Implementation of SMOTE to Improve the Performance of Random Forest Classification in Credit Risk Assessment in Banking

Implementation of SMOTE to Improve the Performance of Random Forest Classification in Credit Risk Assessment in Banking

Background: Credit is essential in banking operations, facilitating investment, corporate expansion, and financial satisfaction. Credit risk may emerge if the borrower defaults on payment commitments. Objective: This study aims to evaluate an individual's creditworthiness by classifying and as...

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Bibliographic Details
Main Authors: Nafa Nur Adifia Nanda, Yuniar Farida, Wika Dianita Utami
Format: Article
Language:English
Published: Universitas Nusantara PGRI Kediri 2025-07-01
Series:Intensif: Jurnal Ilmiah Penelitian Teknologi dan Penerapan Sistem Informasi
Subjects:
Classification
Credit Assessment
Credit Risk
Imbalance Data
Random Forest
SMOTE
Online Access:https://ojs.unpkediri.ac.id/index.php/intensif/article/view/23930
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https://ojs.unpkediri.ac.id/index.php/intensif/article/view/23930

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