THE ROLE OF MACHINE LEARNING IN ENHANCING TALENT ACQUISITION AND WORKFORCE PLANNING

Verbian Hidayat Syam (Universitas Riau Kepulauan)
Oktavianti (Universitas Riau Kepulauan)
Rizki Eka Putra (Universitas Riau Kepulauan)

Multidiciplinary Output Research for Actual and International Issue (Morfai Journal)
Vol. 5 No. 6 (2025): Multidiciplinary Output Research For Actual and International Issue

This study investigates the transformative potential of machine learning (ML) in modern human resource management, addressing industry-wide challenges in talent acquisition, retention, and strategic planning. Through a mixed-methods approach analyzing 45,000 employee records across multiple sectors and employing algorithms including NLP and predictive modeling, the research evaluates ML's efficacy against traditional HR processes. Results demonstrate that ML-driven systems significantly enhance operational efficiency, improving screening speed by 95% and hiring accuracy by 50%, while reducing bias by 60%. Furthermore, ML enables proactive talent management through precise attrition prediction and data-driven succession planning. The discussion concludes that ML integration is pivotal for evolving HR from an administrative function to a strategic asset, fundamentally enhancing organizational agility and human capital optimization, though its success is contingent on ethical implementation and robust data governance.