Hepatitis Disease Classification Analysis Using the Support Vector Machine (SVM) Method
Abstract
Hepatitis, an inflammation of the liver caused by excessive alcohol consumption and autoimmune disorders, is a global health problem with an increasing number of deaths. Based on the WHO Global Hepatitis Report 2024, hepatitis is the second highest cause of infectious death, with 1.3 million cases in 2022. Of these, 83% were caused by hepatitis B and 17% by hepatitis C. Early detection and accurate classification are essential to improve treatment effectiveness. This study applied the Support Vector Machine (SVM) method to classify hepatitis patients. The SVM model applied to the hepatitis dataset, produced a high accuracy of 90.50% after going through 50 iterations. However, there were 8 cases of False Negative (living patients predicted to die) and 11 cases of False Positives (patients who died predicted to live). It is recommended for exploration of SVM parameters such as kernel, C value, and gamma. As well as considering ensemble methods such as Bagging or Boosting to improve accuracy and reduce model variance, especially on unbalanced or complex datasets
References
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[2] A. Damayanti and G. Testiana, “PENERAPAN DATA MINING UNTUK PREDIKSI PENYAKIT HEPATITIS C MENGGUNAKAN ALGORITMA NAÏVE BAYES,” J. Manaj. Inform. Jayakarta, vol. 3, no. 2, pp. 177–186, 2023, doi: 10.52362/jmijayakarta.v3i2.1098.
[3] B. A. Herlambang, A. G. Prathama, and R. R. Waliyansyah, “Aplikasi Pendeteksi Dini Penyakit Hepatitis Menggunakan Mesin Inferensi Forward Chaining Berbasis Android,” J. Appl. Sci. Technol., vol. 1, no. 02, p. 1, 2021, doi: 10.30659/jast.1.02.1-12.
[4] M. A. G. Hazber, E. M. Senan, and H. S. Alrashidi, “Feature Engineering Methods for Analyzing Blood Samples for Early Diagnosis of Hepatitis Using Machine Learning Approaches,” C. - Comput. Model. Eng. Sci., vol. 142, no. 3, pp. 3229–3254, Mar. 2025, doi: 10.32604/CMES.2025.062302.
[5] M. D. S. Kusuma and I. A. P. Wulandari, “Perilaku Hidup Bersih dan Sehat untuk Pencegahan Penyakit Hepatitis di Panti Asuhan,” J. Kreat. Pengabdi. Kpd. Masy., vol. 7, no. 4, pp. 1856–1866, Apr. 2024, doi: 10.33024/jkpm.v7i4.13312.
[6] D. W. Dede Widiyanto and A.Sidiq Purnomo, “Diagnosa Penyakit Hepatitis pada RSUD Bunut Menggunakan Sistem Pakar dengan Metode Certainty Factor,” JEKIN - J. Tek. Inform., vol. 4, no. 3, pp. 650–662, 2024, doi: 10.58794/jekin.v4i3.893.
[7] A. Hajjah, Y. Nora Marlim, and R. Noratama Putri, “APLIKASI PENDETEKSI PENYAKIT HEPATITIS MENGGUNAKAN METODE NAÏVE BAYES,” JOISIE J. Inf. Syst. Informatics Eng., vol. 7, no. 1, pp. 155–164, 2023.
[8] A. W. Feliansyah and E. Purwanto, “Analisis faktor yang berhubungan dengan penyakit hepatitis di Indonesia,” Holistik J. Kesehat., vol. 18, no. 9, pp. 1131–1138, Nov. 2024, doi: 10.33024/hjk.v18i9.587.
[9] M. Zulham, D. Saripurna, and M. Z. Siambaton, “Aplikasi Diagnosa Penyakit Hepatitis dengan Menggunakan Metode Teorema Bayes dan Certainty Factor,” Blend Sains J. Tek., vol. 2, no. 1, pp. 1–15, Apr. 2023, doi: 10.56211/blendsains.v2i1.241.
[10] “WHO membunyikan peringatan tentang infeksi hepatitis virus yang merenggut 3500 nyawa setiap hari.” https://www.who.int/news/item/09-04-2024-who-sounds-alarm-on-viral-hepatitis-infections-claiming-3500-lives-each-day (accessed Feb. 25, 2025).
[11] J. Ilmiah Matematika, S. Sofia Nurwananda, and R. Sulaiman, “APLIKASI HIMPUNAN FUZZY INTUISIONISTIK DALAM DIAGNOSA PENYAKIT HEPATITIS MENGGUNAKAN EXTENDED HAUSDORFF DISTANCE,” Mathunesa J. Ilm. Mat., vol. 10, no. 01, pp. 41–49, 2022.
[12] Jatnika Fahmi Idris, Rafid Ramadhani, and Muhammad Malik Mutoffar, “Klasifikasi Penyakit Kanker Paru Menggunakan Perbandingan Algoritma Machine Learning,” J. Media Akad., vol. 2, no. 2, pp. 1981–2000, 2024, doi: 10.62281/v2i2.145.
[13] R. Aprilia, “Implementasi Machine Learning Untuk Memprediksi Hepatitis Mortality Menggunakan Algoritma Decision Tree,” OKTAL J. Ilmu Komput. dan Sci., vol. 2, no. 4, pp. 1146–1150, 2023, [Online]. Available: https://journal.mediapublikasi.id/index.php/oktal
[14] W. D. Septiani, “OPTIMASI ALGORITMA C4.5 MENGGUNAKAN ALGORITMA GENETIKA UNTUK PREDIKSI PENYAKIT HEPATITIS,” INTI Nusa Mandiri, vol. 15, no. 1, pp. 59–64, Jul. 2020, doi: 10.33480/inti.v15i1.1421.
[15] “Hepatitis Data.” https://www.kaggle.com/datasets/codebreaker619/hepatitis-data/data (accessed May 21, 2025).
[16] T. Sugihartono and R. R. C. Putra, “Penerapan Metode Support Vector Machine Dalam Classifikasi Ulasan Pengguna Aplikasi Mobile Jkn,” SKANIKA Sist. Komput. dan Tek. Inform., vol. 7, no. 2, pp. 144–153, 2024, doi: 10.36080/skanika.v7i2.3193.
[17] S. Kenny Riva, S. Casi, and S. Randy Erfa, “Analisis Algoritma Support Vector Machine Dalam Klasifikasi Penyakit Stroke,” e-Proceeding Eng., vol. 9, no. 3, p. 922, 2022.
[18] V. RISKASARI YR, I. P. E. N. KENCANA, and I. K. G. SUKARSA, “Klasifikasi Penyakit Sirosis Menggunakan Support Vector Machine,” E-Jurnal Mat., vol. 12, no. 2, p. 87, 2023, doi: 10.24843/mtk.2023.v12.i02.p404.
[19] A. F. Djollong, “Teknik Pelaksanaan Penelitian Kuantitatif (Technique of Quantiative Research),” Istiqra’, vol. 2, no. 1, pp. 86–100, 2014.
[20] S. Sulastri, K. Hadiono, and M. T. Anwar, “Analisis Perbandingan Klasifikasi Prediksi Penyakit Hepatitis Dengan Menggunakan Algoritma K-Nearest Neighbor, Naïve Bayes Dan Neural Network,” Dinamik, vol. 24, no. 2, pp. 82–91, 2020, doi: 10.35315/dinamik.v24i2.7867.
Published
2025-07-08
How to Cite
SYAPUTRI, Amanda Novia et al.
Hepatitis Disease Classification Analysis Using the Support Vector Machine (SVM) Method.
JITTER : Jurnal Ilmiah Teknologi dan Komputer, [S.l.], v. 6, n. 2, p. 2470-2479, july 2025.
ISSN 2747-1233.
Available at: <https://ojs.unud.ac.id/index.php/jitter/article/view/129022>. Date accessed: 15 oct. 2025.
Section
Articles
