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

Rifqi Alfinnur Charisma; Sofiyudin Pamungkas; Rifqi Akmal Saputra; Nur Ghaniaviyanto Ramadhan; Faisal Dharma Adhinata

doi:10.47065/josyc.v3i4.2064

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

(*) Corresponding Author

DOI: https://doi.org/10.47065/josyc.v3i4.2064

Keywords: Decision Tree; Hepatitis C; Classification; Data Mining

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.

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