NLP-Based Sentiment Analysis of Islamic Mobile BankingReviews for Digital Service Quality Enhancement
DOI:
https://doi.org/10.30812/bite.v7i2.5943Keywords:
Digital Service, IndoBERT, Islamic Mobile Banking, Na¨ıve Bayes, Natural Language Processing, Sentiment AnalysisAbstract
Background: The development of Islamic mobile banking services in Indonesia requires improvements in the quality
of digital service management that focuses on user experience. User reviews on application platforms are an important
source of data for understanding user perceptions, satisfaction, and problems encountered. The use of Natural Language
Processing enables systematic and automatic sentiment analysis to support the evaluation of Islamic banking service
quality.
Objective: This study aims to analyze the sentiment of user reviews of sharia mobile banking applications and compare
the performance of the Na¨ıve Bayes sentiment analysis model as a classic model and IndoBERT as a transformer-based
model.
Methods: Data was obtained through web scraping of 1,052 user reviews of the Bank NTB Syariah Mobile Banking
app from the Google Play Store. Preprocessing steps included case folding, cleaning, stopword removal, stemming, and
tokenization. Sentiment labeling was based on user rating scores with three categories: positive, neutral, and negative.
Model performance was evaluated using accuracy and F1-score metrics.
Result: The test results show that the Na¨ıve Bayes model achieved an accuracy of 78% with an F1-score of 0.74. Meanwhile,
the IndoBERT model achieved an accuracy of 88% and an F1-score of 0.86.
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References
[1] F. Alqahtani dan D. G. Mayes, “Financial stability of Islamic banking and the global financial crisis:
Evidence from the Gulf Cooperation Council,” en, Economic Systems, vol. 42, no. 2, pp. 346–360, Jun.
2018. doi: 10.1016/j.ecosys.2017.09.001.
[2] C. Rosanti, F. A. Artanto, dan R. E. Saputra, “Analisis Sentiment Pengguna Aplikasi Mobile Banking
Pada Bank Syariah Dengan Support Vector Regression,” Jurnal Pendidikan dan Teknologi Indonesia,
vol. 4, no. 8, pp. 341–347, Jan. 5, 2025. doi: 10.52436/1.jpti.460.
[3] W. F. Sari, R. Rahim, dan F. Adrianto, “Sentiment Analysis Using Big Data User Reviews on Mobile
Banking Performance in Indonesia,” IAR Journal of Business Management, vol. 04, no. 01, pp. 1–7, May 15,
2023. doi: 10.47310/iarjbm.2023.v04i01.028.
[4] B. Liu, Sentiment Analysis: Mining Opinions, Sentiments, and Emotions, en. Cambridge University Press,
Oct. 15, 2020, 451 pp. Google Books: PdX7DwAAQBAJ.
[5] E. Cambria et al., “New Avenues in Opinion Mining and Sentiment Analysis,” IEEE Intelligent Systems,
vol. 28, no. 2, pp. 15–21, Mar. 2013. doi: 10.1109/MIS.2013.30.
[6] W. Medhat, A. Hassan, dan H. Korashy, “Sentiment analysis algorithms and applications: A survey,” en, Ain
Shams Engineering Journal, vol. 5, no. 4, pp. 1093–1113, Dec. 2014. doi: 10.1016/j.asej.2014.04.011
[7] J. Devlin et al. “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.”
version 2, pre-published.
[8] F. Koto et al. “IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for
Indonesian NLP.” version 1, pre-published.
[9] A. A. P. Simarmata dan T. B. Sasongko, “Sentiment Analysis on BRImo Application Reviews Using
IndoBERT,” Journal of Applied Informatics and Computing, vol. 9, no. 3, pp. 851–862, Jun. 3, 2025. doi:
10.30871/jaic.v9i3.8162.
[10] Y. Setiawan dan L. A. Wulandhari, “Comparative Analysis of IndoBERT and LSTM for Multi-Label Text
Classification of Indonesian Motivation Letter,” Jurnal Online Informatika, vol. 10, no. 2, pp. 260–269,
Aug. 17, 2025. doi: 10.15575/join.v10i2.1499.
[11] R. Z. Suchrady dan A. Purwarianti. “Indo LEGO-ABSA: A Multitask Generative Aspect Based Sentiment
Analysis for Indonesian Language.” arXiv: 2311.01757 [cs], pre-published.
[12] T. A. Bawana, F. Mansor, dan K. Noordin, “Gauging Customer Sentiment Regarding Indonesian Islamic
Digital Banks,” AL-IKTISAB: Journal of Islamic Economic Law, vol. 8, no. 1, pp. 101–118, Nov. 2, 2024.
doi: 10.21111/aliktisab.v8i2.12838.
[13] E. Nurmiati, M. Qomarul Huda, dan S. Mitsalina, “Sentiment Analysis and Topics Modeling on Mobile
Banking Reviews of Sharia Bank in Indonesia Using Naive Bayes and Latent Dirichlet Allocation,” in 2024
12th International Conference on Cyber and IT Service Management (CITSM), Batam, Indonesia: IEEE,
Oct. 3, 2024, pp. 1–6. doi: 10.1109/CITSM64103.2024.10775521.
[14] M. M. Dakwah et al., “Sentiment Analysis on Marketplace in Indonesia using Support Vector Machine
and Na¨ıve Bayes Method,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 10, no. 1, p. 39,
Feb. 16, 2024. doi: 10.26555/jiteki.v10i1.28070.
[15] F. Indriani et al., “Comparative Evaluation of IndoBERT, IndoBERTweet, and mBERT for Multilabel
Student Feedback Classification,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 8, no. 6,
pp. 748–757, Dec. 27, 2024. doi: 10.29207/resti.v8i6.6100
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Copyright (c) 2025 Moh. Yushi Assani, Ari Rudiyan, Ummu Radiyah
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