The Improvement Human Resource Performance Through Smart Vision Camera Optimization Based on Artificial Intelligence-Integrated Human Machine Interface Systems

This study aims to investigate the causal relationship between Artificial Intelligence (AI) integrated Human Machine Interface (HMI) system implementation on smart vision camera and the improved performance of waste processing machine operators.It examines how Human Resource Management (HRM) training practices, such as competency development, management change, and job design, influence the success of HMI implementation and contribute to enhanced operator performance.Employing a quantitative research method, this study collected data from selected samples, which were then analyzed using the Structural Equation Modeling-Partial Least Square (SEM-PLS) method.The analysis revealed several significant findings.The results show that improving employee performance has a strong influence on both HR development and the effectiveness of HMI usage.In addition, effective HR practices positively impact operator performance, and skilled operators significantly support the success of AIintegrated HMI systems.These findings suggest that combining technology with targeted HR development strategies is essential for building a more efficient and sustainable waste management system.