Enhancing human resource management in educational institutions through machine learning: A data-driven strategy

Human resource management in educational institutions often faces challenges in terms of efficiency, accuracy, and strategic decision-making. Traditional management systems are sometimes less able to accommodate evolving needs, such as performance assessment, competency development, and optimal resource allocation. In this context, the application of machine learning (ML) offers opportunities to improve HR management through a more accurate and efficient data-driven approach. This research aims to develop and implement machine learning-based human resource management strategies in educational institutions. The research method used involves a quantitative approach by collecting employee data from several educational institutions, including performance, attendance, satisfaction level, and other factors. This data is then analyzed using machine learning algorithms, including regression, classification, and clustering, to identify performance patterns and provide better recommendations in HR management. The results show that the application of machine learning can improve accuracy in performance assessments, speed up the decision-making process related to resource allocation, and help identify potential employees for further development. In addition, educational institutions that implement this strategy can improve operational efficiency and the overall quality of human resource management. This study concludes that the application of machine learning in human resource management has a positive impact on educational institutions, especially in the aspect of data-based decision-making.