Intelligent Decision Support System Using MOORA Method for Admission Management

Abstract

Admission management requires objective and transparent evaluation methods to ensure fairness and efficiency in both educational and healthcare institutions. Traditional selection processes often rely on subjective judgment, leading to bias and inconsistency. This study proposes an Intelligent Decision Support System (IDSS) using the Multi-Objective Optimization by Ratio Analysis (MOORA) method to optimize multi-criteria admission decisions. The system was developed and validated using real admission data from Madrasah Aliyah Negeri 1 Palembang, Indonesia, and designed for adaptability in healthcare contexts such as patient triage or staff recruitment. The MOORA approach was applied to normalize and weight four evaluation criteria, academic performance, written test, religious knowledge test, and interview results yielding objective and transparent rankings. The developed web-based IDSS, implemented using PHP, MySQL, and Apache, processed 340 applicant records in less than two seconds with consistent outcomes matching expert judgment. The findings confirm that mathematical optimization within intelligent frameworks can significantly enhance fairness, transparency, and reproducibility in admission evaluations across domains. This study contributes to the Intelligent Computing and Health Informatics field by demonstrating how MOORA can bridge educational and healthcare decision systems through a unified multi-criteria evaluation model. Future work will explore machine learning based adaptive weighting and fuzzy extensions of MOORA to address uncertainty and improve scalability in broader institutional applications.