Prediksi Cacat Perangkat Lunak dengan SMOTE dan Random Forest untuk Menangani Ketidakseimbangan Kelas

Prediksi Cacat Perangkat Lunak dengan SMOTE dan Random Forest untuk Menangani Ketidakseimbangan Kelas

• By Santoso, Miftah Faroq;
• Disertasi
• 396
• 1-61

Dataset yang ada pada umumnya yang digunakan dalam memprediksi cacat perangkat lunak cenderung memiliki kelas yang tidak sama (class imbalance), hal ini dapat menurunkan kinerja model prediksi. Pada penelitian ini mengusulkan penerapan metode klasifikasi Random Forest dengan pendekatan level data Random Over Sampling (ROS) dan SMOTE agar model pengklasifikasi lebih optimal. Hasil penelitian didapatkan bahwa teknik pendekatan level data mampu menangani ketidakseimbangan pada dataset dengan menghasilkan nilai akurasi yang sangat baik. Dengan nilai akurasi terbaik didapatkan pada model Random Forest yang telah dioptimasi dengan resampling data ROS, dengan nilai akurasi rata-rata sebesar 0.936 untuk RF+ROS, RF+SMOTE sebesar 0.907, RF sebesar 0.869. Untuk nilai rata-rata AUC model klasifikasi Random Forest yang dioptimasi dengan ROS dan SMOTE cenderung mengalami kenaikan yang sangat baik (excellent), dengan nilai AUC rata-rata sebesar 0.980 untuk RF+ROS dan RF+SMOTE sebesar 0.961, sedangkan dengan RF sebesar 0.806.

• Katakunci SMOTE, Random Forest
• URL https://drive.google.com/file/d/1hyo7cOupC5-3mx5yXSuuUnoODJzOwu7-/view?usp=drive_link
• Penerbit STMIK Nusa Mandiri, Jakarta DKI Jakarta

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