<![CDATA[Giving rewards to school students is one strategy to increase learning motivation and participation in school. However, the system used to give rewards is usually still conventional, so it often faces challenges in terms of objectivity and comprehensiveness of assessment criteria. This study aims to apply the K-Nearest Neighbor (KNN) algorithm as an optimization tool in determining student reward recipients in High Schools. The data used include the average report card value, moral values, parents' income, number of siblings and scores in non-academic activities. The KNN method was chosen because of its ability to classify based on the similarity of neighbor data. The research process begins with collecting historical student data, data normalization, determining the KNN model, and evaluating the model. The results of the study show that the KNN model is able to classify students with a certain level of accuracy in recommending the right reward category. The conclusion of this study is that the application of the KNN algorithm can provide a more structured and objective approach to the reward giving process, so that it can help schools make transparent decisions and in accordance with the principles of justice. This system is expected to increase the effectiveness of the reward program and encourage development for students. Keyword : K-Nearest Neighbor, Reward, Classification, Objectivity, Optimization]]>
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