Sentiment Analysis Of NTB Syariah Bank Application Services using The Naïve Bayes and Support Vector Machine Methods

Abstrak

This research analyzed user sentiment toward the NTB Syariah application using Support Vector Machine (SVM) and Naïve Bayes classification methods. A dataset comprising 814 reviews was obtained via web scraping, with 245 allocated for testing. Preprocessing encompassed cleaning, case folding, tokenization, filtering, and stemming, while sentiment labeling employed a lexicon-based approach integrated with TF-IDF weighting, categorizing reviews as positive, neutral, or negative. Model performance was assessed through accuracy, precision, recall, and F1-score metrics. Results demonstrated SVM's superior performance (accuracy: 92.65%; precision: 0.9327; recall: 0.9265; F1-score: 0.9149) compared to Naïve Bayes (accuracy: 84.49%; precision: 0.8415; recall: 0.8449; F1-score: 0.8005). SVM exhibited greater robustness in managing high-dimensional, complex, and moderately imbalanced datasets, delivering consistent cross-class sentiment classification. Conversely, Naïve Bayes remained computationally efficient and suitable for rapid implementation scenarios. These findings underscore machine learning's efficacy in sentiment analysis for digital banking platforms.

Identifier

• DOI: https://doi.org/10.26905/jtmi.v11i2.16311
• OpenAlex: https://openalex.org/W7125949982