Comparison of Support Vector Machine and Random Forest Algorithms for Analyzing Online Loans on Twitter social media

Hamdani; Randi  N.A; M. Khairul Anam

doi:10.33372/jaia.v4i1.1087

Hamdani STMIK Amik Riau
Randi N.A
M. Khairul Anam STMIK Amik Riau

DOI: https://doi.org/10.33372/jaia.v4i1.1087

Keywords: Online loans, Sentiment Analysis, Twitter, SVM, Random Forest

Abstract

Online loans represent a form of financial service wherein borrowers can apply for loans through digital platforms without the need to visit physical offices. The application, approval, and disbursement processes are conducted online, leveraging technology to facilitate financial access and transactions. However, some online lending services impose high-interest rates, resulting in a significant financial burden for borrowers. Moreover, there are instances of inappropriate debt collection practices, such as contacting the borrower's friends or family, leading to discussions and comments on social media platforms like Twitter. This research aims to analyze the patterns of comments in Indonesian society regarding online lending. The study utilizes sentiment analysis and compares machine learning algorithms to assess their accuracy. The algorithms employed in this study are Support Vector Machine (SVM) and Random Forest. The results indicate that the SVM algorithm achieves an accuracy of 93.85%, while Random Forest achieves an accuracy of 91.62%.

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