Analisis Penerapan Metode Ensembled Learning Decision Tree Pada Klasifikasi Virus Hepatitis C

Abstract

Hepatitis C virus is a deadly virus that attacks the liver. This virus can cause chronic infections, even 80% of sufferers have experienced an illness. To minimize the risk of exposure to disease caused by the hepatitis C virus, consultation with a doctor or using an intelligent detection system can be conducted. Of course, if used a smart strategy, our need data that already contains parameters related to hepatitis C. This study uses a public dataset that the public can access. So, the purpose of this study is to classify patients with hepatitis C virus using a tree-based algorithm. The results obtained by applying the proposed algorithm are 93% accuracy, 92% precision, and 91% recall. This study also performs comparisons with other methods, namely naive bayes. The results show that the tree-based way is superior.