Penanganan Data Tidak Seimbang Menggunakan Hybrid Method Resampling Pada Algoritma Naive Bayes Untuk Software Defect Prediction

  • Moch Lutfi univ yudharta pasuruan
  • Arief Tri Arsanto
  • Muhammad Faishol Amrulloh
  • Ummi Kulsum

Abstract

Software defect prediction is software data that is used to identify a software module and can also be used to predict software defects. Before carrying out further trials, it is necessary to carry out special handling, especially by using algorithm models as predictions of software defects with the aim of obtaining information from the device being developed. Therefore, it is necessary to predict software defects using appropriate classification and prediction methods, so that the resulting accuracy results are better. In this study, the naïve Bayes algorithm was used as a classification with a resampling technique approach to handle unbalanced data, including SMOTEENN and SMOTETomek. The best accuracy results in the research conducted were 92.5% on the Nasa Repository PC4 dataset

Published
2023-08-31
How to Cite
LUTFI, Moch et al. Penanganan Data Tidak Seimbang Menggunakan Hybrid Method Resampling Pada Algoritma Naive Bayes Untuk Software Defect Prediction. INFORMAL: Informatics Journal, [S.l.], v. 8, n. 2, p. 119-127, aug. 2023. ISSN 2503-250X. Available at: <https://jurnal.unej.ac.id/index.php/INFORMAL/article/view/41090>. Date accessed: 19 may 2024. doi: https://doi.org/10.19184/isj.v8i2.41090.