<![CDATA[The conventional attendance system at SMPIT Utsman Bin Affan Surabaya is time-consuming, prone to errors, and inefficient for data recapitulation. This research aims to design and implement a digital student attendance system based on face recognition to automate the attendance process. The method used is Research and Development (R&D) with a Prototyping approach. The system was built using the Python programming language, React interface, SQLite database, and the OpenCV library applying the Haar Cascade Classifier algorithm for face detection and Euclidean Distance for feature extraction. System testing was conducted through Black Box Testing for functionality and Confusion Matrix for recognition accuracy. The testing results showed that after algorithmic optimizations—including the application of Histogram Equalization, threshold adjustment to 3000, and a 15% Confidence Gap—the system successfully achieved a 100% accuracy rate under normal lighting conditions at an ideal distance of 30-60 cm. In conclusion, this face recognition-based attendance system operates effectively, accurately, and minimizes administrative time for student attendance.]]>
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