<![CDATA[Student graduation is an important indicator in measuring the effectiveness of the learning process and the success of educational institutions. Low graduation rates can be influenced by various factors, such as academic grades, attendance, and other factors. Using the K-Nearest Neighbor algorithm method, the distance between training data attributes and new input data attributes will be predicted using the Euclidean Distance calculation. Graduation factors considered include grades, attendance, extracurricular activities, attitudes, achievements, and parental education. The results of the study show that the program produces a prediction system with maximum accuracy. However, there are still several program shortcomings that need to be improved, such as increasing the amount of data and also considering the influence of other factors.]]>
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