Intelligent Prediction of Human Resource Requirements in Organizations: A Data Mining Approach

Objective: Data mining plays a crucial role in optimizing human resource management by uncovering valuable patterns from large datasets. This study aims to predict human resource requirements using a novel approach that combines neural networks with the Whale Optimization Algorithm (WOA) and Simulated Annealing (SA). Method: This applied, quantitative research analyzed a dataset of 30,000 records. The proposed method integrates neural networks' pattern recognition capabilities with WOA and SA's optimization strengths to enhance the accuracy and efficiency of human resource needs prediction. The hybrid model was designed to address limitations in traditional forecasting methods. Findings: The hybrid model demonstrated significant improvements over traditional methods, achieving a 15% increase in prediction accuracy and a 20% reduction in computational time. It also showed enhanced robustness when tested with varied input parameters, maintaining consistent performance across different scenarios. Conclusion: This study underscores the potential of advanced data mining techniques in enhancing human resource planning. The proposed method offers organizations a more reliable tool for forecasting staffing needs and making strategic decisions. Future research should explore its applicability across various industries and investigate its potential for real-time workforce optimization. Additionally, integrating this model with existing HR systems could provide a comprehensive solution for dynamic workforce management.