Model Hybrid PSO, Feature Selection Correlation dan Logistic Regression untuk Deteksi Penyakit Jantung
DOI:
https://doi.org/10.23969/infomatek.v28i1.43123Keywords:
Jantung, Correlation, Machine Learning, Logistic Regression, Particle Swarm OptimizationAbstract
Penyakit jantung merupakan salah satu penyebab utama kematian baik di Indonesia maupun secara global sehingga diperlukan model deteksi dini yang akurat. Penelitian ini bertujuan meningkatkan kinerja Logistic Regression dengan regularisasi L2 melalui optimasi Particle Swarm Optimization (PSO) dan feature selection berbasis correlation. Metode yang digunakan meliputi pre-processing, standarisasi, seleksi fitur, serta evaluasi menggunakan K-10 Fold Cross Validation. Hasil pengujian menunjukkan bahwa Logistic Regression menghasilkan accuracy 82,47%, precision 80,31%, recall 88,56%, dan F1-score 84,10%. Setelah dioptimasi dengan PSO, performa meningkat menjadi accuracy 84,45%, precision 81,74%, recall 91,01%, dan F1-score 85,98%. Hasil tersebut menegaskan bahwa pendekatan hybrid yang diusulkan efektif dalam meningkatkan deteksi penyakit jantung.
Downloads
References
Adhitya, R. R., Witanti, W., & Yuniarti, R. (2023). Perbandingan metode CART dan Naïve Bayes untuk klasifikasi customer churn. INFOTECH Journal, 9(2), 307–318. https://doi.org/10.31949/infotech.v9i2.5641
Agarwal, N., Deepakshi, Harikiran, J., Lakshmi, Y. B., Kumar, A. P., Muniyandy, E., & Verma, A. (2024). Predictive modelling for heart disease diagnosis: A comparative study of classifiers EAI Endorsed Transactions on Pervasive Health and Technology, 10,1–11. https://doi.org/10.4108/eetpht.10.5518
Permana, A. A. (2024). Implementasi Metode K-Nearest Neighbors (KNN) untuk Klasifikasi Penyakit Jantung. G-Tech: Jurnal Teknologi Terapan, 8(1), 186–195.
Aspiah, R., Azhima, T., & Siswa, Y. (2022). Implementasi correlation based feature selection (CFS) untuk peningkatan akurasi algoritma C4.5 dalam prediksi performa akademik mahasiswa berbasis learning management system. Jurnal Ilmiah Betrik, 13(3), 199–207.
Bietrosula, A. B., Werdiningsih, I., & Wuriyanto, E. (2024). Classification of cardiovascular disease based on lifestyle using random forest and logistic regression methods. International Journal of Electrical and Electronic Engineering, 12(2). https://doi.org/10.52549/ijeei.v12i2.5388
Black, J. E., Kueper, J. K., & Williamson, T. S. (2023). An introduction to machine learning for classification and prediction. Family Practice, 40(1), 200–204. https://doi.org/10.1093/fampra/cmac104
Wibowo, A. C., Lestari, S. A., & Nurchim, N. (2024). Analisis penggunaan machine learning dalam klasifikasi penentuan penyakit jantung. Simtek: Jurnal Sistem Informasi dan Teknik Komputer, 9(2), 97–101. https://doi.org/10.51876/simtek.v9i2.395
Damari, A., Azhima, T., Siswa, Y., & Pranoto, W. J. (2025). Penerapan metode PSO-SMOTE pada algoritma Naive Bayes untuk mengatasi class imbalance data bencana tanah longsor. Jurnal Inovtek Polbeng, 10(1), 332–343.
Dinova, D. B., & Prasetiyo, B. (2024). Implementasi random forest dalam klasifikasi kanker paru-paru. JOINTER: Journal of Informatics Engineering, 5(1), 27–31.
Elshewey, A. M., Abed, A. H., Khafaga, D. S., Alhussan, A. A., Eid, M. M., & El-Kenawy, E. S. M. (2025). Enhancing heart disease classification using optimization algorithm and LSTM. Scientific Reports, 15(1), 1–21. https://doi.org/10.1038/s41598-024-83592-0
Fan, C., Chen, M., Wang, X., Wang, J., & Huang, B. (2021). A review on data preprocessing techniques toward efficient knowledge discovery. Frontiers in Energy Research, 9, 1–17. https://doi.org/10.3389/fenrg.2021.652801
Firnanda, P. A., Litaysa, S., Rahma, F., & Fauzi, F. (2025). Analisis perbandingan decision tree dan random forest dalam klasifikasi penjualan produk pada supermarket. Emerging Statistics and Data Science Journal, 3(1), 445–461.
Harris, J. K. (2021). Primer on binary logistic regression. Family Medicine and Community Health, 9, 1–7. https://doi.org/10.1136/fmch-2021-001290
Hicks, S. A., Strümke, I., Thambawita, V., Hammou, M., Riegler, M. A., Halvorsen, P., & Parasa, S. (2022). On evaluation metrics for medical applications of artificial intelligence. Scientific Reports, 12(1), 1–9. https://doi.org/10.1038/s41598-022-09954-8
Hidayat, R., Saputra, Y., Sujana, T., Husnah, M., & Saputra, H. T. (2024). Implementasi machine learning untuk prediksi penyakit jantung menggunakan algoritma support vector machine. BIOS: Jurnal Teknologi Informasi dan Rekayasa Komputer, 5(2), 161–168.
Jain, M., Saihjpal, V., Singh, N., & Singh, S. B. (2022). An overview of variants and advancements of PSO algorithm. Applied Sciences, 12(17), 1–21. https://doi.org/10.3390/app12178392
Jibril, A. U., Haruna, K., & Jiangsheng, Z. (2023). Feature selection and parameter optimization using PSO. Journal of Computer Science and Technology, 11(1), 21–47. https://doi.org/10.15640/jcsit.v11n1a3
Junifer, J., Tanjaya, H., & Kenichi, K. (2021). Mendeteksi penyakit jantung menggunakan logistic regression. Journal Information System Development, 6(2), 1–10.
Markoulidakis, I., Rallis, I., Georgoulas, I., Kopsiaftis, G., Doulamis, A., & Doulamis, N. (2021). Multiclass confusion matrix reduction method. Technologies, 9(4). https://doi.org/10.3390/technologies9040081
Oise, G. P., Oyedotun, S. A., Nwabuokei, O. C., Babalola, A. E., & Unuigbokhai, N. B. (2025). Enhanced prediction of coronary artery disease using logistic regression. Fudma Journal of Sciences, 9(3), 201–208. https://doi.org/10.33003/fjs-2025-0903-3263
Oon, W. Y., Tuti, D., Yee, L. S., & Susanti. (2022). Penerapan data mining untuk klasifikasi kelulusan mahasiswa. SATIN - Sains dan Teknologi Informasi, 8(2), 122–131. https://doi.org/10.33372/stn.v8i2.885
World Health Organization. (2025). Cardiovascular diseases. https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases
Ratnasari, R., Wahidin, J., Setiawan, A. E., & Bintoro, P. (2024). Machine learning untuk klasifikasi penyakit jantung. Aisyah Journal of Informatics and Electrical Engineering, 6(1), 145–150. https://doi.org/10.30604/jti.v6i1.272
Kementerian Kesehatan RI. (2024). Kenali gejala jantung sejak dini. https://kemkes.go.id/id/kenali-gejala-jantung-sejak-dini
Rojas, R., Seseña, H., Zuñiga, M., & Martínez, M. (2024). Performance comparison in optimization algorithms. Revista de Ciencias de la Salud, 1–8. https://doi.org/10.35429/johs.2024.11.30.1.8
Salsabila, C. A., Yulianto, F., & Siswa, T. A. Y. (2025). Implementasi metode Naive Bayes untuk klasifikasi kecelakaan lalu lintas. Jurnal Informatika dan Teknik Elektro Terapan, 13(1). https://doi.org/10.23960/jitet.v13i1.5890
Sani, N. S., Esa, M. I., & Musawi, B. A. (2023). Malware detection using deep learning and correlation-based feature selection. Symmetry, 15(3).
Saputra, D., Dharmawan, W. S., & Irmayani, W. (2022). Performance comparison of SVM and SVM-PSO algorithms. International Journal of Advances in Data and Information Systems, 3(2), 74–86. https://doi.org/10.25008/ijadis.v3i2.1243
Sathyanarayanan, S. (2024). Confusion matrix-based performance evaluation metrics. African Journal of Biomedical Research, 4023–4031. https://doi.org/10.53555/ajbr.v27i4s.4345
Sepharni, A., Hendrawan, I. E., & Rozikin, C. (2022). Klasifikasi penyakit jantung menggunakan algoritma C4.5. STRING, 7(2), 117. https://doi.org/10.30998/string.v7i2.12012
Setiani, H., Sunyoto, A., & Nasiri, A. (2022). Metode Naïve Bayes dan particle swarm optimization untuk klasifikasi penyakit jantung. Explore, 12(2), 6. https://doi.org/10.35200/explore.v12i2.566
Sonjaya, C., Masruriyah, A. F. N., Kusumaningrum, D. S., & Pratama, A. R. (2022). Performance comparison of classification algorithm. INTERNAL Journal, 5(2), 166–175. https://doi.org/10.32627/internal.v5i2.595
Sung, W. T., Isa, I. G. T., & Hsiao, S. J. (2023). Designing aquaculture monitoring system based on data fusion. Electronics, 12(9). https://doi.org/10.3390/electronics12092032
Taher, H. A., & Abdulazeez, A. M. (2023). Machine learning approaches for heart disease detection. International Journal of Research and Applied Technology, 3(2), 267–282.
Yi, J., Yu, P., Huang, T., & Xu, Z. (2024). Optimization of transformer heart disease prediction model. ICFTIC Proceedings, 1109–1113. https://doi.org/10.1109/ICFTIC64248.2024.10913096
Zabor, E. C., Reddy, C. A., Tendulkar, R. D., & Patil, S. (2022). Logistic regression in clinical studies. International Journal of Radiation Oncology Biology Physics, 112(2), 271–277. https://doi.org/10.1016/j.ijrobp.2021.08.007
Zhang, L., Geisler, T., Ray, H., & Xie, Y. (2022). Improving logistic regression on imbalanced data. Journal of Applied Statistics, 49(13), 3257–3277. https://doi.org/10.1080/02664763.2021.1939662
Zhu, Y., & Liu, R. (2021). An algorithmic view of L2 regularization. Journal of Machine Learning Research, 22, 1–62.
