Optimization-based Techniques Prediction Model in Determining Employee Turnover

Abstract

Employee turnover is a vital challenge for organizations, often resulting in high financial and operational costs. Traditional methods of turnover prediction have been limited in their ability to capture the complexity of factors influencing employee turnover, leading to ineffective retention strategies. This study proposes a novel approach by integrating Genetic Algorithms (GAs) with machine learning models to enhance the accuracy of turnover predictions. The research introduces an optimization-based prediction model utilizing GAs combined with Naïve Bayes and KNN algorithms. Four crossover operators, including traditional AX and three advanced operators, CAX, IBAX, and FMSAX, are employed to optimize the model’s feature selection and parameters. A dataset of employee turnover records from 2015 to 2023, encompassing various demographic and job-related factors, was used to train and test the model. The results demonstrate that the FMSAX operator, paired with KNN, achieves the highest predictive accuracy, precision, F-measure, and recall compared to other combinations. The findings suggest that the GA-optimized model significantly improves the ability to predict employee turnover and offers actionable insights for developing effective retention strategies. This study contributes to the field by providing a robust tool for human resource management, capable of adapting to dynamic workforce changes and minimizing turnover rates, ultimately enhancing organizational performance. The proposed model improves predictive accuracy and ensures its applicability across different organizational contexts by allowing the integration of new HR data.