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Much Aziz Muslim
Department of Computer Science, Universitas Negeri Semarang, Indonesia
Computer Science & IT

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Journal : Scientific Journal of Informatics

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Comparative Study of Imbalanced Data Oversampling Techniques for Peer-to-Peer Landing Loan Prediction Rini Muzayanah; Apri Dwi Lestari; Jumanto Jumanto; Budi Prasetiyo; Dwika Ananda Agustina Pertiwi; Much Aziz Muslim
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.50274

Abstract

Purpose: Data imbalances that often occur in the classification of loan data on the Peer-to-Peer Lending platform cancause algorithm performance to be less than optimal, causing the resulting accuracy to decrease. To overcome thisproblem, appropriate resampling techniques are needed so that the classification algorithm can work optimally andprovide results with optimal accuracy. This research aims to find the right resampling technique to overcome theproblem 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 techniquesused. The resampling techniques that will be compared in this research include SMOTE, ADACYN, Border Line, andRandom Oversampling.Results: The highest training accuracy was achieved by the combination of the XGBoost model with the Boerder Lineresampling technique with a training accuracy of 0.99988 and the combination of the XGBoost model with the SMOTEresampling technique. In accuracy testing, the combination with the highest accuracy score was achieved by acombination 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 theXGBoost sorting algorithm to overcome the problem of unbalanced data in uploading data on peer-to-peer lendingplatforms so that the sorting algorithm can work optimally and produce optimal accuracy.
Co-Authors Ahmed Mohamed Annegrat Apri Dwi Lestari Budi Prasetiyo Dwika Ananda Agustina Pertiwi Dwika Ananda Agustina Pertiwi Jumanto Unjung Kamilah Ahmad Rini Muzayanah Shahrul Nizam Salahudin