Gerlan Apriandy Manu, Punaji Setyosari, Saida Ulfa, Henry Praherdhiono
This research explores the development of a real-time emotion detection system to improve engagement in online learning. The system uses Convolutional Neural Networks (CNN) to identify five emotions: happy, sad, angry, surprised, and neutral via webcam during virtual classes. Tested with 30 students in an Artificial Intelligence course, it achieved 86.4% accuracy, excelling in detecting happy and neutral states. Instructors used emotional feedback to adapt teaching dynamically, enhancing learning experiences and satisfaction. Feedback showed that 88% of students felt more motivated and engaged. This study highlights the potential of emotion-based tools in bridging gaps between online and traditional education. © 2026, Grand Canyon University. All rights reserved.
State University of Malang, Indonesia
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