Forecasting Employee Potential through Probationary Assessment Memperkirakan Potensi Karyawan melalui Penilaian Masa Percobaan

Main Article Content

Asradiani Novia
Imam Yuadi


Effective corporate governance necessitates the continual nurturing and cultivation of employee potential for long-term professional success. However, assessing an employee's potential and performance objectively and consistently from the start of their career presents a substantial difficulty in reducing any mismatches with the company's goals and expectations. This study introduces a predictive methodology that uses probationary employee performance to map their potential. The study focuses on Performance (Y-axis) and Potential (X-axis) variables using data from 265 employees at Company X who went through a probationary period. Various machine learning models, including Logistic Regression, Naive Bayes, k-NN, SVM, and Decision Tree, were used to analyze data using Orange Data Mining software. The Logistic Regression model has the highest accuracy, at 90% (0.906). Validity testing, using the Confusion Matrix, allowed individuals to be classified into nine potential groups, in accordance with the 9-Box Matrix Talent Management paradigm. This classification provides HR with a strategic tool for tailoring career development strategies based on expected potential within their respective sectors.

Article Details



Abedi-Boafo, E., Duoduaa Nyarko-Tetteh, A., & Tachie-Menson, R. (2019). Assessment of Support Services Available For Staff On Probation in University of Education, Winneba. In Global Journal of Human Resource Management (Vol. 7, Issue 2).
Academy of Innovate HR, & Erik van Vulven. (2020). The 9 Box Grid: A Practitioner's Guide.
Alabadee, S., & Thanon, K. (2021). Evaluation and Implementation of Malware Classification Using Random Forest Machine Learning Algorithm. 7th International Conference on Contemporary Information Technology and Mathematics, ICCITM 2021, 112–117.
Al-Darraji, S., Honi, D. G., Fallucchi, F., Abdulsada, A. I., Giuliano, R., & Abdulmalik, H. A. (2021). Employee attrition prediction using deep neural networks. Computers, 10(11).
Alduayj, S. S., & Rajpoot, K. (2019). Predicting Employee Attrition using Machine Learning. Proceedings of the 2018 13th International Conference on Innovations in Information Technology, IIT 2018, 93–98.
Alziari, L. (2017). A chief HR officer's perspective on talent management. Journal of Organizational Effectiveness, 4(4), 379–383.
Balfour, D. L., & Neff, D. M. (1993). Predicting and Managing Turnover in Human Service Agencies: A Case Study of an Organization in Crisis. Public Personnel Management, 22(3), 473–486.
Baytar, C. U. (2022). Data analytics applied to the human resources industry. In The Future of Data Mining.
Chung, D., Yun, J., Lee, J., & Jeon, Y. (2023). Predictive model of employee attrition based on stacking ensemble learning. Expert Systems with Applications, 215, 119364.
Dewettinck, K., & van Dijk, H. (2013). Linking Belgian employee performance management system characteristics with performance management system effectiveness: Exploring the mediating role of fairness. In International Journal of Human Resource Management (Vol. 24, Issue 4, pp. 806–825).
Garavan, T. N., Carbery, R., & Rock, A. (2012). Mapping talent development: Definition, scope and architecture. In European Journal of Training and Development (Vol. 36, Issue 1, pp. 5–24).
Gaudêncio, P., Coelho, A., & Ribeiro, N. (2014). Organisational CSR practices: Employees' perceptions and impact on individual performance. In International Journal of Innovation Management (Vol. 18, Issue 4). World Scientific Publishing Co. Pte Ltd.
Jain, R., & Nayyar, A. (2018). Predicting employee attrition using xgboost machine learning approach. Proceedings of the 2018 International Conference on System Modeling and Advancement in Research Trends, SMART 2018, 113–120.
Jin, S.-B., & Lee, J.-W. (2017). Study on accident prediction models in urban railway casualty accidents using logistic regression analysis model. Journal of the Korean Society for Railway, 20(4), 482–490.
Kabalina, V., & Osipova, A. (2022). Identifying and assessing talent potential for future needs of a company. Journal of Management Development, 41(3), 147–162.
Mabe, D., Esmael, G., Burg, M., Soares, P., & Halawi, L. (2022). Optimization of Organizational Design. Journal of Computer Information Systems, 62(4), 717–729.
McKinsey, G. (2008, September). Enduring Ideas: The GE–McKinsey nine-box matrix.
Qutub, A., Al-Mehmadi, A., Al-Hssan, M., Aljohani, R., & Alghamdi, H. S. (2021). Prediction of Employee Attrition Using Machine Learning and Ensemble Methods. International Journal of Machine Learning and Computing, 11(2), 110–114.
Sunarti, S., Wahyono, I. D., Putranto, H., Saryono, D., Akhmad Bukhori, H., & Widyatmoko, T. (2021). Optimation Parameter and Attribute Naive Bayes in Machine Learning for Performance Assessment in Online Learning. Proceedings - 4th International Conference on Vocational Education and Electrical Engineering: Strengthening Engagement with Communities through Artificial Intelligence Application in Education, Electrical Engineering and Information Technology, ICVEE 202.
Susilowati, R., & Fahmie, A. (2020). Talent mapping training to improve people analytics efficacy of university students. Proceedings of the International Conference on E-Learning, ICEL, 2020-December, 330–334.
Torre, C., Tommasetti, A., & Maione, G. (2020). Technology usage, intellectual capital, firm performance and employee satisfaction: the accountants' idea. TQM Journal, 33(3), 545–567.
Tu, P.-L., & Chung, J.-Y. (1992). A new decision-tree classification algorithm for machine learning. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, 1992-Novem, 370–377.
Wahyuningtyas, R., Alamsyah, A., & Diliana, N. A. (2021). Mapping Digital Talent Based on Competency using Social Network Analysis. Proceeding - 2021 2nd International Conference on ICT for Rural Development, IC-ICTRuDev 2021.
Wiratama, G. P., & Rusli, A. (2019). Sentiment analysis of application user feedback in Bahasa Indonesia using multinomial naive bayes. Proceedings of 2019 5th International Conference on New Media Studies, CONMEDIA 2019, 223–227.