Analytical Study of Customer Review Sentiment and Education Classification as the Basis for Customer Service Management in the Blu BCA Application

Dwi Andre  Vebriansyah; F. Danardana Murwani; Heri Pratikto

doi:10.31851/jmksp.v10i2.21016

Authors

Dwi Andre Vebriansyah Universitas Negeri Malang
F. Danardana Murwani
Heri Pratikto

DOI:

https://doi.org/10.31851/jmksp.v10i2.21016

Keywords:

Sentiment analysis, naive Bayes, support vector machine, customer service, Blu BCA

Abstract

This study aims to develop a sentiment and education classification model to analyze customer reviews of the Blu BCA mobile banking application in order to identify positive and negative sentiments as a basis for improving customer service management and service quality. The study employs machine learning techniques using the Naive Bayes and Support Vector Machine (SVM) algorithms. Customer review data were collected through web scraping from the Google Play Store and processed using data cleaning, tokenization, stemming, and TF-IDF weighting. Model training and evaluation were conducted using Python with the scikit-learn library. The results indicate that both Naive Bayes and SVM models are capable of effectively classifying Indonesian-language customer reviews into positive and negative sentiment categories. Performance evaluation using confusion matrix, precision, recall, and F1-score reveals differences in accuracy and effectiveness in handling linguistic characteristics of customer feedback. This study can be applied in digital banking services, customer relationship management, service quality evaluation, and financial technology analytics. The novelty of this study lies in its focused application of machine learning-based sentiment analysis on Indonesian-language reviews of a digital banking application, integrating sentiment and education classification with customer service improvement strategies to transform large-scale customer feedback into actionable insights.

References

Abutabenjeh, S., & Jaradat, R. (2018). Clarification Of Research Design, Research Methods, And Research Methodology: A Guide For Public Administration Researchers And Practitioners. Teaching Public Administration, 237–256.

Adela, C., Karnila, S., & Agarina, M. (2024). Analisis Ulasan Pengguna Aplikasi Seabank Dengan Support Vector Machine Dan Naïve Bayes. Indonesian Journal On Software Engineering (Ijse), 147–155.

Bharadwaj, L. (2023). Sentiment Analysis In Online Product Reviews: Mining Customer Opinions For Sentiment Classification. Int J Multidiscip Res, 5(5).

Dewi, M., Nabila, S., & Hilaly, S. (2023). Analisis Kepuasan Pengguna Terhadap Kualitas Layanan Portal Layanan Mahasiswa (Polam) Menggunakan Metode Servqual. Jurnal Teknologi Dan Sistem Informasi, Iv(2), 198–202.

Farida, T. (2016). Pengaruh Customer Relationship Management Dan Kualitas Layanan Terhadap Kepuasan Dan Loyalitas Investor Pt Danareksa Medan. Jurnal Ilmiah Manajemen Dan Bisnis, Xvii(1), 101–124.

Gunawan, S. (2022). Analisis Kualitas Layanan Pengguna Website E- Learning Dengan Metode Servqual (Studi Kasus Spada Dikti Program Kampus Merdeka. Mdp Student Conference, Ii(2), 526–537.

Hampshire, N. (2025). 1 St Gri Conference 2025 Impact Of Predictive Machine Learning Models On Operational Efficiency And Consumer Satisfaction In. April, 376–403. Https://Doi.Org/10.63125/5tjkae44

Hulugh, T., & Ikeh, C. O. (2025). Artificial Intelligence In Product Management : Automating Roadmap Prioritization Through Sentiment Analysis And Customer Feature Demand Modeling.

Id, M. H., Id, T. A., Id, R. I., & Id, P. U. (2024). Leveraging Textual Information For Social Media News Categorization And Sentiment Analysis. 1–28. Https://Doi.Org/10.1371/Journal.Pone.0307027

Id, S. K., Qasim, I., Khan, W., Khan, A., & Khan, J. A. (2024). Plos One An Automated Approach To Identify Sarcasm In Low-Resource Language. 1–29. Https://Doi.Org/10.1371/Journal.Pone.0307186

Jylhä, H., & Hamari, J. (2020). Development Of Measurement Instrument For Visual Qualities Of Graphical User Interface Elements ( Visqual ): A Test In The Context Of Mobile Game Icons. In User Modeling And User-Adapted Interaction (Issue 0123456789). Springer Netherlands. Https://Doi.Org/10.1007/S11257-020-09263-7

Kanapala, A., Sandeep, M., Teja, J., Manikonda, A., Mekala, B., & Mahipal, S. (2025). Adaptive Slang And Language Modeling For Mining Informal Web Content. 103(24), 10341–10357.

Khan, N. D., Khan, J. A., Li, J., & Ullah, T. (2024). Mining Software Insights : Uncovering The Frequently Occurring Issues In Low-Rating Software Applications. Https://Doi.Org/10.7717/Peerj-Cs.2115

Kıcıman, E., & Fox, A. (N.D.). Detecting Application-Level Failures In Component-Based Internet Services. 1–15.

Koto, F., & Rahmaningtyas, G. (2017). Inset Lexicon: Evaluation Of A Word List For Indonesian Sentiment Analysis In Microblogs. 391–394.

Mardani, A., Jusoh, A., Zavadskas, E. K., Khalifah, Z., & Nor, K. (2015). Application Of Multiple-Criteria Decision-Making Techniques And Approaches To Evaluating Of Service Quality : A Systematic Review Of The Literature. 16(5), 1034–1068. Https://Doi.Org/10.3846/16111699.2015.1095233

Mweshi,, G., & Sakyi, K. (2020). Application Of Sampling Methods For The Research Design. Archives Of Business, 180–193.

Owusu-Berko, L. (2025). Harnessing Big Data, Machine Learning, And Sentiment Analysis To Optimize Customer Engagement, Loyalty, And Market Positioning. Int. J. Comput. Appl. Technol. Res, 14, 1–16.

Ranataru, S., & Trianasari, N. (2024). Analisis Sentimen Media Sosial Terhadap Aplikasi Perbankan Untuk Mengetahui Kepuasan Pengguna Aplikasi: Studi Kasus Pada Livin By Mandiri Dan Bca Mobile. Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah, 6818–6838.

Rizun, N., Revina, A., & Edelmann, N. (2025). Text Analytics For Co-Creation In Public Sector Organizations : A Literature Review-Based Research Framework. 1–45.

Sarimole, F., & K. (2024). Analisis Sentimen Terhadap Aplikasi Satu Sehat Pada Twitter Menggunakan Algoritma Naive Bayes Dan Support Vector Machine. Jurnal Sains Dan Teknologi, 783–790.

Sherif, S. M. (2024). Lexicon Annotation In Sentiment Analysis For Dialectal Arabic : Consensus Expert Standardized Criteria. 2024, 165–172.

Sholikhah, A. F., & H. (2023). Pengaruh Kualitas Layanan, Kualitas Produk Dan Harga Terhadap Loyalitas Pelanggan Melalui Kepuasan Pelanggan Mie Gacoan Di Bekasi Timur. Jurnal Economina, Ii(2), 692–709.

Suyanto. (2018). Machine Learning Tingkat Dasar Dan Lanjut. [Online] Informatika.

Zhao, S., Hong, X., Yang, J., Zhao, Y., & Ding, G. (2023). Toward Label-Efficient Emotion And Sentiment Analysis. Proceedings Of The Ieee, 111(10), 1159–1197. Https://Doi.Org/10.1109/Jproc.2023.3309299