SENTIMEN ANALISIS MOBILE BANKING MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE PADA GOOGLE PLAY STORE

Dewi Masitoh; R. Rhoedy Setiawan; Soni Adiyono

doi:10.36080/idealis.v9i2.3811

Authors

Dewi Masitoh Sistem Informasi, Fakultas Teknik, Universitas Muria Kudus, Kudus, Indonesia
R. Rhoedy Setiawan Sistem Informasi, Fakultas Teknik, Universitas Muria Kudus, Kudus, Indonesia
Soni Adiyono Sistem Informasi, Fakultas Teknik, Universitas Muria Kudus, Kudus, Indonesia

DOI:

https://doi.org/10.36080/idealis.v9i2.3811

Keywords:

Google Play Store, mobile banking, Naive Bayes, Support Vector Machine (SVM), TF-IDF

Abstract

The increasing adoption of mobile banking has generated a large volume of user reviews on the Google Play Store, providing valuable insights into customer satisfaction with digital banking services. This study analyzes user sentiment toward four mobile banking applications BRimo, Livin' by Mandiri, BCA Mobile, and SeaBank—and compares the performance of the Naïve Bayes and Support Vector Machine algorithms for sentiment classification. A dataset of 40,000 Play Store reviews was collected through web scraping. Reviews were labeled based on user ratings, followed by preprocessing, TF-IDF feature extraction, and sentiment classification. Model performance was evaluated using the train-test split method and 5-fold cross-validation. The results indicate that Naïve Bayes outperformed and demonstrated greater stability than SVM across all evaluation metrics, achieving an accuracy of 89.23%, precision of 89.76%, recall of 89.23%, and F1-score of 89.37%, while SVM achieved an accuracy of 88.39%. Sentiment analysis revealed that SeaBank received the highest number of positive reviews, whereas BCA Mobile recorded the highest proportion of negative sentiment. These findings provide a comparative evaluation of Naïve Bayes and SVM for mobile banking sentiment classification and offer practical guidance for selecting appropriate classification algorithms while supporting the continuous improvement of digital banking services through user feedback.

Downloads

Download data is not yet available.

References

[1] A. Huda, M. Arifin, and S. Adiyono, “Perancangan Antarmuka Sistem Informasi Employee Self Service Berbasis Mobile Menggunakan Pendekatan Design Thinking Pada Haen Teknologi Nusantara,” Jurnal SITECH : Jurnal Sistem Informasi dan Teknologi, vol. 7, no. 2, pp. 51–60, 2024, doi: 10.24176/sitech.v7i2.13436.

[2] S. P. Halim, M. N. Azhad, and I. Puspitadewi, “Pengaruh Kemudahan, Kemanfaatan, Dan Risiko Terhadap Minat Nasabah Dalam Menggunakan Mobile Banking (Studi Kasus PadaBank Syariah Indonesia Cabang Jember),” Jurnal Mahasiswa Entrepreneur (JME), vol. 1, no. 10, pp. 2035–2042, 2022, doi: 10.36841/jme.v1i10.2252.

[3] M. M. Suri, P. R. Leony, R. A. Keluanan, M. E. Im, M. Klau, and Y. P. Lian, “Peran Aplikasi Mobile Banking dalam Kemudahan Transaksi Harian,” Jurnal Pendidikan Tambusai, vol. 8, no. 2, pp. 32127–32130, 2024.

[4] A. D. Satya, Cupian, and Y. A. Faisal, “Digital Financing Business Models for MSMEs in Indonesia: Effect on Financial Access, Productivity, and Job Creation toward SDG 8,” Jurnal Ilmiah Akuntansi Kesatuan, vol. 13, no. 6, pp. 1651–1662, 2025, doi: 10.37641/jiakes.v13i6.4336.

[5] S. Adiyono, N. Latifah, and D. L. Fithri, “Analysis of Digital Readiness in the Social Assistance Distribution System with the Unified Theory of Acceptance and Use of Technology (UTAUT),” Journal of Applied Informatics and Computing (JAIC), vol. 9, no. 2, pp. 483–489, 2025, doi: 10.30871/jaic.v9i2.9070.

[6] K. Y. Dewi, I. D. A. I. Saraswati, Y. M. Kiuk, G. R. Dantes, G. Indrawan, and I. M. A. O. Gunawan, “Benchmarking User Experience on Mobile Banking Applications in Indonesia: A Comparative Study Using the User Experience Questionnaire,” Jurnal Teknologi Informasi dan Pendidikan, vol. 19, no. 1, pp. 1336–1352, 2026, doi: 10.24036/jtip.v19i1.1087.

[7] O. S. Jelni, M. L. Radhitya, G. W. Wardhana, N. W. J. K. Dewi, and N. M. M. Desmayani, “Sentiment Analysis of BRImo Reviews on Google Play Store Using SVM and KNN,” Indonesian Journal of Data and Science, vol. 6, no. 3, pp. 548–562, 2025, doi: 10.56705/ijodas.v6i3.365.

[8] J. Yi, J. Kim, and Y. K. Oh, “Uncovering the quality factors driving the success of mobile payment apps,” Journal of Retailing and Consumer Services, vol. 77, p. 103641, 2024, doi: 10.1016/J.JRETCONSER.2023.103641.

[9] V. Anggraini, I. Budi, A. B. Santoso, and P. K. Putra, “Measuring mobile banking service quality using Topic Modeling and Term Ranking: A case study of an Indonesian digital bank,” The Indonesian Journal of Computer Science, vol. 13, no. 6, pp. 9136–9152, 2024, doi: 10.33022/ijcs.v13i6.4517.

[10] Z. A. Zulkifly, N. Brasit, M. S. Alhaqqi, and S. Adelia, “Analisis Peningkatan Kualitas Layanan Mobile Banking dengan Pendekatan Metode E-Servqual,” JBMI (Jurnal Bisnis, Manajemen, dan Informatika), vol. 19, no. 1, pp. 61–79, 2022, doi: 10.26487/jbmi.v19i1.21337.

[11] N. D. Septiyanti, M. I. Luthfi, and N. T. Romadloni, “Komparasi Metode Klasifikasi Dalam Analisis Sentimen Ulasan Pengguna Aplikasi KRL Access Di Google Play Store,” Journal Computer Science and Information Systems : J-Cosys, vol. 4, no. 1, pp. 64–75, 2024, doi: 10.53514/jco.v4i1.495.

[12] A. Aryanusa, R. M. Akbar, and Y. Nur, “Evaluasi Kinerja Logistic Regression Dan Multinomial Naïve Bayes Dalam Klasifikasi Sentimen Mobile Banking,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 13, no. 3S1, pp. 463–474, Oct. 2025, doi: 10.23960/jitet.v13i3S1.7687.

[13] Md. S. Islam, M. N. Kabir, N. A. Ghani, and K. Z. Zamli, “Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach,” Artif. Intell. Rev., vol. 57, no. 3, pp. 62–79, 2024, doi: 10.1007/s10462-023-10651-9.

[14] P. W. Harjita, F. M. Putra, and D. A. Tyas, “Sentimen Analisis Pengguna Media Sosial Berdasarkan Metode Ekstraksi Fitur dan Klasifikasi,” Jurnal Ilmu Komputer, vol. 16, no. 2, pp. 88–96, 2023, doi: 10.24843/JIK.2023.v16.i02.p02.

[15] A. U. Safitra, A. M. B. Aji, B. O. Lubis, and B. Santoso, “Evaluasi Mobile Banking Dengan Pendekatan Use Questionnaire Dan Importance Performance Analysis,” Jurnal Teknologi Informatika dan Komputer, vol. 8, no. 2, pp. 148–163, 2022, doi: 10.37012/jtik.v8i2.833.

[16] A. Parlindungan Zai and S. Saleh, “Perbandingan Naïve Bayes Dan Support Vector Machine Untuk Analisis Sentimen Pengguna Jobstreet,” Idealis: Indonesia Journal Information System, vol. 9, no. 1, pp. 30–38, 2026, doi: 10.36080/idealis.v9i1.3663.

[17] A. Palanivinayagam, C. Z. El-Bayeh, and R. Damaševičius, “Twenty Years of Machine-Learning-Based Text Classification: A Systematic Review,” Algorithms, vol. 16, no. 5, p. 236, 2023, doi: 10.3390/a16050236.

[18] K. Raoufuddin Ahmed, S. A. Zura Jalil, and S. Usman, “Improved Tuna Swarm-based U-EfficientNet: Skin Lesion Image Segmentation by Improved Tuna Swarm Optimization,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 5, pp. 903–913, 2023, doi: 10.14569/IJACSA.2023.0140595.

[19] A. Lukito, F. Nugraha, and N. Latifah, “Analisis Sentimen Ulasan Di Playstore Terhadap Aplikasi Marketplace Menggunakan Naive Bayes dan Logistic Regression,” Jurnal Unitek, vol. 18, no. 2, pp. 322–334, 2025, doi: 10.52072/unitek.v18i2.1625.

[20] B. A. Maulana, M. J. Fahmi, A. M. Imran, and N. Hidayati, “Analisis Sentimen Terhadap Aplikasi Pluang Menggunakan Algoritma Naive Bayes dan Support Vector Machine (SVM),” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 2, pp. 375–384, 2024, doi: 10.57152/malcom.v4i2.1206.

[21] F. A. Irawan, A. R. Atmadja, and A. Wahana, “Analisis Sentimen Ulasan Aplikasi Bank Digital Menggunakan Algoritma Naive Bayes,” EXPLORER: Journal of Computer Science and Information Technology, vol. 4, no. 2, pp. 60–68, 2024, doi: 10.47065/explorer.v4i2.1181.

[22] S. Manda and V. Vardhan, “A Systematic Literature Review on Sentimental Analysis using Machine Learning for Preprocessing, Feature Extraction and Classification,” SSRN Electronic Journal, 2024, doi: 10.2139/ssrn.5061012.

[23] D. Rifaldi, A. Fadlil, and Herman, “Teknik Preprocessing Pada Text Mining Menggunakan Data Tweet ‘Mental Health,’” Decode: Jurnal Pendidikan Teknologi Informasi, vol. 3, no. 2, pp. 161–171, 2023, doi: 10.51454/decode.v3i2.131.

[24] A. Sasi Kirana, R. Roeswidiah, and A. Pudoli, “Analisis Sentimen Pada Media Sosial Terhadap Layanan Samsat Digital Nasional Dengan Support Vector Machine,” Idealis: Indonesia Journal Information System, vol. 8, no. 1, pp. 53–63, 2025, doi: 10.36080/idealis.v8i1.3276.

[25] D. Septiani and I. Isabela, “Analisis Term Frequency Inverse Document Frequency (Tf-Idf) Dalam Temu Kembali Informasi Pada Dokumen Teks,” SINTESIA: Jurnal Sistem dan Teknologi Informasi Indonesia, vol. 1, no. 2, pp. 81–88, 2022.

[26] A. Arasy, S. Agustian, L. Handayani, and I. Iskandar, “Klasifikasi Sentimen Menggunakan Metode Multilayer Perceptron dengan Fitur TF-IDF,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 5, no. 3, pp. 908–919, 2025, doi: 10.57152/malcom.v5i3.2052.

[27] M. I. Burhan, A. N. Ali, A. I. Auliyah, and M. Hading, “Sentiment Analysis of Bapenda South Sulawesi Mobile Application on Google Play Store Using Support Vector Machine,” Journal of Mathematics and Applied Statistics, vol. 2, no. 2, pp. 89–96, 2024, doi: 10.35914/mathstat.v2i2.244.

[28] A. Mudya Yolanda and R. Tri Mulya, “Implementasi Metode Support Vector Machine untuk Analisis Sentimen pada Ulasan Aplikasi Sayurbox di Google Play Store,” VARIANSI: Journal of Statistics and Its Application on Teaching and Research, vol. 6, no. 2, pp. 76–83, 2024, doi: 10.35580/variansiunm258.

[29] A. J. Tobing and A. Febriandirza, “Analisis Sentimen Aplikasi Mobile Bangking Bca Pada Ulasan Pengguna di Google Play Store Menggunakan Metode Naive Bayes,” Journal of Information System Research (JOSH), vol. 5, no. 4, pp. 998–1005, 2024, doi: 10.47065/josh.v5i4.5485.

[30] O. M. Wulandari, I. Maulana, F. Syamsudin, and R. Waluyo, “Perbandingan Algoritma Naive Bayes dan SVM dalam Analisis Sentimen Twitter terhadap Isu Ijazah Jokowi Palsu,” Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK), vol. 4, no. 1, pp. 392–400, 2025, doi: 10.70247/jumistik.v4i1.145.

[31] M. Aulia and A. Hermawan, “Analisis Perbandingan Algoritma SVM, Naïve Bayes, dan Perceptron untuk Analisis Sentimen Ulasan Produk Tokopedia,” Jurnal Media Informatika Budidarma, vol. 7, no. 4, p. 1850, 2023, doi: 10.30865/mib.v7i4.6839.