<![CDATA[The quality of online learning in higher education is highly influenced by the level of student alertness and concentration during the lecture process. However, the phenomenon of drowsiness resulting from physical fatigue and prolonged screen time often reduces learning effectiveness. The limitations of lecturers in visually monitoring students' conditions on online learning platforms drive the need for an automated monitoring system capable of operating in real-time. This study aims to develop a student drowsiness detection system using the YOLOv8 algorithm with a CRISP-DM approach. The dataset used originates from two public datasets on the Roboflow platform, namely SleepyDetect and Yawn, which then underwent curation, labeling, and augmentation processes to produce 8,573 images across three classes: open-eyes, pre-drowsy (yawning), and drowsy (eyes closed). The evaluation results show that the model achieves an mAP50 value of 0.992, Precision of 0.973, Recall of 0.979, and an F1-Score of 0.976, with an inference speed of 4.1 ms per image, demonstrating high detection performance on the utilized dataset. The model's implementation was carried out through the AwakeLens software integrated with a monitoring dashboard to support the online learning process. Nevertheless, because this research relies on a public dataset, the obtained results still require further validation using a primary dataset collected from Indonesian students to enhance the external validity and generalization capability of the model.]]>
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