K-means-SMOTE untuk menangani ketidakseimbangan kelas dalam klasifikasi penyakit diabetes dengan C4.5, SVM, dan naive Bayes - Hairani Hairani; Khurniawan Eko Saputro; Sofiansyah Fadli (Jurnal Teknologi dan Sistem Komputer, 2020)
The occurrence of imbalanced class in a dataset causes the classification results to tend to the class with the largest amount of data (majority class). A sampling method is needed to balance the minority class (positive class) so that the class distribution becomes balanced and leading to better classification results. This study was conducted to overcome imbalanced class problems on the Indian Pima diabetes illness dataset using k-means-SMOTE. The dataset has 268 instances of the positive class (minority class) and 500 instances of the negative class (majority class). The classification was done by comparing C4.5, SVM, and naïve Bayes while implementing k-means-SMOTE in data sampling. Using k-means-SMOTE, the SVM classification method has the highest accuracy and sensitivity of 82 % and 77 % respectively, while the naive Bayes method produces the highest specificity of 89 %.