Journal of Technology and Computer (JOTECHCOM)
Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer

Comparison of Random Forest and Naïve Bayes Classifier Methods for Monkeypox Classification

Aprilia, Katharina Tyas (Unknown)
Sitorus, Irwansyah Putera (Unknown)
Ridha, Muhammad Rasyid (Unknown)
Novelan, Muhammad Syahputra (Unknown)



Article Info

Publish Date
11 Feb 2026

Abstract

Monkey Pox is a disease caused by a virus with the genus orthopoxvirus that can infect humans. The initial symptoms of this disease are the appearance of lumps due to swollen lymph nodes, muscle pain, fever, feeling tired and weak. Although similar to Chickenpox, Monkey Pox is clinically difficult to distinguish from other smallpox diseases. This study aims to classify Monkey Pox disease using the "Monkey-Pox PATIENTS Dataset". Classification of Monkey Pox disease is done using Random Forest and Naïve Bayes methods. Random Forest produces higher accuracy than Naïve Bayes in classifying Monkey Pox disease, which is 69.24% with a k-fold value of 5 and the number of trees 64 using an unbalanced dataset with 6 attributes. While Naïve Bayes produces an accuracy of 68.56% using a dataset without balancing with 8 attributes (k-fold=5, kernel=Gaussian) and 9 attributes (k-fold=3 and 10, kernel=Gaussian).

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Journal Info

Abbrev

jotechcom

Publisher

Subject

Computer Science & IT

Description

The Journal of Technology and Computer (JOTECHCOM) brings together researchers, academics (faculty and students), and industry practitioners to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote cross-disciplinary and cross-domain collaboration. ...