Sentiment Analysis by Using Naïve Bayes Classification and Support Vector Machine, Study Case Sea Bank

Authors

  • Yefta Christian Universitas Internasional Batam
  • Tony Wibowo Universitas Internasional Batam
  • Mercy Lyawati Universitas Internasional Batam

DOI:

10.33395/sinkron.v9i1.13141

Keywords:

Flask Framework, Google play store, Machine Learning, SDLC, Sentiment analysis

Abstract

Information technology is developing at a rapid pace, changing people's lives, particularly in the financial sector where customer demands are rising, and banks must innovate to convert from traditional to technological banking systems while also increasing competency and efficiency through improved services. Innovations in digital banking have arisen in Indonesia as a result of technical progress. SEA Bank is one such digital bank; it was established in Indonesia in 2021. An app that may be found on the Google Play Store is used for all transactions. However, there are instances when the application's performance falls short of users' expectations, which prompts some users to voice their dissatisfaction. In order to determine if the evaluations are either beneficial or detrimental, the author therefore carried out a sentiment analysis study on SEA Bank using the Naïve Bayes classification and Support Vector Machine techniques. This was then implemented on a website utilizing the Flask framework. In the experiments with 90% training data, 10% testing data, and k = 10, the results of this study demonstrated that the sentiment classification process using the SVM algorithm was the best classification algorithm for evaluating its accuracy, precision, and recall values of 93.99%, 94.60%, 98.87%, and an F1 score of 96.69%.

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References

Aditra Pradnyana, G., Gede, I., & Darmawiguna, M. (2021). Web-Based System for Bali Tourism Sentiment Analysis during The Covid-19 Pandemic using Django Web Framework and Naive Bayes Method.

Alawi, S. J. S. Al, Jamil Jastini Mohd, & Shaharanee, I. N. M. (2022). Predicting Student Performance Using Data Mining Approach: A Case Study in Oman. Publication Issue, 71(4), 1389–1398. Retrieved from http://philstat.org.ph

Cahyaningrum, A. D., & Atahau, A. D. R. (2021). Intellectual Capital And Financial Performance: Banks’ Risk As The Mediating Variable. Jurnal Manajemen Dan Kewirausahaan, 22(1), 21–32. https://doi.org/10.9744/jmk.22.1.21-32

Cupian, C., & Akbar, F. F. (2020). Analisis Perbedaan Tingkat Profitabilitas Perbankan Syariah Sebelum Dan Setelah Bekerja Sama Dengan Perusahaan Financial Technology (Fintech) (Studi Kasus Bank Bni Syariah, Bank Syariah Mandiri, Dan Bank Mega Syariah). Jurnal Ekonomi Syariah Teori Dan Terapan, 7(11), 2149. https://doi.org/10.20473/vol7iss202011pp2149-2169

Fransiska, S., & Irham Gufroni, A. (2020). Sentiment Analysis Provider by.U on Google Play Store Reviews with TF-IDF and Support Vector Machine (SVM) Method. Scientific Journal of Informatics, 7(2), 2407–7658. Retrieved from http://journal.unnes.ac.id/nju/index.php/sji

Ilmawan, L. B., & Mude, M. A. (2020). Perbandingan Metode Klasifikasi Support Vector Machine dan Naïve Bayes untuk Analisis Sentimen pada Ulasan Tekstual di Google Play Store. ILKOM Jurnal Ilmiah, 12(2), 154–161. https://doi.org/10.33096/ilkom.v12i2.597.154-161

Lubis, A. R., Nasution, M. K. M., Sitompul, O. S., & Zamzami, E. M. (2021). The effect of the TF-IDF algorithm in times series in forecasting word on social media. Indonesian Journal of Electrical Engineering and Computer Science, 22(2), 976–984. https://doi.org/10.11591/ijeecs.v22.i2.pp976-984

Maulana, A. A., Susanto, A., & Kusumaningrum, D. P. (2019). Rancang Bangun Web Scraping Pada Marketplace di Indonesia. JOINS (Journal of Information System), 4(1), 41–53. https://doi.org/10.33633/joins.v4i1.2544

Nur’aini, A., & Alfirman. (2021). Analisa Sentimen Pengguna terhadap kebijakan baru whatsapp menggunakan Naive Bayes Classifier dan Support Vector Machine. 1–14. Retrieved from https://www.ptonline.com/articles/how-to-get-better-mfi-results

Rudra Kumar, M., Pathak, R., & Gunjan, V. K. (2022). Diagnosis and Medicine Prediction for COVID-19 Using Machine Learning Approach. Lecture Notes in Electrical Engineering, 834, 123–133. Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-8484-5_10

Salehudin Basryah, E., Erfina, A., & Warman, C. (2021). Analisis Sentimen Aplikasi Dompet Digital Di Era 4.0 Pada Masa Pendemi Covid-19 Di Play Store Menggunakan Algoritma Naive Bayes Classifier. 189–196.

Shamantha Rai B, & Shetty Sweekriti M. (2019). Sentiment Analysis Using Machine Learning Classifiers: Evaluation of Performance.

Wahyu Handani, S., Intan Surya Saputra, D., Hasirun, Mega Arino, R., & Fiza Asyrofi Ramadhan, G. (2019). Sentiment analysis for go-jek on google play store. Journal of Physics: Conference Series, 1196(1). Institute of Physics Publishing. https://doi.org/10.1088/1742-6596/1196/1/012032

Ridhoi, M. A. (2021). Selamat Datang Era Bank Digital di Indonesia, Prospek & Tantangannya.

https://katadata.co.id/muhammadridhoi/analisisdata/5fe2d448aca0a/selamat-datang-era-bank-digital-di indonesia-prospek-tantangannya

Ramli, R. R. (2021). Bank Digital Terus Tumbuh, Ekonomi Digital Indonesia Diproyeksi Jadi Terbesar Se-Asia Tenggara pada 2025. https://money.kompas.com/read/2021/07/01/175703526/bank-digital-terus-tumbuh-ekonomi-digital-indonesia-diproyeksi-jadi-terbesar?page=all

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How to Cite

Christian, Y., Wibowo, T., & Lyawati, M. (2024). Sentiment Analysis by Using Naïve Bayes Classification and Support Vector Machine, Study Case Sea Bank. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(1), 258-275. https://doi.org/10.33395/sinkron.v9i1.13141