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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING
Anik Vega Vitianingsih
Informatics Department, Universitas Dr. Soetomo, Surabaya
Computer Science & IT Engineering

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SENTIMENT ANALYSIS OF BRIMO APPLICATION USER REVIEWS USING NAÏVE BAYES AND LONG SHORT-TERM MEMORY Muhammad Alif Ilmansyah; Anik Vega Vitianingsih; Anastasia Lidya Maukar; Seftin Fitri Ana Wati; Arizia Aulia Aziiza
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7341

Abstract

In the age of digital transformation, the development of digital banking platforms such as BRImo by Bank Rakyat Indonesia (BRI) continues to evolve to improve customer experience. However, many users still express dissatisfaction through online reviews, especially on platforms such as the Play Store and Twitter (X). This study conducts a systematic and fair comparison between a traditional machine learning approach (Naïve Bayes) and a deep learning approach (Long Short-Term Memory) for sentiment classification under identical dataset conditions. User reviews were collected using web scraping and crawling techniques, followed by text preprocessing, lexicon-based labeling, and appropriate feature representations for each model. The results indicate that both algorithms classify sentiments into three categories: positive, negative, and neutral. The Naïve Bayes model achieved an accuracy of 89%, with macro-average precision, recall, and F1-score of 0.88, 0.58, and 0.59, respectively. Meanwhile, the LSTM model achieved an accuracy of 85%, with macro-average precision, recall, and F1-score of 0.59, 0.63, and 0.60. The findings reveal that Naïve Bayes demonstrates more stable performance on short and highly imbalanced user review data, while LSTM shows limited improvement for minority classes despite its contextual modeling capability. These results highlight the importance of dataset characteristics and evaluation metrics beyond accuracy in sentiment analysis tasks. This research provides practical insights for BRImo development teams and contributes to the understanding of model behavior under real-world sentiment data imbalance.  
Comparative Analysis of Naïve Bayes and K-Nearest Neighbor for Lexicon-Based Emotion Classification of Paxel App User Reviews Azka Salsabilah; Anik Vega Vitianingsih; Dwi Cahyono; Anastasia Lidya Maukar; Hewa Majeed Zangana
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16516

Abstract

The rapid growth of app-based delivery services has increased the importance of understanding user emotions as an indicator of service quality. User reviews on digital platforms provide valuable insights into customer perceptions, satisfaction levels, and service-related issues. This study aims to compare the performance of Naïve Bayes and K-Nearest Neighbor (KNN) algorithms in classifying user emotions related to the Paxel application. The dataset was collected from Google Play Store and X (Twitter) using web scraping techniques and subsequently processed through text pre-processing stages, including case folding, tokenization, and stopword removal. Emotion labels were assigned using the NRC Indonesian Emotion Lexicon, while feature extraction was performed using the TF-IDF method. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied prior to model training. Experimental results show that the Naïve Bayes model achieved the highest overall accuracy of 90.83% with a weighted F1-score of 0.90, while the KNN model obtained an accuracy of 81.21% and a weighted F1-score of 0.77. Both models performed well in identifying happy, sad, and neutral emotions, whereas anger remained the most challenging class to classify. Overall, Naïve Bayes demonstrated more consistent and reliable performance for sentiment analysis tasks..
Co-Authors Anastasia Lidya Maukar Arizia Aulia Aziiza Azka Salsabilah Dwi Cahyono Hewa Majeed Zangana Muhammad Alif Ilmansyah Seftin Fitri Ana Wati