IMPLEMENTASI DEEP LEARNING UNTUK PENDETEKSIAN PENGGUNA MASKER PADA CCTV: STUDI KASUS PUSKESMAS SUDIANG RAYA - Trisnaningrun, Ainun; Tungadi, Eddy; Syamsuddin, Irfan (Politeknik Negeri Ujung Pandang, 2024)
The use of mask is one of the things that needs attention when you want to leave the house to implement health protocols to avoid diseases that are currently troubling people in the world or commonly known as Covid-19. Currently, people are reluctant to come to the hospital for fear of being exposed to the Covid-19 virus, so people who need treatment prefer to visit the puskesmas near their home. However, there are still many people who do not use masks on the grounds that the intended location is close to home. To overcome this can be done by detecting visitors' faces using the camera. So a system is proposed, namely the detection of mask users with the Convolutional Neural Network (CNN) method. One of the widely applied CNN methods for processing image data is YOLO. YOLO (You Only Look Once) is a deep learning-based model developed to detect an object in real-time. YOLO works by looking at the image as a whole, then using a neural network and automatically detecting existing objects. So that in this study the YOLO model, namely YOLOv4, was used as an object detection model in a mask detection system with CCTV video media whose data is sent in real-time.