Prediksi Kelalaian Pinjaman Bank Menggunakan Random Forest dan Adaptive Boosting

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Joseph Sanjaya
Erick Renata
Vincent Elbert Budiman
Francis Anderson
Mewati Ayub


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;


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How to Cite
J. Sanjaya, E. Renata, V. E. Budiman, F. Anderson, and M. Ayub, “Prediksi Kelalaian Pinjaman Bank Menggunakan Random Forest dan Adaptive Boosting”, JuTISI, vol. 6, no. 1, Apr. 2020.

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