Peningkatan performance Logistic Regression menggunakan teknik Ensemble Bagging pada kasus Credit Scoring

Firman Aziz

doi:10.47650/jsce.v1i1.75

Firman Aziz Universitas Pancasakti

DOI: https://doi.org/10.47650/jsce.v1i1.75

Keywords: Credit Scoring; Logistic Regression; Ensemble; Bagging; Newton Raphson

Abstract

Pengembangan model credit scoring yang efektif menjadi sangat penting karena volume data pelanggan industri kredit saat ini sangat besar. Penyelesaian masalah credit scoring berhasil mendapatkan kinerja prediktif terbaik menggunakan model metode statistik tetapi kinerjanya masih dapat ditingkatkan dengan memperkirakan parameter menggunakan persamaan nonlinear. Penelitian ini mengusulkan peningkatan metode Logistic Regression dengan melakukan teknik Ensemble dengan memperkirakan parameter Newton Raphson. Teknik Ensemble yang digunakan adalah bagging. Dua data yang akan digunakan dalam penelitian ini adalah German dan Australian Dataset. Hasil penelitian menunjukkan bahwa metode yang diusulkan berhasil mencapai kinerja terbaik dengan meningkatkan kinerja klasifikasi tunggal dengan akurasi sebesar 79.6 % untuk German Dataset dan 86.9 % untuk Australian Dataset.

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