Analisis Sentimen Nasabah Terhadap Pengguna M-Banking Raya Indonesia Menggunakan Metode Naïve Bayes Berbasis NLP

Yusi Meilanda; Firahmi Rizky

doi:10.55606/jitek.v6i2.11282

Authors

Yusi Meilanda Universitas Muhammadiyah Sumatera
Firahmi Rizky Universitas Muhammadiyah Sumatera Utara

DOI:

https://doi.org/10.55606/jitek.v6i2.11282

Keywords:

Sentiment Analysis, Natural Language Processing (NLP), Naïve Bayes, TF-IDF, Mobile Banking, Google Play Store

Abstract

This study aims to analyze customer sentiment toward the use of the Raya Indonesia M-Banking app based on reviews on the Google Play Store using a Naïve Bayes method grounded in Natural Language Processing (NLP). Data was collected via web scraping, yielding 501 reviews, of which 474 were deemed valid after the selection process. The analysis stages included text preprocessing (case folding, cleansing, tokenizing, stopword removal, and stemming), feature extraction using TF-IDF, and classification using Multinomial Naïve Bayes with a training and testing data split of 80:20. The results of the study show that positive sentiment dominates at 62.66%, followed by negative sentiment at 31.43%, and neutral sentiment at 5.91%. Model evaluation yielded an accuracy of 75.79%, precision of 73.17%, recall of 75.79%, and an F1-score of 70.97%, indicating that the model performed quite well. Further analysis revealed that the main user complaints relate to login failures, transaction issues, and slow service responses; Thus, these findings provide a basis for evaluating and improving digital banking service quality.

References

[1] Putra, A. R., Prasetyo, E., & Wibowo, S. (2021). Transformasi digital perbankan melalui layanan mobile banking. Jurnal Sistem Informasi Bisnis, 11(1), 1–10.

[2] Sari, P. N., & Hidayat, R. (2022). Analisis sentimen ulasan pengguna aplikasi menggunakan metode klasifikasi teks. Jurnal Teknik Informatika, 14(3), 201–210.

[3] Wahyuni, S., Nugroho, Y., & Lestari, D. (2020). Implementasi natural language processing untuk analisis teks berbahasa Indonesia. Jurnal Informatika dan Komputer, 5(2), 67–75

[4] Ramadhan, M. F., & Kurniawan, A. (2021). Pengaruh text preprocessing terhadap akurasi analisis sentimen bahasa Indonesia. Jurnal Informatika Mulawarman, 16(2), 89–97.

[5] Fauzi, A., & Prabowo, H. (2023). Evaluasi kinerja algoritma Naïve Bayes dalam analisis sentimen layanan digital. Jurnal Ilmiah Informatika, 11(2), 98–107.

[6] Liu, B. (2020). Sentiment analysis: Mining opinions, sentiments, and emotions (2nd ed.). Cambridge University Press.

[7] Ananda, R., & Nugroho, Y. (2022). Penerapan Multinomial Naïve Bayes pada klasifikasi sentimen berbasis teks. Jurnal Informatika, 9(2), 112–120

[8] Ananda, R., Nugroho, Y., & Lestari, D. (2021). Analisis sentimen ulasan pengguna aplikasi mobile banking menggunakan metode Naïve Bayes. Jurnal Teknologi Informasi dan Ilmu Komputer, 8(3), 455–464.