Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization
| Main Authors: | Dasril, Yosza, Muslim, Much Aziz, Hakim, M. Faris Al, Jumanto , Jumanto, Prasetiyo, Budi |
|---|---|
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Information Systems - Universitas Pesantren Tinggi Darul Ulum
, 2023
|
| Subjects: | |
| Online Access: |
http://www.journal.unipdu.ac.id/index.php/register/article/view/3060 http://www.journal.unipdu.ac.id/index.php/register/article/view/3060/1544 |
Daftar Isi:
- 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.