Development Of A Mobile-Based Digital Student Id Card Application Using The Flutter Framework

Waterfall Method

Authors

  • Wahit Fitriyanto Boyolali University
  • Muhammad Abdul Aziz Boyolali University
  • Yusuf Eko Rohmadi Boyolali University
  • Taman Ginting Boyolali University

DOI:

https://doi.org/10.59934/jaiea.v6i1.2701

Keywords:

Digital KTM, Flutter Framework, Biometric Authentication, Smart Campus, Waterfall Method

Abstract

The management of physical Student ID Cards at Boyolali University has faced printing inefficiencies and the risk of forgery due to the rapidly growing number of students over the past five years. This research aims to develop a mobile-based Digital KTM application using the Flutter framework and Appwrite backend to provide a secure and efficient identity solution. The software development method applied is the Waterfall model, which includes the stages of requirements analysis, UML diagram, ERD, code construction, and deployment. System security is enhanced through the integration of local fingerprint biometric authentication based on the BiometricPrompt API connected to a Trusted Execution Environment, as well as the implementation of a dynamic QR code based on SHA-256 cryptography with a five-minute expiration limit to prevent visual duplication. Functional testing results using Black Box Testing on 13 test case scenarios showed a high success rate. Usability evaluation via a Likert scale questionnaire administered to 30 respondents yielded an average usability score of 83.70%, indicating that this application is highly suitable for full implementation. This research successfully eliminated the weaknesses of physical cards, reduced issuance time from weeks to minutes, and supports the realization of the Smart Campus vision at Boyolali University.

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Published

2026-10-15

How to Cite

Wahit Fitriyanto, Muhammad Abdul Aziz, Yusuf Eko Rohmadi, & Taman Ginting. (2026). Development Of A Mobile-Based Digital Student Id Card Application Using The Flutter Framework: Waterfall Method. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 6(1), 334–340. https://doi.org/10.59934/jaiea.v6i1.2701