Pengaruh Penanganan Ketidakseimbangan Kelas pada Prediksi Cacat Perangkat Lunak dengan Teknik Oversampling

  • I Gusti Agung Ramananda Wira Dharma Udayana
  • I Wayan Santiyasa Universitas Udayana
DOI: https://doi.org/10.24843/JNATIA.2024.v03.i01.p17

Abstract

Software defect prediction plays a vital role in SDLC testing by identifying modules prone to defects. However, imbalanced class distributions, where defect (minority) samples are outnumbered by non-defect ones, can hinder model performance. This study investigates the impact of oversampling techniques (SMOTE, ADASYN) on Naive Bayes classification for defect prediction. While the base Naive Bayes model achieved good overall accuracy (83%), it struggled with defect class recall (30%). Applying SMOTE and ADASYN improved recall (40% and 38%, respectively) but slightly lowered accuracy (77% and 80%). Future work will explore feature selection and deep learning approaches for potentially better performance.


Keywords: Software Defect Prediction, Classification, Naïve Bayes, Oversampling, SMOTE, ADASYN


 
Published
2024-11-01
How to Cite
DHARMA, I Gusti Agung Ramananda Wira; SANTIYASA, I Wayan. Pengaruh Penanganan Ketidakseimbangan Kelas pada Prediksi Cacat Perangkat Lunak dengan Teknik Oversampling. Jurnal Nasional Teknologi Informasi dan Aplikasnya, [S.l.], v. 3, n. 1, p. 1029-1038, nov. 2024. ISSN 3032-1948. Available at: <https://ojs.unud.ac.id/index.php/jnatia/article/view/116083>. Date accessed: 09 jan. 2025. doi: https://doi.org/10.24843/JNATIA.2024.v03.i01.p17.
Citation Formats
Issue
Vol 3 No 1 (2024): JNATIA Vol. 3, No. 1, November 2024
Section
Articles

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