<![CDATA[Manual student attendance systems still present several limitations, including the potential for data manipulation, human error, and low efficiency in large classroom environments. This study aims to implement the Convolutional Neural Network (CNN) method to simultaneously identify students’ faces within a classroom setting. The dataset consisted of 1,740 facial images collected from 58 students using a 2K Full HD webcam under varying capture angles and lighting conditions. The research stages included data collection, image preprocessing, data augmentation, CNN model training, and evaluation using a confusion matrix, accuracy, precision, recall, and F1-score metrics. The developed CNN model, named FACENET V5, was designed using TensorFlow with three convolutional blocks, batch normalization, max pooling, dropout, and a softmax classifier. Experiments were conducted using image sizes of 100×100, 200×200, 300×300, and 400×400 pixels with several dataset split scenarios. The results demonstrated that the 100×100 image size with a 90:10 data split achieved the best performance, obtaining a validation accuracy of 98.28% and a loss value of 0.1127. Furthermore, FACENET V5 was compared with ResNet50V2, MobileNetV2, and VGG16. Comparative results indicated that FACENET V5 provided the most optimal performance in simultaneous student face recognition. This study confirms that CNN can be effectively implemented as an automated face recognition-based attendance system in academic environments.]]>
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