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RANCANG BANGUN SISTEM DETEKSI GENDER MENGGUNAKAN MODEL YOLOV8 UNTUK PEMANTAUAN KEAMANAN PADA HUNIAN KHUSUS PUTRI BERBASIS FULL-BODY RECOGNITION
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.
FAKULTAS SAINS DAN TEKNOLOGI, UIN SUNAN KALIJAGA YOGYAKARTA
RANCANG BANGUN SISTEM DETEKSI GENDER MENGGUNAKAN MODEL YOLOV8 UNTUK PEMANTAUAN KEAMANAN PADA HUNIAN KHUSUS PUTRI BERBASIS FULL-BODY RECOGNITION (Text)
RANCANG BANGUN SISTEM DETEKSI GENDER MENGGUNAKAN MODEL YOLOV8 UNTUK PEMANTAUAN KEAMANAN PADA HUNIAN KHUSUS PUTRI BERBASIS FULL-BODY RECOGNITION (Other)
HTML Summary of #77704 RANCANG BANGUN SISTEM DETEKSI GENDER MENGGUNAKAN MODEL YOLOV8 UNTUK PEMANTAUAN KEAMANAN PADA HUNIAN KHUSUS PUTRI BERBASIS FULL-BODY RECOGNITION
005.36 Software Development / Pengembangan Perangkat Lunak