Proceedings of the 1st International Conference of Technology, Innovation, Design & Enterprise (ICTIDE 2025)

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Classification Analysis of QRIS Usage Generation Z With Naïve Bayes Algorithm

Authors
Inlahha Putra Gohae1, Siti Aisyah Arif Ilham Fadillah1, *, Amanda Salsabila Nasution1, Annisa Indriani1
1Faculty of Science & Technology, Universitas Prima Indonesia (UNPRI), Medan, Indonesia
*Corresponding author. Email: sitiaisyah@unprimdn.ac.id
Corresponding Author
Siti Aisyah Arif Ilham Fadillah
Available Online 5 March 2026.
DOI
10.2991/978-94-6463-998-8_13How to use a DOI?
Keywords
QRIS; Digital Payment; Gen Z; Naïve Bayes; Classification
Abstract

This paper examines factors related to the use of the Indonesian Standard Quick Response Code (QRIS) among Generation Z and builds a probabilistic classification model to distinguish between active and passive users. A total of 152 valid responses were collected from students through an online questionnaire using a Likert scale. After data cleaning and feature coding, the Naïve Bayes algorithm was trained using stratified training and testing data (75/25). The model achieved an accuracy of 47.22%, precision (weighted) of 51.91%, recall (weighted) of 47.22%, and F1-score (weighted) of 48.70%. Although its performance was moderate, the analysis revealed key determinants such as perceived ease, transaction speed, and perceived security. This study also discusses the causes of misclassification (class imbalance, mixed feature scales, and multiple choice variables) and provides suggestions for improvement (feature engineering, data balancing, and model comparison) for further research.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 1st International Conference of Technology, Innovation, Design & Enterprise (ICTIDE 2025)
Series
Advances in Engineering Research
Publication Date
5 March 2026
ISBN
978-94-6463-998-8
ISSN
2352-5401
DOI
10.2991/978-94-6463-998-8_13How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Inlahha Putra Gohae
AU  - Siti Aisyah Arif Ilham Fadillah
AU  - Amanda Salsabila Nasution
AU  - Annisa Indriani
PY  - 2026
DA  - 2026/03/05
TI  - Classification Analysis of QRIS Usage Generation Z With Naïve Bayes Algorithm
BT  - Proceedings of the 1st International Conference of Technology, Innovation, Design & Enterprise (ICTIDE 2025)
PB  - Atlantis Press
SP  - 97
EP  - 102
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-998-8_13
DO  - 10.2991/978-94-6463-998-8_13
ID  - Gohae2026
ER  -