THE ROLE OF MACHINE LEARNING IN ENHANCING TALENT ACQUISITION AND WORKFORCE PLANNING
Main Article Content
Verbian Hidayat Syam
Oktavianti
Rizki Eka Putra
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.
Alabi, O. A., Ajayi, F. A., Udeh, C. A., & Efunniyi, C. P. (2024). Predictive Analytics in Human Resources: Enhancing Workforce Planning and Customer Experience. International Journal of Research and Scientific Innovation, XI(IX), 149–158. https://doi.org/10.51244/IJRSI.2024.1109016
Alaghbari, M. A., Ateeq, A., Alzoraiki, M., Milhem, M., & Beshr, B. A. H. (2024). Integrating Technology in Human Resource Management: Innovations and Advancements for the Modern Workplace. 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), 307–311. https://doi.org/10.1109/ICETSIS61505.2024.10459498
Al-Alawi, A. I., & Albuainain, M. S. (2024). Machine Learning in Human Resource Analytics: Promotion Classification using Data Balancing Techniques. 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), 1–5. https://doi.org/10.1109/ICETSIS61505.2024.10459566
Angulakshmi, M., Madhumithaa, N., Dokku, S. R., Pachar, S., Sneha, K., & Sahaya Lenin, D. (2024). Predictive HR Analytics: Using Machine Learning to Forecast Workforce Trends and Needs. 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), 1399–1405. https://doi.org/10.1109/IC3I61595.2024.10829013
Dasmadi, D. (2023). Dedication of Machine Learning for Trend of Digital HRM. Jurnal Penelitian Pendidikan IPA, 9(SpecialIssue), 416–421. https://doi.org/10.29303/jppipa.v9iSpecialIssue.5804
Garg, S., Sinha, S., Kar, A. K., & Mani, M. (2022). A review of machine learning applications in human resource management. International Journal of Productivity and Performance Management, 71(5), 1590–1610. https://doi.org/10.1108/IJPPM-08-2020-0427
Hukkeri, P., & Pol, S. (2025). The Digital Shift In Hiring: A Critical Review of Traditional and Contemporary Recruitment Techniques. International Journal of Innovative Science and Research Technology, 747–754. https://doi.org/10.38124/ijisrt/25may671
John, A. S., & HAJAM, A. A. (2024). Leveraging Predictive Analytics for Enhancing Employee Engagement and Optimizing Workforce Planning: A Data-Driven HR Management Approach. International Journal of Innovation in Management, Economics and Social Sciences, 4(4), 33–41. https://doi.org/10.59615/ijimes.4.4.33
Joshi, M., Misal, A. N., Gosavi, P. R., Gautam, S., Lourens, M., & Mungekar, P. R. (2024). Applying Machine Learning to Enhance Human Resource Management Strategies for Improved Organisational Results. 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies, 1–6. https://doi.org/10.1109/TQCEBT59414.2024.10545122
Khan, H. D. (2025). A Review on Integration of Digitized Technologies in Human Resource Management. International Journal for Research in Applied Science and Engineering Technology, 13(2), 717–721. https://doi.org/10.22214/ijraset.2025.66897
Koivunen, S., Sahlgren, O., Ala-Luopa, S., & Olsson, T. (2023). Pitfalls and Tensions in Digitalizing Talent Acquisition: An Analysis of HRM Professionals’ Considerations Related to Digital Ethics. Interacting with Computers, 35(3), 435–451. https://doi.org/10.1093/iwc/iwad018
Krishna, S., & Sidharth, S. (2022). Analyzing Employee Attrition Using Machine Learning: the New AI Approach. 2022 IEEE 7th International Conference for Convergence in Technology (I2CT), 1–14. https://doi.org/10.1109/I2CT54291.2022.9825342
Kumar, M. R., Sharma, A., Bhargavi, Y. K., & Ramesh, G. (2022). Human Resource Management Using Machine Learning-Based Solutions. 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC), 801–806. https://doi.org/10.1109/ICESC54411.2022.9885526
Nisha, B., Manobharathi, V., Jeyarajanandhini, B., & Sivakamasundari, G. (2023). HR Tech Analyst: Automated Resume Parsing and Ranking System through Natural Language Processing. 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), 1681–1686. https://doi.org/10.1109/ICACRS58579.2023.10404426
Nurjaman, K. (2025). Technological Disruption in Human Resource Management: A Review of Machine Learning Algorithms for Strategic Decision-Making. TEM Journal, 2870–2885. https://doi.org/10.18421/TEM143-86
Saxena, A., Buhukya, S., Sumalatha, I., Dutt, A., Shaaker, A. M., & V, A. (2023). Machine Learning and Human Resource Management: A Path to Efficient Workforce Management. 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), 1709–1714. https://doi.org/10.1109/UPCON59197.2023.10434761
Singh, N. (2024). “Machine Learning Approaches to Enhance Candidate Selection: A Comparative Study in HR Recruitment". International Journal for Research in Applied Science and Engineering Technology, 12(7), 1286–1296. https://doi.org/10.22214/ijraset.2024.63745
Singhraul, Prof. B. P., & Anuragi, D. (2024). HUMAN RESOURCE MANAGEMENT IN THE DIGITAL ERA. International Journal of Research in Commerce and Management Studies, 06(05), 267–281. https://doi.org/10.38193/IJRCMS.2024.6515
Singla, P., Kaur, J., Anju, Soni, A., Tuteja, A., & Sharma, S. (2024). Streamlining Talent Acquisition: A Machine Learning Approach to Automated Resume Screening. 2024 Second International Conference on Advanced Computing & Communication Technologies (ICACCTech), 69–75. https://doi.org/10.1109/ICACCTech65084.2024.00022
