Class imbalances in data mining research are quite detrimental because there are difficulties in classifying minority classes (small number of instances) correctly. Oversampling is a method of balancing class distribution by randomly replicating instances in minority classes. multiclass classification might achieve a lower performance than binary classification as the boundaries among the classes may overlap. This issue may become more complex when facing imbalanced data. This study presents test results giving different k values in nearest neighbor searches which are used to balance the class using the ADASYN-N method. The results show that giving different k values can affect the performance of the ADASYN-N method depending on the number of dataset instances.