<![CDATA[In the modern era of computers and the internet, the need for effective data analysis tools is increasing. In the data mining industry, RapidMiner has developed into an essential tool for using simple but effective classification methods such as k-Nearest Neighbors (KNN). The purpose of this study is to evaluate RapidMiner's ability to implement KNN and evaluate its performance on various datasets derived from various characteristics. Test results show that RapidMiner can implement KNN with satisfactory accuracy for most datasets; The methodology used measures classification performance through metrics such as accuracy, recall, precision, and F1 score. In conclusion, RapidMiner has a lot of potential for practical application of KNN, but it has limitations when working with large datasets.]]>
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