ANALISIS SENTIMEN REVIEW PENGGUNA APLIKASI FIZZO NOVEL DI GOOGLE PLAY MENGGUNAKAN ALGORITMA NAIVE BAYES

Teguh Arlovin; Kusrini; Kusnawi

doi:10.51401/jinteks.v6i1.3909

Authors

Teguh Arlovin Universitas AMIKOM Yogyakarta
Kusrini Universitas AMIKOM Yogyakarta
Kusnawi Universitas AMIKOM Yogyakarta

DOI:

https://doi.org/10.51401/jinteks.v6i1.3909

Keywords:

Sentiment Analysis, Fizzo Novel, Digital Platform, Naive Bayes

Abstract

Technological advancements have significantly shaped the digital landscape, aligning with society's practical needs and deriving benefits from chosen technologies. This evolution is prominently observed in the proliferation of digital reading platforms such as Wattpad, Novelah, GoNovel, NovelFul, Inkitt, and Fizzo Novel. Given this context, sentiment analysis of user reviews on these platforms emerges as a crucial area of study. Sentiment analysis involves processing digital text to identify the underlying opinions as positive, negative, or neutral. This study focuses on applying the Naive Bayes algorithm for sentiment classification of reviews on the Fizzo Novel platform, a leading digital reading app in 2022, chosen for its vast and varied user feedback. The research methodology encompassed data collection, pre-processing, and system development phases, utilizing reviews from January to December 2022. The findings reveal an accuracy rate of 83%, precision of 85%, recall of 82%, and specificity of 85%, indicating the model's robust capability in accurately predicting sentiments across reviews. These metrics underscore the efficacy of the Naive Bayes method in sentiment analysis, offering valuable insights for enhancing user experience on digital reading platforms.

References

W. A. Social, Datareportal, and Meltwater, “APAC: mobile internet user share by country 2022,” Statista. [Online]. Available: https://www.statista.com/statistics/1252976/apac-mobile-internet-users-by-country-and-device/

W. A. S. Singapore, “THE GLOBAL STATE OF DIGITAL IN OCTOBER 2022,” We Are Social. Oct. 2022. [Online]. Available: https://wearesocial.com/sg/blog/2022/10/the-global-state-of-digital-in-october-2022/

DataReportal, “Indonesia: consumer spending on mobile apps 2021,” DataReportal. Feb. 2022. [Online]. Available: https://www.statista.com/statistics/1333760/indonesia-consumer-spending-on-mobile-apps/

P. Kaur, “Sentiment analysis using web scraping for live news data with machine learning algorithms,” Materials Today: Proceedings, vol. 65, pp. 3333–3341, Jan. 2022, doi: 10.1016/J.MATPR.2022.05.409.

T. Matsumoto, W. Sunayama, Y. Hatanaka, and K. Ogohara, “Data Analysis Support by Combining Data Mining and Text Mining,” in 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), Hamamatsu: IEEE, Jul. 2017, pp. 313–318. doi: 10.1109/IIAI-AAI.2017.165.

I. Saputra, T. Djatna, R. R. A. Siregar, D. A. Kristiyanti, H. R. Yani, and A. A. Riyadi, “Text Mining of PeduliLindungi Application Reviews on Google Play Store,” vol. 15, no. 2, 2022.

G. K. Locarso, “Analisis Sentimen Review Aplikasi Pedulilindungi Pada Google Play Store Menggunakan Nbc,” no. 2, 2022.

O. Irnawati and K. Solecha, “Analisis Sentimen Ulasan Aplikasi Flip Menggunakan Naïve Bayes dengan Seleksi Fitur PSO Flip App Review Sentiment Analysis Using Naïve Bayes with PSO Feature Selection,” Jurnal Ilmiah Intech?: Information Technology Journal of UMUS, vol. 4, no. 02, pp. 189–199, 2022.

S. I. Nurhafida and F. Sembiring, “Analisis Sentimen Aplikasi Novel Online Di Google Play Store Menggunakan Algoritma Support Vector Machine (SVM),” vol. 6, 2022.

A. I. Tanggraeni and M. N. N. Sitokdana, “Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Naïve Bayes,” JATISI, vol. 9, no. 2, pp. 785–795, Jun. 2022, doi: 10.35957/jatisi.v9i2.1835.

A. Voutama, “Sistem Antrian Cucian Mobil Berbasis Website Menggunakan Konsep CRM dan Penerapan UML,” Komputika, vol. 11, no. 1, pp. 102–111, Feb. 2022, doi: 10.34010/komputika.v11i1.4677.