Design and Construction of Employee Attendance Using a Facial Recognition System at PT. Astra Daihatsu Krakatau

Abstract

The rapid advancement of information technology has transformed organizational operations, including human resource management systems, where accuracy and efficiency in employee attendance recording are crucial for maintaining discipline and supporting performance evaluation. However, traditional attendance systems such as manual recording or fingerprint scanning remain limited, especially for employees who are working remotely or traveling, leading to data inconsistencies and delays. This research aims to design and implement a machine learning–based facial recognition attendance system to improve the flexibility, accuracy, and reliability of attendance processes at PT Astra Daihatsu Krakatau. The study employs the Unified Modeling Language (UML) approach for system design and utilizes facial recognition algorithms to automate attendance verification through biometric analysis. The resulting system comprises several key modules—login, main menu, employee data, attendance statistics, and history tracking—providing real-time and integrated attendance monitoring accessible from various devices. The findings indicate that the system effectively addresses the inefficiencies of conventional methods by enabling accurate biometric verification, minimizing fraud, and supporting remote attendance logging. This innovation enhances organizational transparency, operational efficiency, and adaptability in line with digital transformation initiatives. The implication of this study highlights the strategic role of artificial intelligence in modernizing workforce management and optimizing administrative processes within industrial environments.