INTEGRASI TEKNIK SMOTE BAGGING DENGAN INFORMATION GAIN PADA NAIVE BAYES UNTUK PREDIKSI CACAT SOFTWARE - Sukmawati Anggraeni Putri (LPPM Nusa Mandiri, 2017)
The prediction accuracy of defects in code can help direct the test effort, reduce costs and improve software quality. Until now, many researchers have applied various types of algorithm based on machine learning and statistical methods to build predictive performance software defects. One of them uses a machine learning approach to the classification, which is a popular approach to predict software defects. While Naive Bayes one simple classification to have good performance that produces an average probability of 71 percent. As well as the time required in the process of learning faster than on any other machine learning. Additionally, it has a good reputation on the accuracy of the prediction. While NASA MDP is a very popular data used by previous researchers in the development of predictive models of software defects. Because it is common and freely used by researchers. However, these data have deficiencies, including the occurrence of imbalance class and attribute noise. Therefore by using SMOTE (Synthetic Minority Over-Sampling Technique) for sampling techniques and Bagging on the ensemble method, is used to deal with the class imbalance. As for dealing with noise attribute, in this research using information gain in the process of selecting the relevant attributes. So after the trial that the application of the model SMOTE Bagging and Information Gain proven to obtain good results to handled imbalance class and attribute noise at prediction software defects, and can increase the accuracy of the prediction results software defects.