Integrasi SMOTE pada Naive Bayes dan Logistic Regression Berbasis Particle Swarm Optimization untuk Prediksi Cacat Perangkat Lunak - Andre Hardoni; Dian Palupi Rini; Sukemi Sukemi (STMIK Budi Darma, 2021)
Software defects are one of the main contributors to information technology waste and lead to rework, thus consuming a lot of time and money. Software defect prediction has the objective of defect prevention by classifying certain modules as defective or not defective. Many researchers have conducted research in the field of software defect prediction using NASA MDP public datasets, but these datasets still have shortcomings such as class imbalance and noise attribute. The class imbalance problem can be overcome by utilizing SMOTE (Synthetic Minority Over-sampling Technique) and the noise attribute problem can be solved by selecting features using Particle Swarm Optimization (PSO), So in this research, the integration between SMOTE and PSO is applied to the classification technique machine learning naïve Bayes and logistic regression. From the results of experiments that have been carried out on 8 NASA MDP datasets by dividing the dataset into training and testing data, it is found that the SMOTE + PSO integration in each classification technique can improve classification performance with the highest AUC (Area Under Curve) value on average 0,89 on logistic regression and 0,86 in naïve Bayes in the training and at the same time better than without combining the two.