<![CDATA[The full-day school policy extends the duration of learning activities at school with the aim of improving the quality of education and character building of students. In practice, the addition of study time does not always run in line with student readiness, thus giving rise to various complaints related to fatigue, difficulty concentrating, excessive assignment burden, and lack of rest time. The diversity of these complaints is complex and cannot be analyzed subjectively, so a structured approach is needed to identify the dominant patterns of complaints objectively. This study applies the data mining approach through the k-means clustering algorithm to group student complaints against the implementation of full-day school. The data used are 523 records obtained from a questionnaire, with three variables, namely class, complaint, and student suggestion, which are then transformed into numerical values and processed using MATLAB R2014a. The grouping is tested with three configurations, namely 3, 4, and 5 clusters, and the cluster quality is evaluated based on the cluster variance value. The results show that the configuration of 5 clusters produces the lowest cluster variance of 20.525 compared to 3 clusters (40.767) and 4 clusters (31.681), so that 5 clusters is the most optimal and representative configuration. The type of complaint is the most dominant factor in shaping the grouping pattern. These findings can be used by the school as a basis for evaluating the implementation of full-day school in a more targeted manner in accordance with the characteristics of student complaints.]]>
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