<![CDATA[Education has a very important role in life, student involvement in the learning process in the classroom is an important factor in the success of learning. However, some students pay less attention to the lesson, indicating a lack of productivity in learning. The use of machine learning and computer vision techniques has undergone significant development in the last decade and is applied in a variety of applications, including monitoring student attention in the classroom. One of the commonly used techniques in machine learning and computer vision to detect objects is by applying image processing. One of the algorithms implemented for object detection that can provide good results is You Only Look Once. This research proposes the application of YOLOV5 in real time student focus detection and analyzes the performance and computational load of the five YOLOV5 architectures (YOLOV5n, YOLOV5s, YOLOV5m, YOLOV5l, and YOLOV5x) in student surveillance during classroom learning. The dataset used is video data that has been converted into image form, and 297 images are produced. Where, this dataset is divided into 2 classes, namely the "Focus" and "Not Focus" classes. The results show that YOLOV5x has the highest computational load with large parameter values and GFLOPs. However, in term model performance YOLOV5m provides more optimal results than other architectures, with precision of 83.3%, recall of 85.1%, and mAP@50 of 89.9%. The results of this study show that the proposed YOLOV5 model can be a good performing method in detecting student focus in real time.]]>
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