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

Katharina Tyas Aprilia; Irwansyah Putera Sitorus; Muhammad Rasyid Ridha; Muhammad Syahputra Novelan

Penulis

Katharina Tyas Aprilia Universitas Pembangunan Panca Budi Penulis
Irwansyah Putera Sitorus Universitas Pembangunan Panca Budi Penulis
Muhammad Rasyid Ridha Universitas Pembangunan Panca Budi Penulis
Muhammad Syahputra Novelan Universitas Pembangunan Panca Budi Penulis

Kata Kunci:

Classification, Data mining, Monkey pox, Naive bayes, Random forest

Abstrak

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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