<![CDATA[Student graduation rates are influenced by various academic and non-academic factors, making it necessary to develop analytical methods to classify students based on their likelihood of graduation. This study applies the K-Nearest Neighbors (KNN) algorithm to analyze the factors affecting student graduation at SD Negeri 112269 Padang Lais. The KNN algorithm works by calculating the Euclidean distance between the tested student data and other student data, then determining the graduation status based on the majority of the K nearest neighbors. The results indicate that using K=5 produces highly accurate classifications with an accuracy rate of 100%, where students with the smallest distance to those who have graduated are more likely to pass. The contribution of this study is to demonstrate that the KNN method can serve as a decision-support tool for predicting student graduation and provide insights into the use of classification algorithms in educational decision-making. Future research can enhance the model by incorporating more diverse variables and testing it on larger datasets to improve prediction generalization.]]>
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