<![CDATA[The Industrial Revolution 4.0 has driven the integration of technology across various sectors, including the application of machine learning (ML) in Human Resource Management (HRM). This technology offers significant potential for improving the efficiency and effectiveness of HR management. This study, conducted using a systematic literature review (SLR), aims to examine the potential, driving and inhibiting factors, and impact of machine learning implementation in HRM. The results show that the application of ML in HRM offers significant benefits, including accelerating and improving the accuracy of the recruitment process, predicting employee turnover rates, providing appropriate training recommendations for employees, and evaluating and personalizing HR performance. Machine learning also plays a role in supporting more informed strategic decision-making. Factors driving ML implementation include the need for accurate decision-making, processing large amounts of data, and higher operational efficiency. However, several challenges hinder its implementation, such as data privacy and security concerns, potential bias in algorithms, high implementation costs, and corporate cultural resistance to change. Despite these challenges, the positive impact of machine learning implementation is significant, particularly in transforming the role of HRD to become more strategic, increasing efficiency and accuracy, and accelerating the completion of administrative work. However, the application of this technology also carries risks, such as data privacy breaches, discrimination due to algorithmic bias, and resistance within corporate cultures that hinder the adoption of new technologies. Therefore, while machine learning offers significant potential, its implementation in HR must be undertaken cautiously, with proper risk management to maximize its benefits and minimize potential negative impacts.]]>
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