Identifikasi Citra Penyakit Monkeypox dengan Random Forest Serta Ekstraksi Fitur VGG19: Indonesia - Muhammad Azka Zaki; Eka Prakarsa Mandyartha; Achmad Junaidi (Pusat Riset dan Inovasi Nasional, 2026)
Monkeypox is an infectious disease that can be recognized through images of the patient's skin lesions. A fast and accurate diagnosis method is required to identify Monkeypox. This research aims to identify Monkeypox imagery using the VGG19 feature extraction method, which is then classified using the Random Forest algorithm. The dataset consists of 770 original images, which were expanded to 5,860 images through geometric transformation augmentation. The test results show that the VGG19 feature extraction method with Random Forest classification achieved an accuracy of 95.1%, indicating good performance. This finding suggests the potential of this method as a machine learning approach for detecting Monkeypox and can be further developed with other artificial intelligence approaches.