Comparative Study of Imbalanced Data Oversampling Techniques for Peer-to-Peer Landing Loan Prediction
(1) Department of Computer Science, Universitas Negeri Semarang, Indonesia
(2) Department of Computer Science, Universitas Negeri Semarang, Indonesia
(3) Department of Computer Science, Universitas Negeri Semarang, Indonesia
(4) Department of Computer Science, Universitas Negeri Semarang, Indonesia
(5) Faculty of Technology Management, Universiti Tun Hussein Onn Malaysia, Malaysia
(6) Department of Computer Science, Universitas Negeri Semarang, Indonesia
Abstract
cause algorithm performance to be less than optimal, causing the resulting accuracy to decrease. To overcome this
problem, appropriate resampling techniques are needed so that the classification algorithm can work optimally and
provide results with optimal accuracy. This research aims to find the right resampling technique to overcome the
problem of data imbalance in data lending on peer-to-peer landing platforms.
Methods: This study uses the XGBoost classification algorithm to evaluate and compare the resampling techniques
used. The resampling techniques that will be compared in this research include SMOTE, ADACYN, Border Line, and
Random Oversampling.
Results: The highest training accuracy was achieved by the combination of the XGBoost model with the Boerder Line
resampling technique with a training accuracy of 0.99988 and the combination of the XGBoost model with the SMOTE
resampling technique. In accuracy testing, the combination with the highest accuracy score was achieved by a
combination of the XGBoost model with the SMOTE resampling technique.
Novelty: It is hoped that from this research we can find the most suitable resampling technique combined with the
XGBoost sorting algorithm to overcome the problem of unbalanced data in uploading data on peer-to-peer lending
platforms so that the sorting algorithm can work optimally and produce optimal accuracy.
Keywords
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