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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Indonesian Journal of Artificial Intelligence and Data Mining Building of Informatics, Technology and Science Journal of Applied Data Sciences DECODE: Jurnal Pendidikan Teknologi Informasi
Hikmawati, Nina Kurnia
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

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Sentiment Analysis of BCA Mobile App Reviews Using K-Nearest Neighbour and Support Vector Machine Algorithm Zandroto, Yosefin Yuniati; Vitianingsih, Anik Vega; Maukar, Anastasia Lidya; Hikmawati, Nina Kurnia; Hamidan, Rusdi
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37773

Abstract

The rapid evolution of digital technology has significantly transformed the financial services landscape, especially in the realm of mobile banking. BCA Mobile stands among the most popular apps for digital banking in Indonesia. Despite its widespread adoption, user reviews reflect diverse viewpoints and sentiments about the app. The objective of this research is to examine the user sentiments regarding the BCA Mobile app, based on reviews sourced from the Google Play Store and App Store. Two classification models, namely Support Vector Machine (SVM) and K-Nearest Neighbour (K-NN), are used in the analysis process. The collected review data undergoes several pre-processing stages and is labeled automatically using a Lexicon-Based technique. For feature weighting, the TF-IDF (Term Frequency-Inverse Document Frequency) approach is used.. Sentiment classification is then carried out using both K-NN and SVM, with performance evaluated through a matrix of confusion based on measurements like F1-score, recall, accuracy, and precision.  The findings show that the SVM algorithm outperforms K-NN in terms of performance, with an accuracy of 94%, while K-NN achieves an accuracy of 82%. This study offers valuable insights for BCA management in understanding user sentiment and enhancing service quality through the application of artificial intelligence
Co-Authors Anastasia Lidya Maukar Anik Vega Vitianingsih Christina Juliane, Christina Hamidan, Rusdi Hulukati, Stephan Adriansyah Leonardi Paris Hasugian Sidik, Rangga Syahrul Anwar, Syahrul Wartika, Wartika Winanti, Marliana Budhiningtias Yudi Ramdhani Zandroto, Yosefin Yuniati