Prediction of People's Business Credit Loan Acceptance Using Machine Learning Model: Application of Random Forest Algorithm

Algies Rifkha Fadillah; Mohamad Nurkamal Fauzan

doi:10.25299/itjrd.2025.18209

Authors

Algies Rifkha Fadillah Departement of Electrical and Computer Engineering, Universitas Logistics Business International
Mohamad Nurkamal Fauzan Departement of Electrical and Computer Engineering, Universitas Logistics Business International

DOI:

https://doi.org/10.25299/itjrd.2025.18209

Keywords:

Machine Learning, Random Forest, Credit Loan Acceptance, Credit Risk

Abstract

This research aims to develop a Machine Learning model using the Random Forest algorithm to predict the receipt of People's Business Credit loans. People's Business Credit loans have a crucial role in supporting economic growth in Indonesia, especially for Small and Medium Enterprises (SMEs). Credit risk is a major issue in providing loans, so predicting loan acceptance is crucial. By combining Machine Learning technology and SME empowerment businesses, this research aims to improve credit risk management and optimize the People's Business Credit loan portfolio. The developed Random Forest model achieved an accuracy rate of 95.0%, with the confusion matrix analysis and classification report providing an in-depth overview of the model's performance.

The results of this study are expected to contribute to making better decisions regarding loan application evaluation, managing credit risk, and optimizing financial resource allocation. As such, the implementation of this model in an operational environment is expected to support economic growth and reduce credit risk effectively.

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