Comparative Sentiment Analysis of Digital Banking Reviews Using Knowledge Discovery IndoBERT
DOI:
https://doi.org/10.30871/jaic.v10i2.12436Keywords:
Sentiment Analysis, Knowledge Discovery, Text Mining, IndoBERT, Blu by BCAAbstract
The ever-evolving digital banking services require a comprehensive understanding of user perceptions. This study applies the Knowledge Discovery in Databases (KDD) approach, a structured process involving data selection, preprocessing, data mining, and interpretation, to analyze sentiment in user reviews of digital banking services. Data was collected by gathering reviews from the Google Play Store platform, resulting in 22,923 valid reviews after filtering and preprocessing. Sentiment labels were automatically assigned based on rating values, where ratings of 4–5 were categorized as positive and ratings of 1–3 as negative. After cleaning, the dataset consisted of 5,806 positive reviews and 4,193 negative reviews. Three classification algorithms—Support Vector Machine, Convolutional Neural Network, and IndoBERT—were compared. Model performance was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results show that IndoBERT achieved the most stable and superior performance, with accuracy, precision, recall, and F1-score values of around 0.87, and the highest AUC-ROC value of 0.89, outperforming CNN and SVM. Additionally, WordCloud analysis was used as part of the knowledge discovery process to identify dominant words in each sentiment category, revealing aspects of the service that users frequently appreciate, as well as issues that often arise in reviews. Overall, this study produced knowledge discovery in the form of sentiment patterns and characteristics of user perceptions of the Blu by BCA application based on public review data.
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