@phdthesis{digilib77704,
           month = {June},
           title = {RANCANG BANGUN SISTEM DETEKSI GENDER MENGGUNAKAN MODEL YOLOV8 UNTUK PEMANTAUAN KEAMANAN PADA HUNIAN KHUSUS PUTRI BERBASIS FULL-BODY RECOGNITION},
          school = {UIN SUNAN KALIJAGA YOGYAKARTA},
          author = {NIM.: 22106050063 Ahmad Bidni Musyafa},
            year = {2026},
            note = {Nurochman, S.Kom., M.Kom.},
        keywords = {Yolov8n, Deteksi Gender, Full-Body Recognition, Computer Vision, Sistem Real-Time, Early Warning System},
             url = {https://digilib.uin-suka.ac.id/id/eprint/77704/},
        abstract = {Manual security monitoring in female-only dormitories has limitations and is prone to negligence. To address this issue, this study designs a gender detection system as a proactive early warning system. The system utilizes deep learning-based computer vision technology to perform automatic and Real-time security monitoring.
The system was developed using a full-body recognition approach with the lightweight YOLOv8n architecture, integrated with automatic notifications via Telegram. The dataset consists of simulated CCTV images (semi top-down angles, lighting variations, and mask/helmet occlusion scenarios) as well as secondary data. The entire data was divided into 70\% training data, 20\% validation data, and 10\% testing data using a transfer learning strategy.
Evaluation results show the model achieved an mAP50 of 92.90\%, precision of 90.65\%, recall of 89.01\%, and F1-score of 89.82\%. The system proved efficient, operating stably at an average speed of 20 FPS on purely CPU-based devices without GPU acceleration. Field testing demonstrated that the full-body recognition approach remains robust in detecting subjects under occlusion. In conclusion, the YOLOv8n model provides an optimal balance between high accuracy, local computational efficiency, and operational reliability.}
}