K-NEAREST NEIGHBOR DENGAN ADAPTIVE BOOSTING DAN SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE UNTUK KLASIFIKASI DATA TIDAK SEIMBANG - Ria Sulistyo Yuliani; Agus Rusgiyono; Rukun Santoso (Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro, 2023)
Breast cancer is non-skin cancer that is caused by several factors, including glandular ducts, cells, and breast support tissue, except for the skin of the breast. Breast cancer if not treated immediately will be fatal for the sufferer, so early detection of breast cancer is important for the patient's safety. The success of breast cancer detection depends on the right diagnosis. Measurement of the accuracy of a breast cancer diagnosis can be assisted by statistical methods, namely classification. K-Nearest Neighbor is a classification algorithm based on the nearest neighbor that is easy to implement. In the classification process, there are several problems including when faced with imbalanced data. Imbalanced data can cause classification algorithms to tend to focus on the majority class. Data imbalance can be overcome by using Synthetic Minority Oversampling Technique (SMOTE). Ensemble methods can be applied to improve the performance of imbalanced data classification, one of which is Adaptive Boosting. This study applies K-Nearest Neighbor combined with Adaptive Boosting and SMOTE for handling imbalanced data classification. The results of this study are, SMOTE can handle the problem of imbalanced data and the application of K-Nearest Neighbor with Adaptive Boosting can produce an accuracy of 80%, a sensitivity of 83,33%, a specificity of 66,67%, and a G-Mean value of 74,54%. So it can be concluded that K-Nearest Neighbor combined with Adaptive Boosting and SMOTE can be applied for handling imbalanced data classification.