Knowledge Discovery in Sharia Mobile Banking Reviews Using Aspect-Based Sentiment Analysis and Machine Learning
DOI:
https://doi.org/10.30871/jaic.v10i1.11753Keywords:
ABSA, BYOND by BSI, Classification, Knowledge Discovery, Sentiment AnalysisAbstract
User reviews provide important insights into the quality of digital banking applications; however, their large volume makes manual analysis inefficient. This study applies Aspect-Based Sentiment Analysis (ABSA) to examine user perceptions of the BYOND by BSI application based on three aspects: interface, features and performance, and services. Three classification algorithms were compared: Naïve Bayes, Support Vector Machine (SVM), and Random Forest, evaluated with accuracy, precision, recall, F1-score, and ROC-AUC. The results indicate that SVM and Naïve Bayes achieved the best performance, with an accuracy of 0.95 and an F1-score of 0.92, whereas Random Forest exhibited slightly lower performance with an F1-score of 0.89. Furthermore, sentiment analysis reveals the features and performance aspect exhibits the highest proportion of negative sentiment (39.6%), primarily associated with system reliability issues, login problems, transaction failures, and application instability. These findings demonstrate that ABSA can serve as an effective knowledge discovery approach for identifying critical functional issues and supporting data-driven prioritization in improving digital banking services, particularly within the context of sharia banking applications.
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[1] M. Rahman, H. P. Yee, M. A. K. Masud, and M. U. H. Uzir, “Examining the dynamics of mobile banking app. Adoption during the COVID-19 pandemic: A digital shift in the crisis,” Digit. Bus., vol. 4, no. 2, p. 100088, 2024, doi: 10.1016/j.digbus.2024.100088.
[2] I. A. Firmansyah, R. Yasirandi, and R. G. Utomo, “The influence of efficacy, credibility, and normative pressure to M-banking adoption level in Indonesia,” Procedia Comput. Sci., vol. 197, no. 2021, pp. 51–60, 2021, doi: 10.1016/j.procs.2021.12.117.
[3] N. S. Kirani, D. Widyaningtyas, E. Handayani, and I. Randikaparsa, “The Influence of Perceived of Usefulness, Ease, Risk and Trust on Continuous Intention in Using BRI Mobile Banking (Study of BRI Mobile Banking Users in Banyumas Regency),” Asian J. Manag. Anal., vol. 4, no. 1, pp. 215–230, 2025, doi: 10.55927/ajma.v4i1.13459.
[4] Bank Syariah Indonesia, “Tentang Kami.” Accessed: Jul. 05, 2025. [Online]. Available: https://www.bankbsi.co.id/company-information/tentang-kami
[5] Bank Syariah Indonesia, “SuperApp BYOND by BSI Resmi Diluncurkan! Hadirkan Layanan Komprehensif yang Semakin Nyaman & Aman Diakses.” Accessed: Jul. 05, 2025. [Online]. Available: https://www.bankbsi.co.id/index.php/news-update/berita/superapp-byond-by-bsi-resmi-diluncurkan-hadirkan-layanan-komprehensif-yang-semakin-nyaman-aman-diakses
[6] A. Habib, E. Pramana, H. Junaedi, and E. Ronando, “Extending the Expectation Confirmation Model to Examine Continuous Use Mobile Banking: Security, Trust, and Convenience,” INTENSIF J. Ilm. Penelit. dan Penerapan Teknol. Sist. Inf., vol. 9, no. 1, pp. 76–96, 2025, doi: 10.29407/intensif.v9i1.23751.
[7] C. Rosanti, F. A. Artanto, R. E. Saputra, E. Syariah, U. Muhammadiyah, and P. Pekalongan, “Analisis Sentiment Pengguna Aplikasi Mobile Banking Pada Bank Syariah Dengan Support Vector Regression Sentiment Analysis of Mobile Banking Application User in Sharia Bank Using Support Vector Regression,” vol. 4, no. 8, pp. 341–347, 2024.
[8] C. Rosanti, F. A. Artanto, and R. E. Saputra, “Perception of user opinions towards Sharia mobile banking applications in Indonesia based on comments on Google Play Store,” SERAMBI J. Ekon. Manaj. dan Bisnis Islam, vol. 7, no. 1, pp. 97–108, 2025, doi: 10.36407/serambi.v7i1.1486.
[9] M. Kumar, L. Khan, and H. T. Chang, “Evolving techniques in sentiment analysis: a comprehensive review,” PeerJ Comput. Sci., vol. 11, pp. 1–61, 2025, doi: 10.7717/PEERJ-CS.2592.
[10] Y. T. Bau, T. E. Leong, and C. Le Goh, “Sentiment Analysis of E-Wallet Companies: Exploring Customer Ratings and Perceptions,” J. Logist. Informatics Serv. Sci., vol. 10, no. 4, pp. 189–205, 2023, doi: 10.33168/JLISS.2023.0413.
[11] A. Alsokkar, M. Otair, H. E. Alfar, A. Y. Nasereddin, K. Aldiabat, and L. Abualigah, “Sentiment analysis for Arabic call center notes using machine learning techniques,” J. Auton. Intell., vol. 7, no. 3, pp. 1–16, 2024, doi: 10.32629/jai.v7i3.940.
[12] M. Wankhade, A. Chandra, S. Rao, and C. Kulkarni, A survey on sentiment analysis methods, applications, and challenges | Artificial Intelligence Review, no. 0123456789. 2022.
[13] A. R. Putra and D. E. Ratnawati, “Analisis Sentimen Berbasis Aspek pada Aplikasi Mobile Menggunakan Naïve Bayes berdasarkan Ulasan Pengguna Playstore (Studi Kasus : Jconnect Mobile),” J. Teknol. Inf. dan Ilmu Komput., vol. 12, no. 2, pp. 293–300, Apr. 2025, doi: 10.25126/jtiik.2025127556.
[14] Nurul Hidayati, Faqih Hamami, and Riska Yanu Fa’rifah, “Aspect-Based Sentiment Analysis On FLIP Application Reviews (Play Store) Using Support Vector Machine (SVM) Algorithm,” J. Informatics Telecommun. Eng., vol. 7, no. 1, pp. 183–197, 2023, doi: 10.31289/jite.v7i1.9768.
[15] E. F. Aprilia, A. A. Arifiyanti, and N. Sembilu, “Aspect-Based Sentiment Analysis on User Perceptions of OVO using Latent Dirichlet Allocation and Support Vector Machine,” Aviat. Electron. Inf. Technol. Telecommun. Electr. Control., vol. 7, no. 2, p. 163, 2025, doi: 10.28989/avitec.v7i2.3035.
[16] M. J. Prasetyo and I. M. A. Agastya, “Analisis Sentimen Ulasan Aplikasi Perbankan di Google Play Store menggunakan Algoritma Support Vector Machine Sentiment Analysis of Banking Application Reviews on Google Play Store using Support Vector Machine Algorithm,” Sist. J. Sist. Inf., vol. 13, pp. 2386–2400, 2024.
[17] N. D. Kurniawan, P. R. Ferdian, and N. Hidayati, “Analisis Sentimen Algoritma Naïve Bayes, Support Vector Machine, dan Random Forest Pada Ulasan Aplikasi Ajaib,” J. Nas. Teknol. dan Sist. Inf., vol. 11, no. 1, pp. 87–97, 2025, doi: 10.25077/teknosi.v11i1.2025.87-97.
[18] K. L. Tan, C. P. Lee, and K. M. Lim, “A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research,” Appl. Sci., vol. 13, no. 7, 2023, doi: 10.3390/app13074550.
[19] L. A. Supriyono et al., Buku Ajar Big Data dan Data Mining: Konsep, Metodologi, dan Aplikasi. PT. Sonpedia Publishing Indonesia, 2025. [Online]. Available: https://books.google.co.id/books?id=jNF9EQAAQBAJ
[20] M. T. Hidayat and T. Mauritsius, “Perception of the Indonesian Society on the Performance of the Ministry of Investment/BKPM Based on Sentiment Analysis,” Int. J. Eng. Trends Technol., vol. 72, no. 2, pp. 242–253, 2024, doi: 10.14445/22315381/IJETT-V72I2P125.
[21] S. Akter et al., “a Comprehensive Study of Machine Learning Approaches for Customer Sentiment Analysis in Banking Sector,” Am. J. Eng. Technol., vol. 6, no. 10, pp. 100–111, 2024, doi: 10.37547/tajet/volume06issue10-11.
[22] S. S. Aljameel et al., “A sentiment analysis approach to predict an individual’s awareness of the precautionary procedures to prevent covid-19 outbreaks in Saudi Arabia,” Int. J. Environ. Res. Public Health, vol. 18, no. 1, pp. 1–12, 2021, doi: 10.3390/ijerph18010218.
[23] A. Sabir, H. A. Ali, and M. A. Aljabery, “ChatGPT Tweets Sentiment Analysis Using Machine Learning and Data Classification,” Inform., vol. 48, no. 7, pp. 103–112, 2024, doi: 10.31449/inf.v48i7.5535.
[24] I. P. Ariyani, K. D. Tania, A. Wedhasmara, and A. Meiriza, “Ekstraksi Pengetahuan dari Ulasan Aplikasi CapCut Menggunakan Metode Aspect-Based Sentiment Analysis dan Klasifikasi,” vol. 16, no. April, pp. 80–92, 2025.
[25] G. Zhao, Y. Luo, Q. Chen, and X. Qian, “Aspect-based sentiment analysis via multitask learning for online reviews,” Knowledge-Based Syst., vol. 264, p. 110326, 2023, doi: 10.1016/j.knosys.2023.110326.
[26] A. F. Inayah, K. D. Tania, A. Wedhasmara, and A. Meiriza, “Knowledge Extraction Using Aspect-Based Sentiment Analysis and Classification Method,” JEPIN (Jurnal Edukasi dan Penelit. Inform., vol. 10, no. 3, pp. 378–388, 2024.
[27] Regina, T. H. Saragih, and D. Kartini, “Analisis Sentimen Brand Ambassador Bts Terhadap Tokopedia Menggunakan Klasifikasi Bayesian Network Dengan Ekstraksi Fitur Tf-Idf,” J. Inform. Polinema, vol. 9, no. 4, pp. 383–390, 2023, doi: 10.33795/jip.v9i4.1333.
[28] I. Z. Mustaqim, H. M. Puspasari, A. T. Utami, R. Syalevi, and Y. Ruldeviyani, “Assessing public satisfaction of public service application using supervised machine learning,” IAES Int. J. Artif. Intell., vol. 13, no. 2, pp. 1608–1618, 2024, doi: 10.11591/ijai.v13.i2.pp1608-1618.
[29] F. H. Rachman and I. Imamah, “Pendekatan Data Science untuk Mengukur Empati Masyarakat terhadap Pandemi Menggunakan Analisis Sentimen dan Seleksi Fitur,” J. Edukasi dan Penelit. Inform., vol. 8, no. 3, p. 492, 2022, doi: 10.26418/jp.v8i3.56655.
[30] M. Sulistiyono, Y. Pristyanto, S. Adi, and G. Gumelar, “Implementasi Algoritma Synthetic Minority Over-Sampling Technique untuk Menangani Ketidakseimbangan Kelas pada Dataset Klasifikasi,” Sistemasi, vol. 10, no. 2, p. 445, 2021, doi: 10.32520/stmsi.v10i2.1303.
[31] W. Suci and S. Samsudin, “Algoritma K-Nearest Neighbors dan Synthetic Minority Oversampling Technique dalam Prediksi Pemesanan Tiket Pesawat,” J. Media Inform. Budidarma, vol. 6, no. 3, p. 1775, 2022, doi: 10.30865/mib.v6i3.4374.
[32] V. Siahaan and R. H. Sianipar, EMOTION PREDICTION FROM TEXT USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI. BALIGE PUBLISHING, 2023. [Online]. Available: https://books.google.co.id/books?id=Nw1nEAAAQBAJ
[33] J. G. Brandão et al., “Optimization of machine learning models for sentiment analysis in social media,” Inf. Sci. (Ny)., vol. 694, no. November 2024, p. 121704, 2025, doi: 10.1016/j.ins.2024.121704.
[34] Mubaqarah, A. N. Puteri, and A. Sumardin, “Comparison of Random Forest and XGBoost for Diabetes Classification with SHAP and LIME Interpretation,” JTERA (Jurnal Teknol. Rekayasa), vol. 9, no. 2, pp. 121–130, 2024, doi: 10.31544/jtera.v9.i1.2024.121-130.
[35] A. F. AlShammari, “Implementation of Model Evaluation Using Confusion Matrix in Python,” Int. J. Comput. Appl., vol. 186, no. 50, pp. 42–48, 2024, doi: 10.5120/ijca2024924236.
[36] H. Swapnarekha, J. Nayak, H. S. Behera, P. B. Dash, and D. Pelusi, “An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets,” Math. Biosci. Eng., vol. 20, no. 2, pp. 2382–2407, 2023, doi: 10.3934/mbe.2023112.
[37] N. Singhal and Himanshu, “A Review on Knowledge Discovery from Databases,” Lect. Notes Electr. Eng., vol. 860, no. January, pp. 457–464, 2022, doi: 10.1007/978-981-16-9488-2_43.
[38] Z. T. Zafira, K. D. Tania, W. K. Sari, S. Informasi, and U. Sriwijaya, “Sentiment-Based Knowledge Discovery of Wondr by BNI App Reviews Using SVM , KNN , and Naive Bayes for CRM Enhancement,” vol. 9, no. 5, pp. 2498–2508, 2025.
[39] D. K. Harahap, K. D. Tania, and P. E. Sevtiyuni, “Topic Mining-Based Knowledge Discovery of User Health Information Needs,” vol. 7, no. 4, pp. 641–653, 2025.
[40] S. A. Putri, K. D. Tania, and K. Pasemah, “Knowledge Discovery Through Sentiment Analysis and Topic Modeling of BCA Mobile and MyBCA,” vol. 8, no. 2, pp. 669–682, 2025.
[41] C. Tedjokusumo and W. R. Murhadi, “Customer satisfaction as a mediator between service quality and customer loyalty : a case study of Bank Central Asia,” vol. 27, no. 2, pp. 156–169, 2023, doi: 10.20885/jsb.vol27.iss2.art3.
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