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

Rifqi Alfinnur Charisma (Institut Teknologi Telkom Purwokerto, Banyumas)
Sofiyudin Pamungkas (Institut Teknologi Telkom Purwokerto, Banyumas)
Rifqi Akmal Saputra (Institut Teknologi Telkom Purwokerto, Banyumas)
Nur Ghaniaviyanto Ramadhan (Institut Teknologi Telkom Purwokerto, Banyumas)
Faisal Dharma Adhinata (Institut Teknologi Telkom Purwokerto, Banyumas)

Journal of Computer System and Informatics (JoSYC)
Vol 3 No 4 (2022): August 2022

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.