Hepatitis Disease Classification Analysis Using the Support Vector Machine (SVM) Method - Syaputri, Amanda Novia; Munawardah, Rahmatul; Siregar, Ulysa; Pratiwi, Yesi Dwi; Indra, Evta (JITTER : Jurnal Ilmiah Teknologi dan Komputer; Vol 6 No 2 (2025): JITTER, Vol.6, No.2, August 2025, 2025)
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