PEMILIHAN MODEL REGRESI LOGISTIK LASSO MENGGUNAKAN AIC: STUDI KASUS PREDIKSI DEFAULT KREDIT

Lucky Ramadhani, NIM.: 22106010087 (2026) PEMILIHAN MODEL REGRESI LOGISTIK LASSO MENGGUNAKAN AIC: STUDI KASUS PREDIKSI DEFAULT KREDIT. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

Preview	Text (PEMILIHAN MODEL REGRESI LOGISTIK LASSO MENGGUNAKAN AIC: STUDI KASUS PREDIKSI DEFAULT KREDIT) 22106010087_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf - Published Version Download (5MB) | Preview
	Text (PEMILIHAN MODEL REGRESI LOGISTIK LASSO MENGGUNAKAN AIC: STUDI KASUS PREDIKSI DEFAULT KREDIT) 22106010087_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf - Published Version Restricted to Registered users only Download (5MB) | Request a copy

Abstract

Credit default risk poses a major challenge in the banking industry, which can be mitigated through predictive credit scoring models. This study predicts credit card default risk using Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression optimized by the Akaike Information Criterion (AIC). The LASSO method eliminates redundant and highly correlated transaction variables (multicollinearity), while AIC selects the simplest and most accurate model architecture. Utilizing 30,000 customer records from the UCI Machine Learning Repository partitioned into 80% training and 20% testing sets, the algorithm successfully removed two redundant billing variables (BILL AMT4 and BILL AMT5) at the optimal penalty level of λ = 0.0002013. The final model achieved an overall accuracy of 81.16%, a specificity of 97.20% in identifying non-default customers, and an AUC discrimination power of 0.7202. Furthermore, risk factor analysis revealed that the most recent repayment delay (PAY 1) is the most critical trigger, multiplying default risk by 1.766 times per delay level. This study concludes that integrating LASSO with AIC yields a concise, stable, and practical credit scoring system to assist banks in detecting high-risk borrowers early.

Share this knowledge with your friends :

Actions (login required)

View Item