Pengembangan Model Klasifikasi Aritmia Pada Lansia Menggunakan Algoritma Support Vector Machine (SVM) Berbasis Data EKG
DOI:
https://doi.org/10.30865/jurikom.v12i4.8719Keywords:
Arrhythmia, Electrocardiogram, Elderly, Classification, Support Vector MachinAbstract
Arrhythmia is one of the most dangerous heart rhythm disorders, especially for the elderly, due to degenerative changes in cardiac structure and function. This study aims to develop an electrocardiogram (ECG)-based arrhythmia classification model for the elderly using the Support Vector Machine (SVM) algorithm. Data were collected from three nursing homes with a total of 184 subjects aged 50–75 years using the Smart Holter ECG 5-lead device. The research stages included ECG signal acquisition, signal preprocessing (baseline correction and Butterworth filter), physiological feature extraction (PR, QRS, QT, RR intervals, ST segment, heart rate, R/S ratio), and data labeling by cardiologists. The model was trained and tested using a hold-out approach with an 80:20 ratio and class stratification. Evaluation results showed high performance with 96.36% accuracy on the training set and 94.57% accuracy on the testing set. The Area Under Curve (AUC) reached 0.99 in micro-average and 0.98–1.00 for each class. This research confirms that SVM is effective for arrhythmia classification in the elderly and has potential as an accurate and efficient diagnostic tool
References
Chandra, A., & Suwanto, D., “Tren Kematian akibat Penyakit Jantung di Indonesia,” Jurnal Kesehatan Masyarakat Indonesia, vol. 9, no. 2, pp. 123–130, 2021.
Damayanti, F., & Nur, M., “Aritmia pada Lansia: Kajian Patofisiologi dan Tantangan Diagnosis,” Jurnal Kardiologi Indonesia, vol. 6, no. 1, pp. 45–52, 2024.
Kevin, K., Dasar-Dasar Elektrokardiografi Klinis, Jakarta: Penerbit Medika, 2022.
Rospia, A., Hartono, L., & Andika, Y., “Perubahan Anatomi Jantung pada Lansia,” Jurnal Geriatri dan Gerontologi, vol. 3, no. 2, pp. 66–72, 2022.
Candrawati, T., & Sukraandini, M., “Komorbiditas dan Risiko Aritmia,” Jurnal Ilmu Kedokteran Klinik, vol. 4, no. 3, pp. 88–95, 2022.
Yosephine, A., & Ratnadewi, N., “Analisis Fungsi EKG dalam Deteksi Awal Penyakit Jantung,” Jurnal Kesehatan Elektromedis, vol. 5, no. 1, pp. 33–39, 2021.
J. Nandanwar, J. Singh, S. Choudhar, and M. Patidar, “ECG Signals- Early detection of Arrhythmia using Machine Learning approaches,” Confluence: The Journal of Graduate Liberal Studies, Jan. 2023
Hissa, A., Rachman, T., & Sasongko, E., “Penggunaan Smart Holter 5-Lead untuk Pemantauan Jantung Lanjut Usia,” Seminar Nasional Teknologi Kesehatan, pp. 115–121, 2024.
J. Nandanwar, J. Singh, S. Choudhar, and M. Patidar, “ECG Signals- Early detection of Arrhythmia using Machine Learning approaches,” Confluence: The Journal of Graduate Liberal Studies, Jan. 2023.
Rustandi, R., Suhaedi, H., & Pemanasari, A., “Penerapan Algoritma SVM untuk Diagnosis Penyakit Jantung,” Jurnal Sistem Informasi Medis, vol. 7, no. 4, pp. 301–309, 2023.
Zuriel, M., & Fahrurozi, F., “Perbandingan Kernel SVM dalam Klasifikasi Sinyal Kesehatan,” Jurnal Teknologi Informasi dan Komputer, vol. 9, no. 3, pp. 205–213, 2021.
Ayuni, Q., Wihandika, R. C., & Yudistira, N., “Klasifikasi Aritmia dari Hasil Elektrokardiogram Menggunakan Metode Support Vector Machine,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 5, no. 6, pp. 2163–2170, 2021.
Nugrahadi, D. T., Arifin, M. Z., & Sari, L. D., “Efek Transformasi Wavelet Diskrit pada Klasifikasi Aritmia dari Data Elektrokardiogram Menggunakan Machine Learning,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 10, no. 2, pp. 112–120, 2023.
Mardhiah, A., Yulianti, D., & Hidayat, R., “Hubungan Status Gizi, Aktivitas Fisik, dan Multimorbiditas terhadap Penurunan Curah Jantung pada Pasien Cardiovascular Disease,” Jurnal Kesehatan, vol. 12, no. 1, pp. 25–32, 2024.
M. Ojha, “Automatic detection of arrhythmias from an ECG signal using an auto-encoder and SVM classifier,” Physical and Engineering Sciences in Medicine, Mar. 2022
Putra, B. W., Rahayu, S., & Wijaya, M., “Klasifikasi Aritmia pada Sinyal EKG Menggunakan Deep Neural Network,” Jurnal Penelitian Ilmu dan Teknologi Komputer, vol. 13, no. 1, pp. 54–63, 2021.
Hapsari, T. R., Sukmarini, L., & Herawati, T., “Deteksi Penyakit Kardiovaskular pada Isyarat EKG Berbasis Deep Learning,” Jurnal Teknologi dan Informatika (JOTING), vol. 5, no. 1, pp. 1376–1383, 2023.
K. B et al., “An Innovative Machine Learning Approach for Classifying ECG Signals in Healthcare Devices,” Journal of Healthcare Engineering, Apr. 2022,
S. Hasan, T. Pantha, and M. A. Arafat, "Design and development of a cost-effective portable IoT enabled multi-channel physiological signal monitoring system," Biomedical Engineering Advances, vol. 7, 100124, 2024.
Z. C. Oleiwi, E. Alshemmary, and S. H. M. Al-Augby, “Heart Disease Diagnosis System Using Multi-Methods Machine Learning,” Int. J. Electr. Comput. Eng., vol. 13, no. 1, pp. 45–54, 2023.
P. S. Madhulika and N. Sampath, “An Application of Normalizer Free Neural Networks on the SVHN Dataset,” May 2022
Pratama, A. R., Suryani, N., & Gunawan, A., “Implementasi Sistem Pendeteksi Premature Ventricular Contraction (PVC) Aritmia Menggunakan Metode SVM,” Jurnal Sistem Informasi dan Informatika, vol. 6, no. 1, pp. 75–84, 2021.
Undang-Undang Republik Indonesia Nomor 13 Tahun 1998 tentang Kesejahteraan Lansia, Jakarta: Sekretariat Negara, 1998.
World Health Organization, Cardiovascular Diseases Fact Sheet, 2021. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
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