CROSS-PROJECT DEFECT PREDICTION PADA DATASET AEEEM MENGGUNAKAN HYBRID SMOTE–TOMEK DAN ENSEMBLE LEARNING

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  • 11 Mar
  • 2026

CROSS-PROJECT DEFECT PREDICTION PADA DATASET AEEEM MENGGUNAKAN HYBRID SMOTE–TOMEK DAN ENSEMBLE LEARNING

Penelitian ini bertujuan untuk meningkatkan kinerja Cross-project Defect Prediction (CPDP) pada dataset AEEEM melalui penerapan pendekatan hybrid preprocessing yang mengombinasikan normalisasi, reduksi dimensi menggunakan PCA, penyeimbangan kelas dengan SMOTE–Tomek, serta penyesuaian threshold keputusan. Eksperimen dilakukan pada lima proyek AEEEM, yaitu EQ, JDT, LC, ML, dan PDE, dengan dua skenario utama, yaitu single-source CPDP dan multi-source CPDP. Model yang digunakan adalah Random Forest dan Support Vector Machine (SVM), sedangkan kinerja dievaluasi menggunakan metrik F1-score dan AUC. Hasil eksperimen menunjukkan bahwa pendekatan multi-source secara umum menghasilkan kinerja yang lebih stabil dibandingkan single-source. Selain itu, penerapan hybrid preprocessing terbukti mampu meningkatkan F1-score secara signifikan dibandingkan baseline, terutama pada dataset dengan rasio defect yang rendah. Ablation study mengonfirmasi bahwa peningkatan kinerja diperoleh dari kombinasi penyeimbangan kelas dan threshold tuning, bukan dari satu komponen tunggal.

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