Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Inventa: Journal of Science, Technology, and Innovation
Norma Zuhrotul Hayati
Universitas Negeri Semarang
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Deep Learning Transparan untuk Analisis Gagal Bayar Kredit: Kerangka Kerja Jaringan Syaraf Tiruan yang Dapat Dijelaskan dengan Menggabungkan SHAP dan LIME Norma Zuhrotul Hayati; Anggyi Trisnawan Putra
Journal of Science, Technology, and Innovation Vol 1 No 1 (2025): August: Inventa: Journal of Science, Technology, and Innovation
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/w6ne3h43

Abstract

This study introduces a transparent deep learning framework for credit default analysis that integrates Artificial Neural Networks (ANN) with dual interpretability mechanisms SHapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). Using the Default of Credit Card Clients dataset from the UCI Machine Learning Repository, the research develops an optimized model that combines predictive precision with explanatory transparency. The ANN model achieved an accuracy of 81.8% and an AUC of 0.77, outperforming conventional classifiers such as XGBoost and LightGBM while maintaining interpretive clarity. The hybrid SHAP–LIME configuration provides both global and local explanations, identifying repayment status (PAY_0), billing amount (BILL_AMT1), and credit limit (LIMIT_BAL) as the most influential predictors. Empirical findings confirm that interpretability enhances trust, auditability, and regulatory alignment without sacrificing statistical performance. The framework offers a methodological contribution to transparent financial modeling, bridging the gap between algorithmic precision and human interpretive accountability. It advances the paradigm of responsible credit risk management by transforming black-box neural architectures into auditable, evidence-based decision tools for financial institutions
Co-Authors Anggyi Trisnawan Putra