The Effect of Class Imbalance Handling on Datasets Toward Classification Algorithm Performance

Class imbalance is a condition where the amount of data in minority class smaller than that majority class. The impact dataset occurrence misclassification, so it can affect classification performance. Various approaches have been taken to deal with problem imbalances such as level approach, algorithmic and cost-sensitive learning. At level, one methods used apply sampling method. In this study, ADASYN, SMOTE, SMOTE-ENN were combined AdaBoost, K-Nearest Neighbor, Random Forest algorithms. purpose study was determine effect handling on tests carried out five datasets based results integration ADASYN gave better compared other model schemes. criteria evaluate include accuracy, precision, true positive rate, negative g-mean score. 5% 10% models.