Penerapan PSO Over Sampling Dan Adaboost Random Forest Untuk Memprediksi Cacat Software

Penerapan PSO Over Sampling Dan Adaboost Random Forest Untuk Memprediksi Cacat Software

• By CFJ, RICHKY FAIZAL AMIR;
• Jurnal Nasional Terakreditasi
• 321
• 230-239

The dataset of software metrics, in general, are not balanced (Imbalanced). Class imbalance in Dataset can reduce the performance of software defect prediction models, because it tends to produce majority class predictions from minority classes, the dataset used in this study uses the National Aeronautics and Space Administration (NASA) Metrics Data Program (MDP), dataset From Stages Pre-processing proposed the Particle Swarm Optimization (PSO). method to overcome the problem of attributes in the training data and the Random Over Sampling (ROS) Resampling method. to deal with class imbalances. This study proposes that the Random Forest method combined with Adaboost can estimate the level of disability of software through training data. The results of this study indicate that the Resampling + Adaboost + Random Forest algorithm can be used to predict software defects with an average accuracy of 94.70% and a value of AUC 0.939. While the PSO + Random Forest algorithm only has an average accuracy of 89.60% and AUC 0.636 the difference in the accuracy of the two models is 5.10% and AUC 0.303. Statistical tests show that there is a significant influence between the proposed model and the Random Forest model with a p-value (0.036) smaller than the alpha value (0.05), which means there is a significant difference between the two models.

• Katakunci mbalanced Class, Resample, Particle Swarm Optimization, Random Forest, Adaboost, Software Defect
• URL https://ejournal.bsi.ac.id/ejurnal/index.php/ijse/article/view/9258
• Penerbit IJSE – Indonesian Journal on Software Engineering, Tegal Jawa Tengah

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