<![CDATA[Conventional attendance systems in companies, including PT Ruragraha Propertindo, still face significant issues such as the potential for fraud (buddy punching), inefficient manual processes, and non integrated data processing. The implementation of face recognition-based biometric technology offers a more practical and secure solution. This study aims to design and implement an automated face recognition-based attendance system at PT Ruragraha Propertindo to improve the accuracy, efficiency, and accountability of employee attendance recording. The system was developed using the Waterfall model of the System Development Life Cycle (SDLC). The Haar Cascade Classifier algorithm from the OpenCV library was used for real-time face detection, combined with Local Binary Pattern Histogram (LBPH) for identity recognition. Software development utilized the Python programming language with the Flask framework for the web interface and MySQL as the database. Functional testing was conducted using Blackbox and Whitebox Testing. The research output is a system prototype expected to automate the attendance process, reduce waiting time, minimize fraud, and provide accurate, integrated attendance data to support managerial decision-making at PT Ruragraha Propertindo.]]>
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