Analisis Model Prediktif Talent Succesion dengan Machine Learning : Studi Kasus di PT. X

Febi Aulia Fitriana; Hijrah Saputra

doi:10.30640/jmcbus.v4i3.6686

Authors

Febi Aulia Fitriana Universitas Airlangga
Hijrah Saputra Universitas Airlangga

DOI:

https://doi.org/10.30640/jmcbus.v4i3.6686

Keywords:

HR Analytics, Human Resource Management, Machine Learning, Succession Planning, Talent Management

Abstract

Talent management and succession planning are strategic components of human resource management; however, their implementation frequently faces challenges arising from manual processes susceptible to subjectivity and inefficiency. This study aims to develop a predictive model based on machine learning to identify succession candidates for Team Leader positions at PT X using the CRISP-DM framework. The dataset comprises 1,107 actual employee records encompassing demographic, competency, performance, and experience dimensions. Three supervised learning classification algorithms were compared: Random Forest, Support Vector Machine (SVM), and Logistic Regression, with class imbalance addressed through SMOTE. Evaluation results indicate that Random Forest achieves the best performance with AUC 0.963, Accuracy 0.913, Recall 0.913, and MCC 0.827 on training data, and AUC 0.883 and Accuracy 0.868 on independent testing data. Feature importance analysis identifies the average performance appraisal score as the most dominant predictor (Information Gain = 0.536), followed by duration of assignment and tenure. These findings confirm that machine learning-based predictive models can serve as an objective, transparent, and equitable decision-support instrument in the succession candidate selection process within national energy enterprises.

References

Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012

Benabou, Y., & Touhami, K. (2026). Optimizing employee promotion predictions using machine learning. International Journal of Production Management and Engineering, 14(1), 1–15. https://doi.org/10.4995/ijpme.2026.xxxxxx

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0: Step-by-step data mining guide. SPSS Inc.

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953

Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21, Article 6. https://doi.org/10.1186/s12864-019-6413-7

Collings, D. G., & Mellahi, K. (2009). Strategic talent management: A review and research agenda. Human Resource Management Review, 19(4), 304–313. https://doi.org/10.1016/j.hrmr.2009.04.001

Colquitt, J. A., Conlon, D. E., Wesson, M. J., Porter, C. O. L. H., & Ng, K. Y. (2001). Justice at the millennium: A meta-analytic review of 25 years of organizational justice research. Journal of Applied Psychology, 86(3), 425–445. https://doi.org/10.1037/0021-9010.86.3.425

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018

Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary review. Journal of Management, 31(6), 874–900. https://doi.org/10.1177/0149206305279602

Fauziah, A., et al. (2024). Human resource analytics: Leveraging data for strategic workforce management. Jurnal Manajemen dan Bisnis Indonesia, 10(2), 112–128.

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010

García, S., Luengo, J., & Herrera, F. (2016). Data preprocessing in data mining. Springer.

Greenberg, J. (1990). Organizational justice: Yesterday, today, and tomorrow. Journal of Management, 16(2), 399–432. https://doi.org/10.1177/014920639001600208

Ibrir, A., & Çavur, M. (2024). Forecasting employees' promotion based on personal indicators by using a machine learning algorithm. Journal of Human Resources Management Research, 1–12. https://doi.org/10.5171/2024.xxxxxx

Ingale, P. (2024). Succession planning and leadership development: A systematic review. Journal of Human Resource Management, 12(1), 45–60.

Jantan, H., Hamdan, A. R., & Othman, Z. A. (2010). Human talent prediction in HRM using C4.5 classification algorithm. International Journal on Computer Science and Engineering, 2(8), 2526–2534.

Kristof-Brown, A. L., Zimmerman, R. D., & Johnson, E. C. (2005). Consequences of individuals' fit at work: A meta-analysis of person–job, person–organization, person–group, and person–supervisor fit. Personnel Psychology, 58(2), 281–342. https://doi.org/10.1111/j.1744-6570.2005.00672.x

Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR analytics. International Journal of Human Resource Management, 28(1), 3–26. https://doi.org/10.1080/09585192.2016.1244699

Pradito, B., et al. (2024). Classification for human resource talent management using support vector machine model. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 8(2), 310–318.

Robbins, S. P., & Judge, T. A. (2017). Organizational behavior (17th ed.). Pearson.

Rothwell, W. J. (2010). Effective succession planning: Ensuring leadership continuity and building talent from within (4th ed.). AMACOM.

Saputro, A. D., et al. (2024). Predictive insights into talent management: A random forest approach to assessing top talent in state-owned enterprises. Jurnal Teknologi Informasi dan Ilmu Komputer, 11(3), 55–66.

Schröer, C., Kruse, F., & Gómez, J. M. (2021). A systematic literature review on applying CRISP-DM process model. Procedia Computer Science, 181, 526–534. https://doi.org/10.1016/j.procs.2021.01.199

Wiblen, S., & Marler, J. H. (2021). Talent management: An HRM process approach. Oxford University Press.