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All Journal International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET)
Bondan Ari Wijaya
Universitas Airlangga
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education

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ENHANCING JOB FIT PREDICTION IN CORPORATIONS – A COMPARATIVE MACHINE LEARNING STUDY UTILIZING GRADIENT BOOSTING Bondan Ari Wijaya; Imam Yuadi
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 5 No. 4 (2026): MARCH
Publisher : RADJA PUBLIKA

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Abstract

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.
Co-Authors Imam Yuadi