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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi Buletin Ilmiah Sarjana Teknik Elektro Indonesian Journal of Electrical Engineering and Computer Science
Much Aziz Muslim
Universiti Tun Hussein Onn Malaysia
Computer Science & IT Electrical & Electronics Engineering

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An improved light gradient boosting machine algorithm based on swarm algorithms for predicting loan default of peer-to-peer lending Much Aziz Muslim; Yosza Dasril; Muhammad Sam'an; Yahya Nur Ifriza
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1002-1011

Abstract

Internet finance and big data technology are booming in the world. The launch of peer to peer (P2P) lending platforms is a sign and a great opportunity for entrepreneurs to easily increase their capital injection. However, this great opportunity has a high risk of impacting the sustainability and security development of the platform. One way to minimize loan risk is to predict the possibility of loan default. Hence, this study aims to find the best predictive model for predicting loan default of P2P Lending Club dataset. An improved light gradient boosting machine (LightGBM) via features selection by using swarm algorithms i.e. Ant colony optimization (ACO) and bee colony optimization (BCO) to the prediction analysis process. The best feature selection process is selected 6 out of 18 features. The synthetic minority oversampling technique (SMOTE) method is also provided to solve the unbalance class problem in the dataset, then a series of operations such as data cleaning and dimension reduction are performed. The experimental results prove that the LightGBM algorithm has been successfully improved. This success is shown by the prediction accuracy of LightGBM+ACO is 95.64%, LighGBM+BCO is 94.70% and LightGBM is 94.38%. This success also demonstrates outstanding performance in predicting loan default and strong generalizations.
Implementation of Discretisation and Correlation-based Feature Selection to Optimize Support Vector Machine in Diagnosis of Chronic Kidney Disease Dwika Ananda Agustina Pertiwi; Pipit Riski Setyorini; Much Aziz Muslim; Endang Sugiharti
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i2.7548

Abstract

This study aims to improve the accuracy of the classification algorithm for diagnosing chronic kidney disease. There are several models of data mining. In classification, the Support Vector Machine (SVM) algorithm is widely used by researchers worldwide. The data used is a chronic kidney disease dataset taken from the UCI machine learning repository. This data consists of 25 attributes and 11 numeric data attributes, and 14 negative attributes. To call continuously, discrete data is used. Meanwhile, data is selected using Correlation-based Feature Selection (CFS) to reduce irrelevant and redundant data. The research results by applying discretization and feature selection based on correlation for classification in the SVM algorithm with 10-fold cross-validation show an increase in accuracy of 0.5%. The classification of the vector machine support algorithm in the diagnosis of chronic kidney disease produces an accuracy of 99.25%, and after applying discretization and correlation-based feature selection, produces an accuracy of 99.75%. Implementation of discretion and correlation-based feature selection to optimize support vector machine for diagnosis of chronic kidney disease has increased accuracy by 0.5%. The proposed method is feasible as a method of diagnosing chronic kidney disease.
Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization Yosza Dasril; Much Aziz Muslim; M. Faris Al Hakim; Jumanto Jumanto; Budi Prasetiyo
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 1 (2023): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i1.3060

Abstract

The credit risk evaluation is a vital task in the P2P Lending platform. An effective credit risk assessment method in a P2P lending platform can significantly influence investors' decisions. The machine learning algorithm that can be used to evaluate credit risk as LightGBM, however, the results in evaluating P2P lending need to be improved. The aim of this research is to improve the accuracy of the LightGBM algorithm by combining the Particle Swarm Optimization (PSO) algorithm. The novelty developed in this research is combining LightGBM with PSO for large data from the Lending Club Dataset which can be accessed on Kaggle.com. The highest accuracy also presented satisfactory results with 98.094% of accuracy, 90.514% of Recall, and 97.754% of NPV respectively. The combination of LightGBM and PSO shows better results.
Co-Authors Budi Prasetiyo Dwika Ananda Agustina Pertiwi Endang Sugiharti, Endang Jumanto Unjung M. Faris Al Hakim Muhammad Sam'an Pipit Riski Setyorini Yahya Nur Ifriza Yosza Dasril Yosza Dasril