KLASIFIKASI PERSETUJUAN PINJAMAN MENGGUNAKAN ENSEMBLE SMOTEENN DAN BALANCED BAGGING BERBASIS DECISION TREE PADA LOAN RISK PREDICTION DATASET
DOI:
https://doi.org/10.52436/1.jpti.2323Kata Kunci:
Balanced Bagging, credit scoring, ensemble learning, imbalanced data, SMOTEENNAbstrak
Klasifikasi persetujuan pinjaman pada sistem credit scoring merupakan permasalahan penting dalam industri keuangan, terutama pada kondisi data tidak seimbang (imbalanced dataset) yang menyebabkan model cenderung bias terhadap kelas mayoritas sehingga menurunkan kemampuan mendeteksi calon peminjam berisiko. Penelitian terdahulu umumnya menerapkan teknik resampling atau ensemble learning secara terpisah, sedangkan integrasi keduanya pada loan risk prediction dataset masih jarang dikaji. Penelitian ini mengusulkan kombinasi hybrid resampling SMOTEENN dan Balanced Bagging Classifier berbasis Decision Tree yang menggabungkan penyeimbangan pada tingkat data dan algoritma dalam satu kerangka kerja. Kontribusi utama penelitian dibuktikan melalui tujuh skenario ablation yang menunjukkan bahwa kedua pendekatan saling melengkapi dan menghasilkan performa lebih stabil dibandingkan penerapan parsial maupun model tunggal. Evaluasi menggunakan metrik Accuracy, Precision, Recall, Specificity, F1-score, G-Mean, dan AUC-ROC, dengan F1-score sebagai indikator utama karena lebih representatif untuk data tidak seimbang. Hasil eksperimen menunjukkan bahwa kombinasi SMOTEENN dan Balanced Bagging Classifier mencapai F1-score tertinggi sebesar 0,9054 dengan precision 0,9393, serta kemampuan paling stabil dalam menekan kesalahan klasifikasi pada kedua kelas. Temuan ini menunjukkan bahwa integrasi teknik resampling dan ensemble learning efektif meningkatkan performa klasifikasi pada data persetujuan pinjaman yang tidak seimbang.
Unduhan
Referensi
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