Optimasi Model Yolov8n Menggunakan Augmentasi Data Untuk Peningkatan Akurasi Sistem Dress-Code Surveillance - Sherla Mutia; Rina Firliana; Arie Nugroho (Bulletin of Information Technology (BIT), 2026)
Manual surveillance of student dress-code compliance on campus is often inefficient, subjective, and constrained by the physical fatigue of security personnel. This study aims to automate the surveillance system by optimizing the Nano variant of the YOLOv8 (YOLOv8n) Deep Learning model based on Computer Vision. The main challenge in real-time object detection is limited datasets and visual diversity, which increases the risk of overfitting. The solution applied to address this issue is the implementation of comprehensive dynamic data augmentation strategies, including Hue, Saturation, Value (HSV) manipulation, Horizontal Flipping, and Mosaic Augmentation. Utilizing the CRISP-DM methodology, this technique expanded the dataset from 5,000 initial images to 9,742 training images. The empirical test results show that the optimized YOLOv8n model significantly improved accuracy by 43,5% compared to the baseline model. The best-performing model achieved a Mean Average Precision (mAP@0.5) of 95.3%, with a Precision of 93.1%, Recall of 91.1%, and an F1-score of 0.92. These metrics demonstrate the reliability of the system in reducing false positives while operating in crowded real-world environments. This automated surveillance system is highly feasible for direct integration into campus CCTV infrastructure using edge computing to objectively support institutional discipline.