Analisis Sentimen Ulasan Mobile Banking Bank Kalbar pada Google Play Store Menggunakan IndoBert
DOI:
https://doi.org/10.30865/jurikom.v13i3.9779Keywords:
Sentiment Analysis, Mobile Banking, Bank Kalbar, Google Play Store, IndoBERTAbstract
User reviews on the Google Play Store can serve as an important data source for understanding public perceptions of mobile banking services. At the time this paper was written, the application’s average user review score was 3.2, indicating a poor rating. Therefore, the bank needs to further examine the factors that could improve this rating in order to minimize reputational risk, since most users who intend to install an application tend to check its rating first before deciding whether to install it. In general, a rating considered very good is at least 4.5. This study aims to analyze user review sentiment toward the Bank Kalbar Mobile Banking application using a natural language processing approach with IndoBERT, along with two comparison models: TF-IDF with Logistic Regression and RNN BiLSTM. The dataset consists of 2,465 reviews classified into three sentiment classes: positive, negative, and neutral. The data distribution shows class imbalance, with 1,445 positive reviews, 894 negative reviews, and 126 neutral reviews. The data were split using a stratified method into 70% training data and 30% testing data. The research stages included text cleaning, data splitting, model training, and evaluation using accuracy, macro-F1, precision, recall, and a confusion matrix. The experimental results show that the TF-IDF + Logistic Regression model achieved the best performance, with an accuracy of 0.8581 and a macro-F1 score of 0.6777. The RNN BiLSTM model obtained an accuracy of 0.8311 and a macro-F1 score of 0.6777, while IndoBERT achieved an accuracy of 0.8041 and a macro-F1 score of 0.6774. Although IndoBERT did not achieve the highest accuracy, it demonstrated better capability in identifying the neutral class, as indicated by a recall score of 0.6316. These findings indicate that, for a relatively small and imbalanced dataset, a classical TF-IDF-based model can still deliver competitive performance compared with deep learning and transformer-based models.
References
[1] Bank Kalbar, “Mobile Banking.” Accessed: Apr. 20, 2026. [Online]. Available: https://bankkalbar.co.id/Mobile_Banking.php
[2] Google Play Store, “Mobile Banking Bank Kalbar.” Accessed: Apr. 01, 2026. [Online]. Available: https://play.google.com/store/apps/details?id=com.xlink.xmobilebankingadkalbar
[3] D. Perea-Khalifi, A. I. Irimia-Diéguez, and P. Palos-Sánchez, “Exploring the determinants of the user experience in P2P payment systems in Spain: a text mining approach,” Financ. Innov., vol. 10, no. 1, p. 2, Jan. 2024, doi: 10.1186/s40854-023-00496-0.
[4] F. Mi Alnaser, S. Rahi, M. Alghizzawi, and A. H. Ngah, “Does artificial intelligence (AI) boost digital banking user satisfaction? Integration of expectation confirmation model and antecedents of artificial intelligence enabled digital banking,” Heliyon, vol. 9, no. 8, p. e18930, Aug. 2023, doi: 10.1016/j.heliyon.2023.e18930.
[5] H. Adiningtyas and A. S. Auliani, “Sentiment analysis for mobile banking service quality measurement,” 2024, Elsevier BV. doi: 10.1016/j.procs.2024.02.150.
[6] M. Eksa Permana, H. Ramadhan, I. Budi, A. Budi Santoso, and P. Kresna Putra, “Sentiment analysis and topic detection of mobile banking application review,” 2020 5th Int. Conf. Informatics Comput. ICIC 2020, 2020, doi: 10.1109/ICIC50835.2020.9288616.
[7] K. Andini Safarah, D. I. Inan, R. Juita, and V. L. Arie Sirait, “ANALYSING SERVICE QUALITY USING SENTIMENT ANALYSIS AND TOPIC MODELING: A CASE STUDY OF THE LIVIN MANDIRI APPLICATION,” J. Inf. Syst. Informatics Eng., vol. 8, no. 2, pp. 209–220, 2024, [Online]. Available: https://doi.org/10.35145/joisie.v8i2.4517
[8] M. J. Prasetyo and I. M. A. Agastya, “Sentiment Analysis of Banking Application Reviews on Google Play Store using Support Vector Machine Algorithm,” SISTEMASI, vol. 13, no. 6, p. 2386, Nov. 2024, doi: 10.32520/stmsi.v13i6.4536.
[9] M. D. Bimantara and I. Zufria, “Text Mining Sentiment Analysis on Mobile Banking Application Reviews using TF-IDF Method with Natural Language Processing Approach,” JINAV J. Inf. Vis., vol. 5, no. 1, pp. 115–123, 2024, doi: 10.35877/454ri.jinav2772.
[10] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proceedings of the 2019 Conference of the North, Stroudsburg, PA, USA: Association for Computational Linguistics, 2019, pp. 4171–4186. doi: 10.18653/v1/N19-1423.
[11] F. Koto, A. Rahimi, J. H. Lau, and T. Baldwin, “IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP,” in Proceedings of the 28th International Conference on Computational Linguistics, Stroudsburg, PA, USA: International Committee on Computational Linguistics, 2020, pp. 757–770. doi: 10.18653/v1/2020.coling-main.66.
[12] S. Cahyawijaya et al., “IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Stroudsburg, PA, USA: Association for Computational Linguistics, 2021, pp. 8875–8898. doi: 10.18653/v1/2021.emnlp-main.699.
[13] S. Cahyawijaya et al., “NusaCrowd: Open Source Initiative for Indonesian NLP Resources,” in Findings of the Association for Computational Linguistics: ACL 2023, Stroudsburg, PA, USA: Association for Computational Linguistics, 2023, pp. 13745–13818. doi: 10.18653/v1/2023.findings-acl.868.
[14] Y. A. Singgalen, “Performance Analysis of IndoBERT for Sentiment Classification in Indonesian Hotel Review Data,” J. Inf. Syst. Res., vol. 6, no. 2, pp. 976–986, Jan. 2025, doi: 10.47065/josh.v6i2.6505.
[15] S. Apriliani, A. Erfina, and C. Warman, “Fine-Tuned IndoBERT for Aspect-Based Sentiment Analysis of Indonesian Five-Star Hotel Reviews,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 14, no. 4, pp. 437–445, Oct. 2025, doi: 10.32736/sisfokom.v14i4.2491.
[16] K. C. Pradhisa and R. Fajriyah, “Analisis Sentimen Ulasan Pengguna E-commerce di Google Play Store Menggunakan Metode IndoBERT,” Build. Informatics, Technol. Sci., vol. 6, no. 1, Jun. 2024, doi: 10.47065/bits.v6i1.5247.
[17] A. A. P. Simarmata and T. B. Sasongko, “Sentiment Analysis on BRImo Application Reviews Using IndoBERT,” J. Appl. Informatics Comput., vol. 9, no. 3, pp. 851–862, 2025, doi: 10.30871/jaic.v9i3.8162.
[18] Natan Kharisma A, Dewi Lestari, and Gatot T Pranoto, “Sentiment Analysis Review Threads Google Play Store with RoBERTa Modelx`,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 14, no. 4, pp. 272–280, 2025, doi: 10.22146/jnteti.v14i4.22038.
[19] M. Ullah, J. Li, and B. Wadood, “Analysis of Urban Expansion and its Impacts on Land Surface Temperature and Vegetation Using RS and GIS, A Case Study in Xi’an City, China,” 2020, Springer Science and Business Media LLC. doi: 10.1007/s41748-020-00166-6.
[20] W. Ullah, Z. Zhang, and K. Stefanidis, “Sentiment Analysis of Mobile Apps Using BERT,” H. Fujita, Y. Wang, Y. Xiao, and A. Moonis, Eds., Cham: Springer Nature Switzerland, 2023, pp. 66–78. doi: 10.1007/978-3-031-36822-6_6.
[21] F. Koto, J. H. Lau, and T. Baldwin, “IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Stroudsburg, PA, USA: Association for Computational Linguistics, 2021, pp. 10660–10668. doi: 10.18653/v1/2021.emnlp-main.833.
[22] R. Savitri, F. Rizki, and A. Sobri, “Implementation of BERT in Sentiment Analysis of National Digital Samsat (SIGNAL) User Reviews Based on Machine Learning,” MATICS J. Ilmu Komput. dan Teknol. Inf. (Journal Comput. Sci. Inf. Technol., vol. 17, no. 2, pp. 67–75, 2025, doi: 10.18860/mat.v17i2.32059.
