<![CDATA[ As digital technology accelerates at an unprecedented pace, educational institutions and businesses are looking for more efficient and accurate ways to record attendance. Traditional attendance methods, such as manual signing or manual data entry, can face several challenges, including human error, inaccuracy, and time consumption, ultimately leading to decreased efficiency and productivity. With the increasing demand for smarter and more efficient solutions, it has become imperative to explore new technologies that can improve this vital process. The main problem is that traditional attendance systems are unable to keep up with the speed, accuracy, and security demands of the modern era. These systems are often prone to errors, which can result in incorrect or inaccurate attendance, and the attendance process can be time-consuming, especially in large groups, reducing time allocated for educational or productive activities. This study aims to develop an automated attendance system using real-time face detection technology, with the aim of improving efficiency, accuracy, and security compared to traditional attendance methods. Haar Cascades technology was used to recognize and classify faces, with attendance data stored in a MySQL database managed via SQL. The results of the study showed an accuracy rate of up to 97.44% in detecting faces and recording attendance under different conditions. It is proposed to enhance this system in the future by integrating anti-impersonation and mask detection technologies and using deep learning principles to increase security and effectiveness in different scenarios.]]>
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