<![CDATA[The growing capabilities of facial recognition technology have driven the innovation of digital attendance solutions, offering a more reliable alternative to conventional systems that tend to be inefficient and vulnerable to misuse. This research focuses on designing and applying a digital attendance system utilizing face detection and recognition at the Department of Information and Communication Technology Education, Universitas Negeri Manado. This is a research and development (R&D) study using the prototype method, in which the system is built using face encoding technology and the K-Nearest Neighbors (KNN) algorithm to detect and recognize users' faces through a webcam in real-time. The system was tested in real-world scenarios to evaluate accuracy, duplicate prevention, and system response time. The results showed that the system successfully recognized faces with 92% accuracy, prevented duplicate face registration, and recorded attendance quickly and automatically. The implemented web-based dashboard demonstrated strong performance in helping administrators oversee user information and attendance records. As a result, the system has proven to be effective in improving the precision and speed of attendance monitoring in academic environments, establishing itself as a modern solution that supports the digitalization of educational administration.]]>
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