Tesis Sukmawati Anggraeni Putri

Tesis Sukmawati Anggraeni Putri

• By Putri, Sukmawati, Anggraeni;
• Disertasi
• 420

Perangkat lunak banyak memainkan peran penting. Karena itu, tugas untuk menjamin kualitas, seperti pengujian perangkat lunak merupakan hal yang mendasar dan penting. Tetapi pengujian perangkat lunak merupakan pekerjaan yang sangat mahal, baik dalam penggunaan biaya maupun waktu. Oleh karena itu penting bagi sebuah perusahaan pengembangan perangkat lunak untuk melakukan pengujian kualitas perangkat lunak dengan biaya seminimal mungkin. Naïve Bayes pada prediksi cacat perangkat lunak telah menunjukkan kinerja yang baik yaitu menghasilkan rata-rata probabilitas sebesar 71 persen. Selain itu juga merupakan pengklasifikasi yang sederhana dan waktu yang diperlukan pada proses pembelajaran membutuhkan waktu lebih cepat dibandingkan dengan algoritma machine learning lainnya. Dataset NASA sangat populer digunakan pada pengembangan metode prediksi cacat perangkat lunak, yang bersifat umum dan dapat digunakan bebas oleh para peneliti. Dari penelitian yang dilakukan sebelumnya terdapat dua permasalahan utama pada prediksi cacat perangkat lunak yaitu masalah noise atribut dan ketidakseimbangan kelas. Penerapan teknik SMOTE (Synthetic Minority Over-Sampling Technique) menghasilkan hasil yang baik dan efektif untuk menangani ketidakseimbangan kelas yang mengalami overfitting pada proses teknik oversampling untuk kelas minoritas (positif). Dan information gain digunakan dalam pemilihan atribut untuk menangani noise atribut. Eksperimen yang dilakukan untuk membandingkan hasil kinerja Naive Bayes sebelum dan setelah diterapkan metode SMOTE dan Information Gain, serta dibandingkan dengan hasil pengklasifikasi lain seperti algoritma OneR dan SVM. Setelah dilakukan eksperimen bahwa penerapan metode SMOTE dan Information Gain terbukti dapat menangani masalah ketidakseimbangan kelas dan noise atribut pada prediksi cacat perangkat lunak.

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