Decision Support System for Determining the Eligibility of Economically Disadvantaged Students for Assistance Using the K-Means and MOORA Methods - Gilbert Johan Martin Sinaga (ITEJ (Information Technology Engineering Journals), 2025)
The determination of recipients for student financial aid often faces challenges related to subjectivity in the selection process, necessitating a system capable of conducting objective analysis. This study develops a Decision Support System using the K-Means method to cluster students based on similar socioeconomic characteristics and the MOORA method to rank aid recipients more accurately. The K-Means method is applied to classify students into three clusters based on parental income, number of dependents, and academic performance. The clustering results indicate that students in Cluster 1 belong to the lowest economic group, making them the top priority in the selection process. Subsequently, the MOORA method is used to rank students within Cluster 1 based on an optimal value calculated from the weighted benefit and cost criteria. This calculation produces a priority ranking that is more transparent and objective compared to conventional selection systems. The findings show that the combination of K-Means and MOORA methods enhances accuracy in selecting aid recipients while reducing subjectivity in the selection process. With this system, schools or relevant institutions can expedite decision-making and ensure that aid is distributed to the students most in need. This study is expected to serve as a solution for educational institutions in improving the effectiveness and efficiency of student welfare programs.