detection, while SVM is more generalizable. The novelty lies in the comprehensive comparison with feature interpretation in the Indonesian health context, supporting AI-based prediction systems for heart disease prevention.
Co-Authors Alexander Sebayang Efrata Tarigan Zakaria Sembiring