PENERAPAN FEATURE SELECTION PADA BAYESIAN NETWORK UNTUK PREDIKSI CACAT PERANGKAT LUNAK

Authors

  • Sukmawati Anggraeni Putri Sistem Informasi STMIK Nusa Mandiri
  • Dewi Larasati Teknik Komputer AMIK BSI Jakarta

Keywords:

Bayesian Network, Feature Selection, Software Defect Prediction, Naive Bayesian

Abstract

Pada perkembangannya penelitian pada bidang prediksi cacat perangkat lunak, semakin banyak diminati oleh para peneliti. Untuk mengurangi biaya perawatan dan menjaga kualitas perangkat lunak. Salah satunya dengan, pemilihan modul cacat dan tidak cacat pada perangkat lunak menggunakan machine learning. Salah satunya adalah machine learning Bayesian Network, yang memiliki kinerja lebih baik dari Naive Bayesian. Seperti yang telah dilakukan pada penelitian ini, bahwa Bayesian Network dengan mengintegrasikan algoritma pemilihan atribute seperti Chi Square, Information Gain dan Relief. Model tersebut dapat menghasilkan tingkat akurasi hingga 0,9 % pada salah satu dataset Nasa yang digunakan pada penelitian ini. Oleh karenanya kinerja dan tingkat akurasi Bayesian Network pada prediksi cacat perangkat lunak sangat baik.

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Published

2017-09-15

How to Cite

Putri, S. A., & Larasati, D. (2017). PENERAPAN FEATURE SELECTION PADA BAYESIAN NETWORK UNTUK PREDIKSI CACAT PERANGKAT LUNAK. Jurnal Pilar Nusa Mandiri, 13(2), 275–280. Retrieved from https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/250

Issue

Vol. 13 No. 2 (2017): Pilar Nusa Mandiri : Journal of Computing and Information System Periode September 2017

Section

Articles

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