This research has developed a facial recognition system for human resource management, specifically for scanning employee faces to check in and out of work. The system applies face detection and face recognition techniques to identify employees. The system captures images through a Webcam/USB Camera and compares them with the database to verify identities. This developed system will be integrated as a feature in the company's Human Resource Management (HRM) system, addressing the shortcomings of the existing check-in/check-out process.
      The objectives of this research are: 1) To apply facial recognition technology for employee check-in and check-out, 2) To test the efficiency of the developed facial recognition system, and 3) To study user satisfaction with the facial recognition system for employee check-in and check-out. The target group for this research includes two machine learning experts and 32 employees. The research tools consist of an expert quality assessment and a user satisfaction survey.
     The research findings are as follows: 1) The developed system effectively reduces the problems of the existing process and enhances the convenience of employee check-in/check-out, 2) The performance evaluation of the system, measured by the accuracy of employee face identification, shows an accuracy rate of 97.8% and a specificity rate of 82.6%, demonstrating its reliability and applicability in real-world scenarios, 3) The satisfaction assessment results from the sample groups, divided into machine learning experts and employees, show that the system's quality is rated at a high level by experts ( = 4.10, S.D. = 0.42) and that employees' satisfaction with the system is at the highest level (= 4.56, S.D. = 0.57).