<![CDATA[Population data plays a crucial role in development planning, yet its utilization has not yet been optimized to generate truly informative insights. This study aims to classify population growth rates across provinces in Indonesia into low, medium, and high categories using the K-Nearest Neighbor (KNN) algorithm. The research process includes data collection and preprocessing, category labeling using the quantile method, data normalization via Min-Max Scaling, and splitting the data into training and test sets with a 70:30 ratio. The KNN model was built using parameter values of k, namely 3, 5, and 7, and the best value of k was selected based on the model evaluation results. Evaluation was performed using a confusion matrix by calculating the accuracy value. The test results showed that the best model was obtained at k = 5 with an accuracy of 75%. These findings indicate that KNN can identify similarities in demographic characteristics across provinces quite well, although there are still classification errors in classes with closely related characteristics. Therefore, the KNN method can serve as a simple and effective approach for population data analysis and has the potential to support data-driven decision-making.]]>
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