<![CDATA[Academic stress is a common problem experienced by students due to the burden of assignments, exams, and social pressures. If not managed properly, it can impact achievement and psychological well-being. This study aims to classify the academic stress levels of students at the Faculty of Science and Technology, Sultan Syarif Kasim State Islamic University, Riau, using the Fuzzy C-Means (FCM) algorithm, which allows flexibility in the degree of data membership in more than one cluster. Data were obtained from a modified Perception of Academic Stress Scale (PASS) questionnaire, with 587 respondents from the 2021–2024 intake. The research stages included data selection, cleaning, and transformation, application of the FCM algorithm, and evaluation using three validation metrics: the Partition Coefficient Index (PCI), the Fuzzy Silhouette Index (FSI) and the Silhouette Coefficient. The test results showed the optimal number of clusters at C = 2, with the highest PCI value of 0.5663, FSI and ilhouette Coefficient score of 0.3056, resulting in two groups of students: 313 with high stress levels and 274 with low stress levels. The decrease in PCI, FSI and Silhouette scores across a larger number of clusters indicates that dividing two clusters provides the clearest grouping structure. These findings demonstrate that the FCM algorithm is effective in mapping students' academic stress patterns and can be used as a basis for designing more targeted academic mentoring strategies, counseling services, and psychological intervention programs services.]]>
