Implementasi Sistem Verifikasi dan Pengenalan Wajah Berbasis YOLOv8n dan VGG-Face untuk Absensi Perkuliahan

Abstrak

This research presents the development of a facial recognition and verification system that aims to address attendance record falsification in university lectures, a persistent challenge in non-biometric attendance management. The proposed framework integrates the efficiency of YOLOv8n for facial detection with the strong feature representation capability of VGG-Face. The system applies image augmentation to create varied facial embeddings, uses Cosine Similarity for identity verification, and evaluates its performance through accuracy, precision, recall, and F1-score metrics. Experimental evaluations on student facial datasets captured under different lighting conditions, poses, and viewing angles show that the system achieves a stable accuracy of around 90 % without augmentation, increasing to 97 % with augmentation, which enhances overall stability and reliability. These results demonstrate that the integration of YOLOv8n and VGG-Face offers an effective and dependable solution for strengthening the security and credibility of facial recognition-based attendance systems in academic settings.Keywords: face recognition; student attendance; face verification; augmentation; YOLOv8; VGG-Face

Identifier

• OpenAlex: https://openalex.org/W7134848857
• DOI: https://doi.org/10.33369/pseudocode.13.1.44-53