Published in Data Science and Engineering (DSE) Record 2023 Vol. 4 No. 1 pp. 125-131
Abstract
The occurrence of imbalanced class in a dataset causes the classification re-sults to tend to the class with the largest amount of data (majority class). A sampling method is needed to balance the minority class (negative class) so that the class distribution becomes balanced and leading to better classifi-cation results. This study was conducted to overcome imbalanced class problems on the Nonoperative reduction of intussusception dataset using ADASYN, SMOTE-NC and k-means-SMOTE. The dataset has 173 in-stances of the positive class (majority class) and 79 instances of the nega-tive class (minority class) by comparing the classification (Logistic Regres-sion, SVM, and Decision Tree) while implementing Decision Tree with SMOTE-NC Oversampling and Decision Tree with K-means SMOTE Over-sampling has the highest accuracy of 94%, while Support Vector Machine with Non-Oversampling produces the highest sensitivity of 100%