Perbandingan Kinerja YOLOv8 dan SSD MobileNet untuk Deteksi Kelengkapan Atribut Seragam Siswa - Argi Ginanjar; Adhi Kusnadi (Fakultas Teknik Universitas Persada Indonesia YAI, 2026)
Inspecting the completeness of student uniform attributes is challenging because several items, suchas epaulettes, name tags, belts, black shoes, and caps, may appear small, partially occluded, orvisually unclear. This study compares YOLOv8n and SSD MobileNet for detecting student uniformattributes using a self-collected dataset. The dataset contains 1,532 augmented images, five objectclasses, and 5,294 YOLO-format bounding box annotations. The experimental procedure includeddataset preparation, object annotation, train-validation-test splitting, model training, and finalevaluation on the test set. The evaluation used precision, recall, mAP@50, mAP@50-95, inferencetime, and FPS. On the test set, YOLOv8n achieved a precision of 0.8966, recall of 0.8689, mAP@50of 0.9276, mAP@50-95 of 0.6723, inference time of 10.36 ms, and 96.47 FPS. SSD MobileNetachieved a precision of 0.5140, recall of 0.5169, mAP@50 of 0.6034, mAP@50-95 of 0.3171,inference time of 11.00 ms, and 90.87 FPS. These findings indicate that YOLOv8n performed betterin the tested dataset and experimental setting.