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All Journal JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Erick Renata
Maranatha Christian University
Computer Science & IT

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Prediksi Kelalaian Pinjaman Bank Menggunakan Random Forest dan Adaptive Boosting Joseph Sanjaya; Erick Renata; Vincent Elbert Budiman; Francis Anderson; Mewati Ayub
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 1 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i1.2313

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. Keywords— Adaptive Boosting; Bank; Loan Default; Machine learning; Random Forest;
Co-Authors Francis Anderson Joseph Sanjaya Mewati Ayub Vincent Elbert Budiman