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All Journal Jurnal Informatika Dan Tekonologi Komputer
Muhammad Azka Zaki
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Computer Science & IT Control & Systems Engineering Other

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Identifikasi Citra Penyakit Monkeypox dengan Random Forest Serta Ekstraksi Fitur VGG19: Indonesia Muhammad Azka Zaki; Eka Prakarsa Mandyartha; Achmad Junaidi
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.10132

Abstract

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.
Co-Authors Achmad Junaidi Eka Prakarsa Mandyartha