<![CDATA[The process of selecting students for educational assistance in schools is still frequently performed manually by comparing individual student data. This approach is time-consuming and vulnerable to subjectivity and human error. To address this issue, this study developed a Decision Support System (DSS) using the Fuzzy C-Means (FCM) algorithm to cluster students based on their eligibility level for aid. FCM is chosen due to its capability to categorize data into multiple clusters based on data similarity. Seven evaluation criteria were employed: father's income, mother's income, father's education, mother's education, birth order, number of siblings, and type of transportation to school. The dataset consists of student information that was preprocessed and weighted based on a predefined scale. Clustering was conducted with a maximum iteration of 100 and an error tolerance of 0.00001. The results indicate that FCM successfully grouped the students into two clusters, with 487 students classified as eligible for assistance and 202 students as ineligible. To validate the clustering, the results were compared with the official Dapodik dataset, which demonstrated a high degree of consistency. Therefore, the implementation of FCM in this decision support system has proven to be effective in producing objective, accurate, and efficient classifications.]]>
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