Development of a Web Application for Candidate Screening and Qualification Assessment Using Artificial Intelligence to Support Human Resource Management

Abstract

This research aims to develop an AI-powered job application screening system to enhance the efficiency of organizational recruitment and selection processes amidst the challenges of a growing applicant pool. The developed system serves as a centralized platform for storing applications and analyzing data from resumes. By integrating Natural Language Processing (NLP) techniques with a Rule-based AI approach, the system calculates suitability scores (0-100) and automatically ranks candidates based on predefined criteria, including skills, education, and experience. The development followed the Agile methodology, focusing on continuous Rule Refinement to improve assessment accuracy and transparency.

           The evaluation results indicate that the system reduces screening time by an average of 30% and increases candidate matching accuracy by 20% compared to traditional methods. Furthermore, regression testing conducted after rule refinement showed 100% alignment with HR professional decisions (15 out of 15 samples). Regarding user satisfaction, most users were highly satisfied with the system, reporting an average score of 4.62 out of 5.00. This study reflects the potential of AI to elevate recruitment processes, making them faster, more transparent, and better aligned with digital-era human resource management.