Hybrid resampling to handle imbalanced class on classification of student performance in classroom
Yoga Pristyanto, Noor Akhmad Setiawan, Igi Ardiyanto · 2017
Conventional class with many students leads learning materials to be not well absorbed by the students, consequently the results of student learning to be less than the maximum. Therefore, the process of prediction on the success rate of students should be done as early as possible in order to reduce the impact of the problem. The prediction process is done using various methods of data mining based on the classification pattern of the dataset to be processed. In the process, these datasets often have an unbalanced class distribution, it can be a serious constraint when applied to various algorithms for data classification. Therefore, this study discusses the handling of the dataset imbalance using a combination of the SMOTE and OSS methods. The SMOTE and OSS methods work by balancing the distribution of classes on the dataset, this will increase the value of g-mean when implemented on various classification algorithms. In the experiment, the classification algorithms used in this research are, K-NN, Naïve Bayes, and SVM. From the test result, the combination of SMOTE and OSS method can increase the g-mean value of KNN algorithm from 85,519% to 89,367%, Naïve Bayes algorithm from 82,482% to 85,416% and SVM algorithm from 85,829% to 96,503%. These suggest that the combination of the SMOTE and OSS methods can be a solution to address the unbalanced distribution of classes in data mining processes.