<![CDATA[The rapid advancement of face recognition technology offers potential solutions for inefficient manual attendance systems, such as the one at SMK Tunas Muda Berkarya Vocational School, which relies on time-consuming, error-prone methods. This study aimed to design and implement an automated attendance system using face recognition to enhance accuracy and efficiency. Employing Python, OpenCV, and the Eigenface method with Principal Component Analysis (PCA), the system integrated Viola-Jones algorithm for face detection and Haar-like features for training. UML diagrams guided the design, while Black Box Testing validated functionality. Results demonstrated successful implementation with 15 students, achieving efficient real-time attendance recording and reduced processing time. However, accuracy depended on optimal lighting and frontal face positioning. The conclusion affirms the Eigenface method’s effectiveness in automating attendance, significantly improving over manual systems. Future recommendations include optimizing environmental adaptability, integrating mobile platforms, and enhancing user interaction features for broader applicability. This research underscores the viability of biometric systems in educational institutional management.]]>
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