ANALISIS PERSEPSI PENGGUNA TERHADAP APLIKASI QPON MENGGUNAKAN METODE KLASIFIKASI SVM DAN NAÏVE BAYES
Abstract
The quality, user experience, and reliability of the Qpon application greatly influence the importance of consumer perception. The variation of the text and its increasing number is difficult if manual analysis is carried out. Machine learning is an effective model for efficiently classifying perceptions. The goal is to analyze and compare the performance of the SVM and Naïve Bayes algorithms in classifying the perception of Qpon application users. This research method uses a quantitative approach with Machine Learning-based text analysis techniques to classify the perception of Qpon application users. Data was obtained from the Google Play Store using the scraping method with a scope of 1–31 October 2025. The main result of the total valid data is 631 reviews; the majority of users who give positive reviews are 52.8% (5 stars). After labeling, the distribution of positive perception was 56.49%, negative 39.24% and neutral 4.27%. The difference in algorithm comparison can be seen from slightly higher accuracy as well as more consistent predictions on negative and positive classes. So, SVM is more suitable for the classification of users of the Qpon application.
References
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[5] Y. Akman and H. G. Turkmen, “A Study On Customer Perceptions and Attitudes Towards Digital Coupons,” Journal of Business, Innovation and Governance, vol. 4(2), 2021, doi: doi.org/10.54472/jobig.1034612.
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[7] D. M. Immanuel and C. N. Mustikarini, “Price Perception: Effect of Coupon Proneness and Sale Proneness on Consumer Impulse Buying,” Jurnal Aplikasi Manajemen, vol. 6, No. 1, 2018, doi: 10.21776/ub.jam.2018.016.
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Published
2025-12-31
How to Cite
ILLADANA, Ageng Ayu; ROSYID, Harun Al.
ANALISIS PERSEPSI PENGGUNA TERHADAP APLIKASI QPON MENGGUNAKAN METODE KLASIFIKASI SVM DAN NAÏVE BAYES.
Jurnal Teknik Informasi dan Komputer (Tekinkom), [S.l.], v. 8, n. 2, p. 585-591, dec. 2025.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php?journal=Tekinkom&page=article&op=view&path%5B%5D=2543>. Date accessed: 21 apr. 2026.
doi: https://doi.org/10.37600/tekinkom.v8i2.2543.
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