Computational Modeling Using Linear Regression and Random Forest to Analyze the Impact of Workload on Employee Performance Evaluations

Each employee has a number of workloads in the form of tasks and responsibilities that must be completed within a certain period of time.There are several aspects that are assessed in performance evaluations, such as the number of working hours per week, the number of projects handled, the number of overtime hours to complete work, the number of sick days, the number of team members, the number of hours to develop self-skills, job promotion offers, etc.All of these aspects certainly impact performance scores, employee satisfaction scores, and the ability to survive.Excessive workload will have a negative impact on physical and mental health, performance, and employee satisfaction levels.This study aims to analyze the results of employee performance evaluations based on workload factors using machine learning approaches such as linear regression and random forest.The computational results will be used to compare the effectiveness of machine learning models and analyze the accuracy of the assessment results.The significance of this study lies in its potential to enhance employee performance management systems by providing accurate, data-driven insights for decision-making processes such as promotions, compensation, and workforce planning.Practical and fair employee performance assessments will enable decision-makers to make informed choices regarding job promotions, salary increases, annual bonuses, and employee career development.