Deteksi Helm pada Pengguna Sepeda Motor dengan Metode Convolutional Neural Network

Albert Albert (Program Studi Informatika)
Kartika Gunadi (Program Studi Informatika)
Endang Setyati (Program Studi Informatika)

Jurnal Infra
Vol 8, No 1 (2020)

In order to ensure security measures, traffic violations are an important matter. One of the most traffic violations is the use of helmets on motorcycle riders. Therefore, a program was created that could help in identifying helmet users for motorcycle riders. In the process of identifying data, a problem that is often experienced is helmet characteristics. In this study a filter experiment will be conducted in order to recognize the characteristics of the helmet. This study uses 2 methods, You Only Look Once (YOLO) and Convolutional Neural Network (CNN). The YOLO method is used to find regions of motorbikes and motorbike riders. The CNN method is used to classify helmet users in motorcycle riders. The results of the CNN classification will be calculated using a confusion matrix in order to get the accuracy of the correct prediction. The program results from this study will identify helmet users on motorcyclists in the video. Accuracy obtained between motorcycle riders driving with helmets and without helmets is 70.49%.