Comparison of SVM, KNN, And Naïve Bayes Algorithms in Monkeypox Disease Classification

Kohsasih, Kelvin Leonardi

TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt

Advances in medical technology have enabled the application of machine learning for disease classification, including monkeypox. Monkeypox is a zoonotic disease caused by the monkeypox virus and can be detected through patient data. This study aims to compare the performance of Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and Naïve Bayes algorithms in building a monkeypox classification model. The dataset used consists of 25,000 patient records. The results show that the SVM model with a linear kernel achieved the best accuracy compared to KNN and Naïve Bayes. These findings demonstrate that the SVM model with a linear kernel is highly effective in classifying monkeypox, offering great potential for further medical applications.