Implementing AI in HRM: Leveraging Machine Learning for Smart Recruitment Systems

Resume-Efficient candidate selection has long been a priority for recruiters and businesses, driven by the need to reduce costs and streamline the hiring process. The accuracy of predictive models crucially hinges on the selection of discriminant va-riables, which significantly impact candidate assess-ment. In response, this study focuses on enhancing job performance prediction systems using K-Nearest Neighbors (KNN), Logistic Regression, and Support Vector Machine (SVM) models, all optimized through Sequential Forward Selection (SFS). Leveraging his-torical employee data and conditions, this system improves recruitment screening in a four-stage process encompassing data collection, preprocessing, model development, optimization and evaluation. Our comprehensive study compares the performance of each model, providing valuable insights for practitioners and researchers alike, emphasizing the pivotal role of AI and machine learning in human resource manage-ment.