Predictions of Stem Skills in Human Resource Management Using Machine Learning
Issued Date
2025-01-01
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Scopus ID
2-s2.0-105040631351
Journal Title
6th Technology Innovation Management and Engineering Science International Conference Times Icon 2025 Proceedings
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SCOPUS
Bibliographic Citation
6th Technology Innovation Management and Engineering Science International Conference Times Icon 2025 Proceedings (2025)
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Nuntasing S., Leelasantitham A., Sukamongkol Y. Predictions of Stem Skills in Human Resource Management Using Machine Learning. 6th Technology Innovation Management and Engineering Science International Conference Times Icon 2025 Proceedings (2025). doi:10.1109/TIMES-iCON67125.2025.11488114 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/117200
Title
Predictions of Stem Skills in Human Resource Management Using Machine Learning
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Abstract
Governments are now widely adopting Information Technology (IT) as a critical tool for reforming and driving public sector organizations. STEM (Science, Technology, Engineering, and Mathematics) skills are fundamental for analyzing and executing initiatives related to this IT-driven transformation. This research focuses on applying Machine Learning (ML) techniques to human resource management. By efficiently analyzing data from HRM systems, the goal is to improve the planning of human resource development. The objective of this research is to optimize outcomes and determine the best-performing Machine Learning (ML) model. This research compared various Machine Learning models and techniques for handling imbalanced data to evaluate their predictive performance for a target group. The models considered include K-Nearest Neighbors, Random Forests, Support Vector Machines, Multi-layer Perceptron, Cat Boost, and others. These models were evaluated based on their accuracy in predicting the potential of the target group. The results indicate that K-Nearest Neighbors and Random Forests are the topperforming models, with superior accuracy and strong ROC curve performance. These findings suggest that these models are wellsuited for evaluating the target group's scores. Additionally, the performance of other models was discussed, highlighting their respective strengths and weaknesses in various scenarios. This research is of paramount importance for the planning and development of human resources, as well as for advancing organizational progress. By applying the results from Machine Learning (ML), we can enhance efficiency and strategize Human Resource Management (HRM) more effectively. Furthermore, this study underscores the necessity of selecting an appropriate ML model for specific tasks to maximize organizational benefits.