Prediksi Cacat Software Dengan Teknik Sampel Dan Seleksi Fitur Pada Bayesian Network

Sukmawati Anggraeni Putri

Sari


Proses prediksi cacat software telah menjadi bagian penting pada proses pengujian kualitas software. Penelitian ini berfungsi sebagai alternatif bagi praktisi software untuk menentukan prioritas modul software yang akan diuji. Sehingga dapat mengurangi biaya maupun waktu dalam pengujian kualitas software, Sebagai percobaannya, sejak awal para peneliti pada bidang prediksi cacat perangkat lunak ini menggunakan dataset NASA MDP yang bersifat publik. Tetapi, dataset ini memiliki dua kekurangan seperti noise atribut dan ketidak seimbangan kelas. Permasalahan noise atribute dapat diatasi menggunakan algoritma seleksi fitur, seperti Chi Square dan Information Gain. Sementara, permasalahan ketidak seimbangan kelas dapat diatasi menggunakan teknik sampel, seperti RUS (Random Undersampling) dan SMOTE (Synthetic  Minority  Over-sampling Technique). Sehingga pada penelitian ini dilakukan integrasi antara teknik sampel (RUS dan SMOTE) pada algoritma pemilihan atribut (algoritma Information Gain) yang diterapkan pada machine learning Bayesian Network. Machine learning Bayesian Network menurut Lessman merupakan  pengklasifikasi statistik yang  memiliki performa  yang  baik  pada  proses  klasifikasi. Dari hasil percobaan yang dilakukan di empat dataset NASA MDP diperoleh hasil bahwa model SMOTE + IG dapat meningkatkan akurasi pengklasifikasi Bayesian Network hingga rata-rata 0.912 dari 4 dataset NASA MDP yang digunakan.

Kata Kunci


Cacat Perangkat Lunak, Teknik Sampel, Algoritma Seleksi Fitur, Algoritma Bayesian Network

Teks Lengkap:

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Referensi


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DOI: http://dx.doi.org/10.31599/jki.v19i1.314

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