<![CDATA[A facial recognition-based attendance system is a modern solution to overcome the weaknesses of manual attendance methods that are prone to manipulation and recording errors. This study uses a deep learning-based attendance application by implementing a Convolutional Neural Network (CNN) using MobileNetV2, VGG16, and ResNet50 architectures optimized for devices with limited resources. The facial dataset was collected independently and went through preprocessing stages, including normalization, resizing, augmentation, and face detection with OpenCV. The model was trained using TensorFlow and Keras on Google Colab with a GPU. It was then evaluated using a confusion matrix, which yielded accurate predictions with a low error rate. A classification report was also conducted, with an accuracy of 0.98, a precision of 1.00, a recall of 1.00, and an F1-score of 1.00, achieving a very high level of performance, indicating no prediction errors. A Flask web-based application was designed to connect the facial recognition model with the user interface, and was tested in real-time to measure the speed and accuracy of attendance. The results show that the CNN-based attendance application is able to provide a safer, faster, and more efficient attendance alternative compared to conventional methods.]]>
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