A Web-Based Human Resource Management System with Machine Learning Techniques

Gideon Muleme (), Shubi Kaijage () and Ben Ruhinda ()
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Gideon Muleme: School of Computational and Communication Science and Engineering (CoCSE), Nelson Mandela African Institution of Science and Technology (NM-AIST)
Shubi Kaijage: School of Computational and Communication Science and Engineering (CoCSE), Nelson Mandela African Institution of Science and Technology (NM-AIST)
Ben Ruhinda: The Inter-University Council for East Africa (IUCEA)

A chapter in Smart and Secure Embedded and Mobile Systems, 2024, pp 35-45 from Springer

Abstract: Abstract Human Resources Management (HRM) is a very important aspect of any organization as it involves several core company activities that include but are not limited to recruitment, training of employees, establishing a healthy company culture and alignment of employee performance with the company’s strategic goals. HRM has evolved over the years from mere administrative duties such as processing employees’ salaries to devising strategic policies for organizations. This paper presents a human resource management system in the form of an interactive web application integrated with machine learning models that are utilized in the employee recruitment process. The web application was developed using Hyper Text Mark Language (HTML), Cascading Style Sheets (CSS), bootstrap, JavaScript and the CodeIgniter framework. The MySQL database is used for storage as well as retrieval of system data. The system consists of six core modules of recruitment, employment onboarding and administration, leave management, payroll management, employment management and reporting. Three classification algorithms i.e., logistic regression, random forest classifier and Support Vector Machine (SVM) classifier were used in the training of the machine learning models for three job positions i.e., executive secretary, principal internal auditor and principal human resources and administration. The random forest classifier demonstrated the best performance for both the principal human resource and administration and executive secretary job position with accuracies of 98.74% and 99.98% respectively while the SVM classifier exhibited the best performance for the principal internal auditor job position with an accuracy of 98.58%.

Keywords: Human resources management; Web application; Machine learning; Human resources (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-56603-5_4

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