Determining classes for outstanding students based on exam results is a crucial step in promoting the improvement of learning quality. This study applied a data mining method using the K-Means Clustering algorithm to group students based on their exam results. The process includes collecting exam score data, preprocessing the data, and applying the K-Means algorithm to form several student groups based on their achievement levels. Through this algorithm, students are clustered into groups with similar characteristics, such as excellent, average, and those requiring more attention. The study's results indicate that the K-Means Clustering approach can provide an accurate representation of the distribution of student abilities, serving as a basis for designing more effective and equitable learning strategies. This implementation is expected to help schools identify students' potential more objectively and enhance overall educational quality.
