KOMBINASI INTEGRASI METODE SAMPLING DENGAN NAIVE BAYES UNTUK KETIDAKSEIMBANGAN KELAS PADA PREDIKSI CACAT PERANGKAT LUNAK

  • Sukmawati Anggraeni Putri STMIK Nusa Mandiri Jakarta

Abstract

Distribution of the dataset class software engineering experienced imbalance class. This research will analyze the data class is counterbalanced by the sampling technique Naive Bayes algorithm uses four general dataset from NASA repository MDP to find the defect module. Results of this research, sampling techniques consisting of Random Under Sampling, SMOTE (Synthetic Minority Over-sampling Technique) and Resampling shows increase in the percentage of the size of the AUC. Of the three proposed sampling technique, that SMOTE technique gives better results than the other two techniques.

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Author Biography

Sukmawati Anggraeni Putri, STMIK Nusa Mandiri Jakarta

Jl. Damai No. 8, Warung Jati Barat (Margasatwa), Jakarta Selatan

Published
2015-08-08
How to Cite
PUTRI, Sukmawati Anggraeni. KOMBINASI INTEGRASI METODE SAMPLING DENGAN NAIVE BAYES UNTUK KETIDAKSEIMBANGAN KELAS PADA PREDIKSI CACAT PERANGKAT LUNAK. Konferensi Nasional Ilmu Pengetahuan dan Teknologi, [S.l.], v. 1, n. 1, p. 109-116, aug. 2015. Available at: <http://konferensi.nusamandiri.ac.id/prosiding/index.php/knit/article/view/69>. Date accessed: 29 mar. 2024.
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