Analisis Perbandingan Klasifikasi Virus Cacar Monyet Dengan Pendekatan Algoritma Machine Learning - Bintoro, Panji; Zulkifli, Zulkifli; Putri, Nopi Anggista (Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK), 2024)
Monkeypox Virus (MPXV) is part of the Orthopoxvirus (OPXV) group within the Poxviridae family. This virus is contagious when someone has direct contact with infected individuals, animals, or contaminated materials. Transmission can occur through direct bodily contact, animal bites, respiratory droplets, or mucus in the eyes, nose, or mouth. However, since the emergence of the recent outbreak in May 2022, this disease has spread to various countries, posing a threat to develop into a global pandemic. Many machine learning algorithm approaches, including for classifying monkeypox disease, are proposed. This research suggests a system that can be used for Comparative Analysis of monkeypox virus classification with several machine learning algorithm approaches. From the work that has been done, it states that the neural network algorithm model outperforms other algorithm models. Testing the neural network algorithm model obtained accuracy of 1.0, precision of 1.0, recall of 1.0, f1-score of 1.0, and ROC-AUC of 1.00 for Monkeypox Positive and Monkeypox Negative.