IMPLEMENTASI PERBANDINGAN YOLO V8 DAN YOLO V11 DALAM PENERAPAN TATA TERTIB BERPAKAIAN DI LINGKUNGAN KAMPUS STUDI KASUS UNIVERSITAS ESA UNGGUL KAMPUS BEKASI
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neat clothing arrangement,
real-time monitoring
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Abstract
The consistent enforcement of dress code regulations is an important approach to creating an ethical campus environment. This study aims to develop an automated dress code violation detection system utilizing artificial intelligence technology with the the YOLOv8 and YOLOv11 algorithm algorithm. The system is designed to detect categories of compliant and non-compliant clothing in real-time through cameras and provide automatic notifications when violations are detected. The model, trained with a dataset of clothing images, is evaluated using metrics such as precision, recall, and mean Average Precision (mAP). Training results show that the model achieves high accuracy in detecting dress code violations, supporting the goal of enhancing monitoring efficiency and reducing the violation rate. At Esa Unggul University, Bekasi Campus, this system also contributes to modernizing campus management through technology, fostering the creation of a disciplined academic environment aligned with the values of educational institutions.
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