ABSTRACT
Almost all activities are currently implemented with applications, including the implementation of exams, especially the covid-19 pandemic has caused online exams to have begun to be widely used by various kinds of educational institutions, online exams are carried out generally using computer devices that require a lot of devices and space for infrastructure. This study aims to create an online exam system that is simple and easy to do by utilizing mobile devices. This system utilizes the face recognition feature for security and minimizes the occurrence of fraud. The method used is FaceNet taking an input image quality of 160x160 pixels with an accuracy percentage of 85.7%. © 2022 IEEE.
ABSTRACT
During COVID19 pandemic, people are encouraged to practice physical distancing at least 1 meter when interacting with other people to prevent the spread of the COVID19. This study aims to develop a system that can monitor the physical distancing and track physical contact in a room using internet of things (IoT) and artificial intelligent technology. The system consists of a small single-board computer (Raspberry Pi), webcam, and web application displaying physical contact information. The system uses YOLO algorithms to detect the human object and euclidean distance formula to determine the distance between human objects. We evaluated the performance of YOLOv3 and YOLOv3-tiny running on Raspberry Pi. The evaluation result shows that YOLOv3 consumes more CPU resources than YOLOv3-tiny but has better accuracy in detecting human objects. YOLOv3-tiny can process images and detect objects faster than YOLOv3. © 2022 IEEE.