ENHANCING JOB FIT PREDICTION IN CORPORATIONS – A COMPARATIVE MACHINE LEARNING STUDY UTILIZING GRADIENT BOOSTING - Bondan Ari Wijaya; Imam Yuadi (RADJA PUBLIKA, 2026)
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.