PENGARUH SOFTWARE METRIK PADA KINERJA KLASIFIKASI CACAT SOFTWARE DENGAN ANN - Achmad Zainudin Nur; Mohammad Reza Faisal; Friska Abadi; Irwan Budiman; Rudy Herteno (Fakultas MIPA Universitas Lambung Mangkurat, 2020)
Software Defect Prediction has an important role in quality software. This study uses 12 D datasets from NASA MDP which then features a selection of metrics categories software. Feature selection is performed to find out metrics software which are influential in predicting defects software. After the feature selection of the metric software category, classification will be performed using the algorithm Artificial Neural Network and validated with 5-Fold Cross Validation. Then conducted an evaluation with Area Under Curve (AUC), From datasets D” 12 NASA MDP that were evaluated with AUC, PC4, PC1 and PC3 datasets obtained the best AUC performance values. Each value is 0.915, 0.828, and 0.826 using the algorithm Artificial Neural Network.