<i>RanKer</i>: An AI-Based Employee-Performance Classification Scheme to Rank and Identify Low Performers
Keyur Patel,
Karan Sheth,
Dev Mehta,
Sudeep Tanwar,
Bogdan Cristian Florea,
Dragos Daniel Taralunga,
Ahmed Altameem,
Torki Altameem,
Ravi Sharma
Affiliations
Keyur Patel
Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Karan Sheth
Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Dev Mehta
Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Sudeep Tanwar
Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Bogdan Cristian Florea
Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 061071 Bucharest, Romania
Dragos Daniel Taralunga
Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 061071 Bucharest, Romania
Ahmed Altameem
Computer Science Department, Community College, King Saud University, Riyadh 11451, Saudi Arabia
Torki Altameem
Computer Science Department, Community College, King Saud University, Riyadh 11451, Saudi Arabia
Ravi Sharma
Centre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, P.O. Bidholi Via-Prem Nagar, Dehradun 248007, India
An organization’s success depends on its employees, and an employee’s performance decides whether the organization is successful. Employee performance enhances the productivity and output of organizations, i.e., the performance of an employee paves the way for the organization’s success. Hence, analyzing employee performance and giving performance ratings to employees is essential for companies nowadays. It is evident that different people have different skill sets and behavior, so data should be gathered from all parts of an employee’s life. This paper aims to provide the performance rating of an employee based on various factors. First, we compare various AI-based algorithms, such as random forest, artificial neural network, decision tree, and XGBoost. Then, we propose an ensemble approach, RanKer, combining all the above approaches. The empirical results illustrate that the efficacy of the proposed model compared to traditional models such as random forest, artificial neural network, decision tree, and XGBoost is high in terms of precision, recall, F1-score, and accuracy.