<![CDATA[Financial Well-Being is the condition that a person has been able to meet current and future financial obligations. There are many parameters in determining people who have obtained financial well-being. Classification is a data mining task that can be used to identify someone with financial well-being. One of the most popular classification algorithms is K Nearest Neighbor (KNN). However, there is also a Modified K Nearest Neighbor (MKNN) classification algorithm which is an extended KNN. In this paper, we will analyze a comparison of KNN and MKNN algorithms to classify financial well-being datasets. Comparative analysis is based on the accuracy and running time of both algorithms. Prior to the classification process, K-Fold Cross Validation was performed to find the optimal data modeling. The results of the K Fold Cross Validation modeling will be a model for the sample of training data and data testing. Evaluation of classification results based on precision, recall, and F-1. The test resulted in a higher KKN performance compared to MKNN in all test parameters, with an average gap of 25 percent. In addition, it was also found that the execution time of the KNN algorithm was faster than that of the MKNN]]>
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