Sentiment Analysis on User Reviews of Mobile Banking BRImo Using Naïve Bayes Classifier and Support Vector Machine Models

Karenina, Adhelia (2023) Sentiment Analysis on User Reviews of Mobile Banking BRImo Using Naïve Bayes Classifier and Support Vector Machine Models. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

BRI's mobile banking application, namely BRImo, is a digital BRI financial application based on internet data that provides convenience for both BRI customers and non-customers. Briefly see the BRImo application scores and reviews on the Google Play Store, there have many complaints made by users regarding the application. This research is conducted by analyzing user reviews of the BRImo mobile banking application using sentiment analysis to understand the opinion and sentiment of BRImo users. Sentiment analysis of data from BRImo application user reviews will make it easier for BRI to obtain perceptual information from BRImo mobile banking. Two of the statistical methods that can classify text are Support Vector Machine (SVM), with Linear Kernel and RBF Kernel, and Naïve Bayes Classifier (NBC) algorithms. This research compared the two algorithms to determine which algorithm produces the greatest degree of performance of accuracy. It was found that the SVM method, specifically SVM Linear Kernel, has the best classification performance compared to the other SVM model, using RBF Kernel, and Naive Bayes model. The conclusion made was based on consideration of accuracy, precision, recall, AUC, and F-1 score metrics. In addition, training time was also taken into consideration.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	sentiment, BRImo, user reviews, naïve bayes classifier, support vector machine (SVM), sentimen, ulasan pengguna
Subjects:	Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines.
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Adhelia Karenina
Date Deposited:	14 Sep 2023 04:58
Last Modified:	14 Sep 2023 04:58
URI:	http://repository.its.ac.id/id/eprint/104565

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