Integrating Machine Learning with HRIS to Optimize Workforce Management

Abstract

The operation of HRIS & their key influence on organizational employee record handling and data informed decision-making in HRIS are of a noteworthy importance. Organization efficiency improvement, especially in organizational resource distribution, possible through forecasting employee performance and turnover metrics, is possible through HRIS utilization and ML (machine learning) application. Therefore, this research is motivated in part by trying to improve the capabilities of HRIS through the development of a proposed optimized model, SHROF (Smart HR Optimization Forest). The research makes use of the employee records of a given organization. The data is subjected to Z Score Normalization in the preprocessing stage in order to scale the numerical attributes, which in this case are salary and performance. To achieve this, PCA (Principal Component Analysis) is utilized. The SHROF model has made noteworthy gains in terms of accuracy Improvement in prediction, thereby facilitating effective decision making and improved employee performance. Regulatory submissions indicate SHROF to be 99.3% accurate, 92.7% precise, with 78% and 84% of the F1 score corresponding to 92.7% recall, thereby shining a spotlight on the model to predict the above metrics with considerable accuracy which strengthens its adaptability to a broad class of HRD (high dimensional HR data to be derived. This with the added competence of employee attrition to optimize HRD (high dimensional data) to improve resource utilization and the overall efficiency of the managerial decision making in the HR domain Determining the competence of the model, SHROF predict employee turnover providing a clear pathway in the refinement of effective HR D (High dimensional HR data). Overall SHROF provides a clear pathway to the refinement of effective organizational HRIS.