Forecasting Employee Potential through Probationary Assessment Memperkirakan Potensi Karyawan melalui Penilaian Masa Percobaan
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Published:
Sep 27, 2023
Keywords:
Probationary Period, Talent Management, Employee's Potential, Predictive Model, Machine Learning Models
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Abstract
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.
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Copyright (c) 2023 Asradiani Novia, Imam Yuadi
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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). www.eajournals.org
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. https://doi.org/10.1109/ICCITM53167.2021.9677693
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). https://doi.org/10.3390/computers10110141
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. https://doi.org/10.1109/INNOVATIONS.2018.8605976
Alziari, L. (2017). A chief HR officer's perspective on talent management. Journal of Organizational Effectiveness, 4(4), 379–383. https://doi.org/10.1108/JOEPP-05-2017-0047
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. https://doi.org/10.1177/009102609302200310
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. https://doi.org/10.1016/j.eswa.2022.119364
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). https://doi.org/10.1080/09585192.2012.700169
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). https://doi.org/10.1108/03090591211192601
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. https://doi.org/10.1142/S136391961450025X
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. https://doi.org/10.1109/SYSMART.2018.8746940
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. https://doi.org/10.7782/JKSR.2017.20.4.482
Kabalina, V., & Osipova, A. (2022). Identifying and assessing talent potential for future needs of a company. Journal of Management Development, 41(3), 147–162. https://doi.org/10.1108/JMD-11-2021-0319
Mabe, D., Esmael, G., Burg, M., Soares, P., & Halawi, L. (2022). Optimization of Organizational Design. Journal of Computer Information Systems, 62(4), 717–729. https://doi.org/10.1080/08874417.2021.1906783
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. https://doi.org/10.18178/ijmlc.2021.11.2.1022
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. https://doi.org/10.1109/ICVEE54186.2021.9649661
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. https://doi.org/10.1109/econf51404.2020.9385483
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. https://doi.org/10.1108/TQM-04-2020-0070
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. https://doi.org/10.1109/TAI.1992.246431
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. https://doi.org/10.1109/IC-ICTRuDev50538.2021.9656521
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. https://doi.org/10.1109/CONMEDIA46929.2019.8981850
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. https://doi.org/10.1109/ICCITM53167.2021.9677693
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). https://doi.org/10.3390/computers10110141
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. https://doi.org/10.1109/INNOVATIONS.2018.8605976
Alziari, L. (2017). A chief HR officer's perspective on talent management. Journal of Organizational Effectiveness, 4(4), 379–383. https://doi.org/10.1108/JOEPP-05-2017-0047
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. https://doi.org/10.1177/009102609302200310
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. https://doi.org/10.1016/j.eswa.2022.119364
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). https://doi.org/10.1080/09585192.2012.700169
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). https://doi.org/10.1108/03090591211192601
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. https://doi.org/10.1142/S136391961450025X
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. https://doi.org/10.1109/SYSMART.2018.8746940
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. https://doi.org/10.7782/JKSR.2017.20.4.482
Kabalina, V., & Osipova, A. (2022). Identifying and assessing talent potential for future needs of a company. Journal of Management Development, 41(3), 147–162. https://doi.org/10.1108/JMD-11-2021-0319
Mabe, D., Esmael, G., Burg, M., Soares, P., & Halawi, L. (2022). Optimization of Organizational Design. Journal of Computer Information Systems, 62(4), 717–729. https://doi.org/10.1080/08874417.2021.1906783
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. https://doi.org/10.18178/ijmlc.2021.11.2.1022
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. https://doi.org/10.1109/ICVEE54186.2021.9649661
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. https://doi.org/10.1109/econf51404.2020.9385483
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. https://doi.org/10.1108/TQM-04-2020-0070
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. https://doi.org/10.1109/TAI.1992.246431
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. https://doi.org/10.1109/IC-ICTRuDev50538.2021.9656521
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. https://doi.org/10.1109/CONMEDIA46929.2019.8981850