<![CDATA[One of the challenges in monitoring exams in the classroom or closed room is the limited eye of the tire supervisor if he/she continues to monitor for a long time. Therefore, many behaviors of students cheating over the escape. One solution to overcome this problem is to implement an smart monitoring system that capable of detecting student exam cheating. A number of studies on smart monitoring systems have been conducted. However, the studies have not archieved optimal accuracy in identifying exam cheating. Therefore, this study provides a method to detect exam cheating in a closed room. The method used to detect the object is YOLO version 8 (Yolov8). Before training using the YOLOv8 method, hyperparameter tuning was made to generate best model performance. The test results have shown that the Yolov8s model has created the best performance with the precision, recall, IoU-Score and mAP50 values of 0.952, 0.966, 0.8977 and 0.984. Testing in the working environment shows that the Yolov8s model can detect exam cheating in real time at a frame rate of 28 fps. Although it has achieved quite optimal performance. However, the performance of this exam cheating monitoring system can still be improved. Furthermore, this study has limitations, specifically that it can only detect cheating in the place where the dataset was collected.]]>
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