Published: 2026-04-10
A Comparative Performance Analysis of Naïve Bayes, LSTM, and BiLSTM with Data Balancing Techniques for Sentiment Analysis of EasyCash Application Reviews
DOI: 10.35870/ijsecs.v6i1.6862
Fitri Abelia, Fitriyani Fitriyani
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Fitri Abelia:
Institut Sains dan Bisnis Atma Luhur
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Fitriyani Fitriyani:
Institut Sains dan Bisnis Atma Luhur
Abstract
This study compares the performance of Naïve Bayes, Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) models in sentiment analysis of EasyCash application reviews, with data balancing techniques applied throughout the process. The dataset was collected from the Google Play Store and processed through cleaning, tokenization, stemming, and normalization. Sentiment labeling classified reviews into positive, neutral, and negative categories. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied prior to model training. Feature extraction was conducted using TF-IDF, and models were evaluated on accuracy, precision, recall, and F1-score. Naïve Bayes outperformed both LSTM and BiLSTM, producing higher accuracy and more stable results across evaluation metrics. The findings suggest that simpler machine learning models can be more effective than deep learning approaches when working with limited and imbalanced datasets. Careful data preprocessing, appropriate balancing techniques, and deliberate model selection remain central to achieving reliable sentiment classification performance in fintech applications.
Keywords
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 6 No. 1 (2026)
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Section: Articles
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Published: 2026-04-10
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License: CC BY 4.0
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Copyright: © 2026 Authors
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DOI: 10.35870/ijsecs.v6i1.6862
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Fitri Abelia, Institut Sains dan Bisnis Atma Luhur
Institut Sains dan Bisnis Atma Luhur, Pangkal Pinang City, Bangka Belitung Islands Province, Indonesia
Fitriyani Fitriyani, Institut Sains dan Bisnis Atma Luhur
Institut Sains dan Bisnis Atma Luhur, Pangkal Pinang City, Bangka Belitung Islands Province, Indonesia
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