Decision Support System for Determining the Eligibility of Economically Disadvantaged Students for Assistance Using the K-Means and MOORA Methods - Gilbert Johan Martin Sinaga (Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon, 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.