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June 28, 2025
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September 30, 2025
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Abstract

This study elaborates the application of various machine learning (ML) algorithm models to identify the classification of employee competency to take the competency certification needed to support the work. The goal is to choose the best ML model to improve accuracy, scalability, and fairness. The algorithms to be tested in this study are Logistic Regression, SVM, KNN, and Naive Bayes as traditional algorithms with Random Forest as an ensemble algorithm. This study took data from 13,830 employees who had been submitted in competency certification activities in 2024 at PT PLN (Persero). All models were measured through cross-validation on parameters such as accuracy, precision, recall, and F-1 score using the Python programming language on Jupyter Notebook. The best performing model on the F-1 score parameter was Logistic Regression which achieved the highest score of 0.9559. Meanwhile, Random Forest is the best model on the Precision parameter which is very important to identify employees who are truly incompetent to avoid losses due to human error. Based on this research, Logistic Regression and Random Forest can be prioritized to improve the accuracy of employee competency status in order to produce truly competent employees to strengthen operational performance and increase company revenue.

Keywords

Comparative Analysis Competency Certification Machine Learning

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References

  1. Aruna, P., Sujatha D, C., Surya Prakash, S., & Prince, R. (2022). An Automated Self Rating Questionnaire Assessment Tool for the evaluation of Entrepreneurial Competencies using SVM Algorithm. 5th International Conference on Inventive Computation Technologies, ICICT 2022 - Proceedings, 1230–1234. https://doi.org/10.1109/ICICT54344.2022.9850442
  2. Bian, K., & Priyadarshi, R. (2024). Machine Learning Optimization Techniques: A Survey, Classification, Challenges, and Future Research Issues. In Archives of Computational Methods in Engineering. Springer Science and Business Media B.V. https://doi.org/10.1007/s11831-024-10110-w
  3. Blanka, C., Krumay, B., & Rueckel, D. (2022). The interplay of digital transformation and employee competency: A design science approach. Technological Forecasting and Social Change, 178. https://doi.org/10.1016/j.techfore.2022.121575
  4. BNSP. (2013). Pedoman Pelaksanaan Uji Kompetensi Oleh Panitia Teknis BNSP. Badan Nasional Sertifikasi Profesi, 9-10. https://sisfo.bnsp.go.id/download/0bY3gyEIrs4uoeLh8ZMW5TR1D26xiHFf.pdf
  5. Bongale, A. M., Dharrao, D., & Urolagin, S. (2023). Exploratory Data Analysis and Classification of Employee Retention based on Logistic Regression Model. 2023 9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023, 1929–1933. https://doi.org/10.1109/ICACCS57279.2023.10112681
  6. Dubey, R. K., & Kumar, A. (2015). Comparison of subjective and objective speech quality assessment for different degradation / noise conditions. 2015 International Conference on Signal Processing and Communication, ICSC 2015, 261–266. https://doi.org/10.1109/ICSPCom.2015.7150659
  7. Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media. http://14.139.161.31/OddSem-0822-1122/Hands-On_Machine_Learning_with_Scikit-Learn-Keras-and-TensorFlow-2nd-Edition-Aurelien-Geron.pdf
  8. Kamper, H., Niehaus, C., & Wolff, K. (2023). Using Machine Learning to Understand Assessment Practices of Capstone Projects in Engineering. 2023 IEEE IFEES World Engineering Education Forum and Global Engineering Deans Council: Convergence for a Better World: A Call to Action, WEEF-GEDC 2023 - Proceedings. https://doi.org/10.1109/WEEF-GEDC59520.2023.10344177
  9. Krishna, S. H., Pandey, N., Sheshadri, T., Lourens, M., Bala, A., & Bhati, I. (2024). Management of Human Resources 2.0: Applying Machine Learning to the Recruitment and Retention of Talent. Proceedings of International Conference on Communication, Computer Sciences and Engineering, IC3SE 2024, 1438–1441. https://doi.org/10.1109/IC3SE62002.2024.10593520
  10. Mahmud Nawawi, H., Baitul Hikmah, A., Mustopa, A., & Wijaya, G. (2024). Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir. Jurnal SAINTEKOM, 14(1), 13–25. https://doi.org/10.33020/saintekom.v14i1.512
  11. Manoharan, G., Perla, S., Kadam, P. S., Sathe, M., Ranichandra, S., & Lourens, M. (2024). Supervised Learning in Employee Performance Prediction. 2024 Asian Conference on Intelligent Technologies, ACOIT 2024. https://doi.org/10.1109/ACOIT62457.2024.10940050
  12. McKinney, W. (2017). Python for Data Analysis. O’Reilly Media.
  13. Patil, A., Sarda, T., Shetye, S., Bachchan, S., & Gutte, V. S. (2023). Comparing Logistic Regression and Tree Models on HR Data. Proceedings - 2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023, 671–677. https://doi.org/10.1109/ICACCTech61146.2023.00113
  14. Sharma, R., & Sohal, J. (2024). Data-Driven Talent Management: The Impact of Machine Learning on HR Efficiency and Effectiveness. 2024 2nd World Conference on Communication and Computing, WCONF 2024. https://doi.org/10.1109/WCONF61366.2024.10691955
  15. Spencer, L.M., & Spencer, S.M. (1993). Competence at work: Models for superior performance. New York: John Wiley and Sons
  16. Stone, D.L., Deadrick, D.L., Lukaszewski, K.M., Johnson, R. (2015). The influence of technology on the future of human resource management, Hum. Resour. Manag. Rev. 25 216–231, https://doi.org/10.1016/j.hrmr.2015.01.002