ENHANCING JOB FIT PREDICTION IN CORPORATIONS – A COMPARATIVE MACHINE LEARNING STUDY UTILIZING GRADIENT BOOSTING
Main Article Content
Bondan Ari Wijaya
Imam Yuadi
The test results demonstrated how well the Gradient Boosting model could predict outcomes, with the model achieving the best performance metrics, such as an overall accuracy of 98% with 10-fold cross-validation. using group learning techniques to evaluate job fit. This remarkable performance was attained despite the organizational dataset's inherent class imbalance. Crucially, the model showed constant effectiveness in every aspect of job fit. The majority class, Perfect Match (98.8%), is divided into groups based on the difference between PeG and PoG. The minor groups, Overqualified (96.2%) and Underqualified (96.5%), are also divided into groups with strong accuracy and memory. "Jenjang - Main Grp "Text" and "PeG" are the two most important things that can tell you work fit," according to the feature importance analysis. These data give us a solid, objective basis for future talent management and placement decisions by clearly demonstrating that there are distinct, data-driven patterns in placing people in jobs at a company. Machine Learning, Job Fit, Human Resources, Gradient Boosting and Personnel Analytics.
[1] Kristof, A. L. (1996). Person-organization fit: An integrative review of its conceptualizations, measurement, and implications. Personnel Psychology, 49(1), 1-49.
[2] Lauver, K. J., & Kristof-Brown, A. (2001). Distinguishing between employees' perceptions of person-job and person-organization fit. Journal of Vocational Behavior, 59(3), 454-470.
[3] Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7, 21.
[4] Malik, A. (2022). Machine learning in HR recruitment: A systematic literature review. Journal of Business Research, 139, 1369-1383.
[5] Greguras, G. J., & Diefendorff, J. M. (2009). Different fits satisfy different needs: Linking person-environment fit to employee commitment and performance using self-determination theory. Journal of Applied Psychology, 94(2), 465.
[6] Stolampros, P., Korfiatis, N., Kourouthanassis, P., & Symitsi, E. (2019). Flying to quality: Cultural influences on online reviews. Journal of Travel Research, 58(3), 496-511.
[7] Saradhi, V. V., & Palshikar, G. K. (2011). Employee churn prediction. Expert Systems with Applications, 38(3), 1999-2006.
[8] Verguer, M. L., Beehr, T. A., & Wagner, S. H. (2003). A meta-analysis of relations between person-organization fit and work attitudes. Journal of Vocational Behavior, 63(3), 473-489.
