<![CDATA[The development of digital technology has opened opportunities for educational institutions to improve the efficiency and accuracy of administrative systems, including student attendance recording. The current attendance system, which relies on RFID cards, often encounters issues such as damaged, lost, or unreadable cards, leading to long queues and the need for manual administration. This study aims to address these problems by developing an automatic attendance system based on facial recognition using deep learning technology. The proposed system integrates the Multi-task Cascaded Convolutional Neural Networks (MTCNN) algorithm for face detection and FaceNet for face recognition. Data collection is conducted by acquiring student facial images as the dataset for model training. The data is processed through normalization, face detection, and feature extraction using FaceNet embeddings. The system is integrated with a MySQL database to record student attendance in real time. Testing results show that the system performs well in detecting and recognizing student faces with satisfactory accuracy levels, despite variations in lighting conditions. By reducing dependency on physical cards, this system can streamline the attendance process and provide ease of use for users. This study demonstrates that the application of deep learning technology has the potential to improve the efficiency of attendance management in higher education institutions.]]>
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