Sanjaya Joseph Primary Author mixed material bibliography Bandung Maranatha University Press 2020 Indonesia

Artikel Jurnal

hlm : 50-60 Abstract Abstract — A loan is one of the most important products on the bank, which used for main revenue. All bank tries to find the most effective business strategy to persuade a customer to use the loan, but loan default has a negative effect after the application is approved. Loan default causes loss on the bank, therefore it is mandatory to calculate in order to decrease the risk of the loan default. This study uses random forest and adaptive boosting machine learning methods to get the prediction and decision. The random forest uses a voting method from many decision trees and adaptive boosting can support to increase accuracy, stability and handle an underfit or overfit problem. The experimental results show that Adaptive Boosted Random Forest outperformed normal random forest and Deep learning Neural Network (DNN) in recall rate evaluation metrics with small trade-offs in the accuracy. Informatika Sistem Informasi JUTISI 24432210 Perpustakaan Teknik UPI YAI JUTISI V6N1 April 2020 JUTISI1-005 Perpustakaan FT UPI YAI JUTISI V6N1 April 2020 cover_issue_174_en_US.png.png 59187 2023-02-22 13:34:47 2023-02-22 13:34:47 machine generated