Facial Expression Recognition of Al-Qur'an Memorization Students Using Convolutional Neural Network
Abstract
Facial expression recognition technology has advanced significantly and has become an intriguing topic of study. This research focuses on the facial expressions of Al-Qur’an memorization students, which naturally reveal various aspects of their engagement, understanding, and emotional barriers about the verses being memorized. The issue is that facial expression recognition still lacks optimal accuracy, and the need for a better algorithmic model to improve accuracy is evident. Therefore, an intelligent computing system is required to address this problem. This study aims to enhance the accuracy of facial expression recognition in Al-Qur’an memorization students using the Convolutional Neural Network (CNN) method, classifying facial expressions such as happy, neutral, and tired based on collected facial image data, achieving improved accuracy. The first stage involves capturing image data via CCTV, followed by preprocessing, training the CNN model, result analysis, and model evaluation. By using the CNN method to recognize the facial expressions of Al-Qur’an memorization students, a high accuracy of 84% was achieved with a loss value of 14.9.
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