Sentiment Analysis Of Online Loan Application Reviews Using Support Vector Machine (SVM) Method

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Published Mar 2, 2024
https://doi.org/10.24252/jagti.v4i1.74
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Vol. 4 No. 1 (2024): September - February

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ST. Aminah Dinayati Ghani
Universitas Dipa Makassar
Nur Salman
Universitas Dipa Makassar
Farhan Wahyuta Kusuma
Universitas Dipa Makassar

Abstract

Online loans, abbreviated as "Pinjol," refer to the practice of lending money online through applications or websites without involving traditional financial institutions such as banks or other traditional creditors. Examples of applications in this field include AkuLaku and Kredivo. These apps operate in the e-commerce and credit provision sectors in Southeast Asia, including Indonesia. Despite their operational strengths, these applications have both advantages and disadvantages, leading to positive and negative reviews on platforms like the Play Store. The research aims to identify positive and negative reviews within these online loan applications that can influence users' decisions when choosing a particular app. SVM classification technique is employed to analyze positive and negative sentiments from these reviews. The accuracy results obtained after sentiment analysis for Kredivo are 81%, while for AkuLaku, it is 75%. A higher accuracy value indicates a better ability of the model to predict sentiments correctly. Visualization of impactful words based on word frequency is presented in the form of a Word Cloud. Therefore, based on the sentiment analysis conducted using the SVM model, the author suggests choosing the Kredivo app when selecting an online loan application, as the analysis indicates that Kredivo has better quality compared to AkuLaku.

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
[1]
S. A. D. Ghani, N. S. Nur Salman, and F. W. K. Farhan Wahyuta Kusuma, “Sentiment Analysis Of Online Loan Application Reviews Using Support Vector Machine (SVM) Method”, Jagti, vol. 4, no. 1, pp. 13-21, Mar. 2024.

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