Prediksi Cacat Perangkat Lunak Kelas Tidak Seimbang Menggunakan Resample J48 Dan J48 Consolidated

  • Ilham Nurjabar Universitas Nusa Mandiri
  • Lindung Parningotan Manik Universitas Nusa Mandiri

Abstract

Prediksi cacat perangkat lunak merupakan aspek penting dalam jaminan kualitas perangkat lunak, dengan tujuan mengidentifikasi dan mengatasi potensi cacat sebelum mereka muncul dalam lingkungan produksi. Penelitian ini menyajikan pendekatan inovatif untuk mengatasi masalah distribusi kelas yang tidak seimbang dalam prediksi cacat perangkat lunak menggunakan teknik resampling dan algoritma J48 dan J48 Consolidated. Selain itu, penelitian ini memperkenalkan varian baru dari J48, yang disebut sebagai J48 Consolidated, yang menggabungkan beberapa pohon keputusan menjadi satu model ensemble tunggal untuk meningkatkan kinerja prediksi. Model J48 Consolidated dibandingkan dengan algoritma J48 tradisional dalam konteks prediksi cacat perangkat lunak dengan distribusi kelas yang tidak seimbang. Dataset yang yang digunakan pada penelitian ini menggunakan dataset PROMISE repository. Hasil penelitian menunjukan bahwa integrase Algoritma RUS + J48 Consolidated layak digunakan untuk memprediksi cacat software dengan rata-rata akurasi 78% dengan nilai AUC 0.783. Penelitian ini menguji kinerja J48 dengan pendekata ROS dan RUS menggunakan Algoritma J48 dan J48 Consolidated. Hasil penelitian menunjukan model RUS+J48 Consolidated lebih baik dari model RUS+J48 dengan nilai rata-rata akurasi 78% dan 77% serta nilai AUC 0.783 dan 0.766.

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Published
2024-07-09
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