Analisis Perbandingan Model Machine Learning untuk Prediksi Penyakit Jantung
DOI:
https://doi.org/10.29407/b4yzdb65Keywords:
logistic regression, heart disease, machine learning, prediction model, random forestAbstract
Penyakit jantung merupakan salah satu penyebab utama kematian di dunia yang memerlukan deteksi dini secara akurat. Penelitian ini bertujuan untuk menganalisis dan memprediksi risiko penyakit jantung menggunakan dataset kesehatan publik melalui pendekatan machine learning. Metode yang digunakan melibatkan perbandingan dua algoritma klasifikasi, yaitu Random Forest dan Logistic Regression. Data diproses melalui tahapan pembersihan, pembagian dataset (80:20), serta normalisasi khusus untuk regresi logistik. Hasil evaluasi menunjukkan bahwa algoritma Random Forest memberikan performa sempurna dengan akurasi 100%, sementara Logistic Regression menghasilkan akurasi sebesar 87,80%. Penelitian ini menyimpulkan bahwa Random Forest lebih unggul dalam menangkap pola medis yang kompleks, sementara Logistic Regression unggul dalam aspek interpretabilitas koefisien fitur.
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References
[1] WHO, "Cardiovascular diseases (CVDs) Fact Sheets," 2024.
[2] Anjeli, L., & Rizki, F. 2025. Analisis Prediksi Penyakit Jantung Menggunakan Perbandingan Algoritma Machine Learning. JTINFO: Jurnal Teknik Informatika, 4(2), 262-269.
[3] Jasman, T. Z., dkk. 2022. Perbandingan Logistic Regression, Random Forest, dan Perceptron pada Klasifikasi Pasien Gagal Jantung. CSRID Journal, 14(3), 271-286.
[4] Nasution, N., dkk. 2025. An Evaluation of Logistic Regression, Random Forest, SVM, and KNN Models on the UCI Heart Disease Dataset. IT Journal Research and Development.
[5] Putri, S. K. N. A., dkk. 2025. Penerapan Hyperparameter Tuning pada Model Klasifikasi untuk Prediksi Risiko Penyakit Jantung. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 5(4).
[6] Yuliasari, S., & Rahmatulloh, A. 2025. Performance Analysis and Accuracy of Machine Learning Algorithms for Heart Disease Prediction. Telematika, 22(3).
[7] Alexander, M. 2025. Penerapan Algoritma Random Forest untuk Deteksi Penyakit Jantung dan Optimisasi Menggunakan Feature Importance. Bachelor Thesis, Universitas Multimedia Nusantara.
[8] Wahidin, A. J., dkk. 2024. Machine Learning Untuk Klasifikasi Penyakit Jantung. Aisyah Journal of Informatics and Electrical Engineering, 6(1).
[9] Simanjuntak, M. S., dkk. 2025. A Comparative Study of Machine Learning and Deep Learning Models for Heart Disease Classification. Journal of Applied Informatics and Computing, 9(6).
[10] Putri, I. P. 2021. Analisis Performa Metode K-Nearest Neighbor (KNN) dan Crossvalidation pada Data Penyakit Cardiovascular. Indonesian Journal of Data Science, 2(1), 21-28.
[11] Farida, L. N., & Bahri, S. 2024. Klasifikasi Gagal Jantung Menggunakan Metode SVM (Support Vector Machine). Komputika: Jurnal Sistem Komputer, 13(2).
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