Title: Machine learning-based-HR appraisal system (ML-APS)

Authors: Madapuri Rudra Kumar; Vinit Kumar Gunjan; Mohd Dilshad Ansari

Addresses: Department of Computer Science and Engineering, G Pullaiah College of Engineering and Technology, Kurnool, Andhra Pradesh, India ' Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, Telangana, India ' Department of Computer Science and Engineering, CMR College of Engineering & Technology, Hyderabad, Telangana, India

Abstract: Appraisal systems hold critical importance in organisational human resource management. The way HR departments have developed over the period to the recent trends of AI-based human resource management systems and practices reflect on the emerging importance of effective HRM. In this present work, one of the key functionalities of the HRM process, the Appraisal system, is focused upon. This work presents a comprehensive model of appraisal system that relies on the machine learning solution for predicting evaluating the appraisal score. The developed model is trained with SVM classifier and is tested with 600+ records for evaluation. The precision and recall values indicated by the test results reflect that the model is potential and if more effectively pursued in terms of training and incorporating more in-depth analysis, the model can be a sustainable solution for human resource appraisal system.

Keywords: machine learning-based appraisal system; ML-APS; 360-degree performance system analysis.

DOI: 10.1504/IJAMS.2023.131669

International Journal of Applied Management Science, 2023 Vol.15 No.2, pp.102 - 116

Received: 09 Sep 2019
Accepted: 27 Nov 2020
Published online: 26 Jun 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article