MACHINE LEARNING FOR EMPLOYMENT POSITION MAPPING

Authors

  • Sena Aditia Apriadi Universitas Muhammadiyah Kuningan
  • Hilman Ferdinandus Pardede National Research and Innovation Agency (BRIN)

DOI:

https://doi.org/10.33480/pilar.v21i2.3028

Keywords:

bagged decision tree, employees, machine learning, organizational performance, predictive analytics

Abstract

Employee performance directly impacts organizational efficiency, yet traditional HR analytics often lack predictive precision. This study bridges HR theory and machine learning by evaluating tree-based algorithms for employee data analysis. Using a dataset of 15,227 employee records, we tested the Bagged Decision Tree algorithm, focusing on variables such as talent, career values, and aspirations. The Bagged Decision Tree achieved 98.65% accuracy, with talent and career values as key predictors. Excluding aspiration values reduced accuracy slightly to 98.57%, while excluding career values lowered it significantly to 92.13%. These findings highlight the robustness of the Bagged Decision Tree in HR analytics and emphasize the importance of variable selection, particularly career values and talent, in predicting performance outcomes. Future work should further explore real-world implementation challenges.

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Published

2025-09-23

How to Cite

Apriadi, S. A., & Pardede, H. F. . (2025). MACHINE LEARNING FOR EMPLOYMENT POSITION MAPPING. Jurnal Pilar Nusa Mandiri, 21(2), 266–272. https://doi.org/10.33480/pilar.v21i2.3028

Issue

Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Period for September 2025

Section

Articles

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Copyright (c) 2025 Sena Aditia Apriadi, Hilman Ferdinandus Pardede

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