Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer dan Teknologi (IKOMTI)
Ali Sya'bana Syukurillah
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Deteksi Lesi Cacar Monyet pada Citra Dermatologi Menggunakan Metode YOLOv7 Ali Sya'bana Syukurillah; Anggit Wirasto; Retno Agus Setiawan
Jurnal IT UHB Vol 6 No 3 (2025): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v6i3.2015

Abstract

Monkeypox is an infectious disease characterized by skin lesions that are often difficult to distinguish from other pox-related conditions, which complicates diagnosis in resource-limited settings. This study aims to implement YOLOv7 for detecting monkeypox lesions in dermatological images and to evaluate its accuracy. The dataset consisted of 1,500 annotated images resized to 512×512 pixels, monkeypox was used as the target class, while chickenpox and cowpox were included as comparison/non-target classes to support the differentiation of lesions during model training and evaluation. The YOLOv7 model was trained for 50 epochs using default configurations and a transfer learning approach, with a data split of 70% for training, 20% for validation, and 10% for testing. Training results showed an mAP@0.5 of 89.1% and an mAP@0.5:0.95 of 59.2%. Meanwhile, on the testing stage using original (non-augmented) data, the model performance decreased, achieving an mAP@0.5 of 75.3% and an mAP@0.5:0.95 of 44.9%.
Co-Authors Anggit Wirasto Retno Agus Setiawan