Development of a Web-Based TOEFL E-Certificate System with QR Code Verification and AES–SHA-256 Security Framework

Authors

  • Zainal Arifin Universitas Gunadarma Author

DOI:

https://doi.org/10.64845/jistech.v2i1.316

Keywords:

Web-Based System, TOEFL E-Certificate, QR Code Verification, AES–SHA-256 Security

Abstract

The rapid advancement of digital technologies has accelerated the adoption of electronic certification systems in higher education institutions, including TOEFL certificate management. However, issues related to document authenticity, data security, and verification reliability remain significant challenges. This study aims to develop a secure web-based TOEFL electronic certificate (e-certificate) system that integrates QR code verification with cryptographic techniques, specifically the Advanced Encryption Standard (AES) and Secure Hash Algorithm 256 (SHA-256). The proposed system enables efficient certificate generation, storage, and validation while ensuring data integrity and protection against unauthorized access and forgery. QR codes are utilized to provide instant and user-friendly verification, linking each certificate to a secure database. AES encryption is applied to safeguard sensitive data, while SHA-256 hashing ensures the integrity and authenticity of certificate information. The system is developed using a structured software development approach and evaluated through functional testing and user acceptance testing. The results indicate that the proposed system significantly enhances security, reliability, and efficiency compared to conventional certificate management methods. Furthermore, it provides a scalable and practical solution for academic institutions seeking to implement secure digital certification systems. 

References

Baltrusaitis, T., Ahuja, C., & Morency, L. P. (2019). Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423–443. https://doi.org/10.1109/TPAMI.2018.2798607

Dehimi, N. E. H., & Tolba, Z. (2024). Attention mechanisms in deep learning: Towards explainable artificial intelligence. Proceedings of the International Conference on Pattern Analysis and Intelligent Systems, 1–7. https://doi.org/10.1109/PAIS62026.2024.00006

Ghaffarian, S., Valente, J., Van Der Voort, M., & Tekinerdogan, B. (2021). Effect of attention mechanism in deep learning-based remote sensing image processing: A systematic literature review. Remote Sensing, 13(15), 2965. https://doi.org/10.3390/rs13152965

Ghaleb, E., Niehues, J., & Asteriadis, S. (2023). Joint modelling of audio-visual cues using attention mechanisms for emotion recognition. Multimedia Tools and Applications, 82(8), 11239–11264. https://doi.org/10.1007/s11042-022-13557-w

Hassanin, M., Anwar, S., Radwan, I., Khan, F. S., & Mian, A. (2024). Visual attention methods in deep learning: An in-depth survey. Information Fusion, 108, 102417. https://doi.org/10.1016/j.inffus.2024.102417

I. Gunawan, “Peningkatan Pengamanan Data File Menggunakan Algoritma Kriptografi AES Dari Serangan Brute Force,” TECHSI - Jurnal Teknik Informatika, vol. 13, no. 1, p. 14, Apr. 2021, doi: 10.29103/techsi.v13i1.2395.

Kibria, M. R., Lafond, S., & Arslan, J. (2025). Decoding the multimodal maze: A systematic review on the adoption of explainability in multimodal attention-based models. arXiv preprint. https://doi.org/10.48550/arXiv.2508.04427

Mardiansyah, “Black Box Testing with Equivalence Partitioning and Boundary Value Analysis Methods (Study Case: Academic Information System of Mataram University),” in Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science), Atlantis Press International BV, 2022, pp. 207–219. doi: 10.2991/978-94-6463-084-8_19.

Mocanu, B., Tapu, R., & Zaharia, T. (2023). Multimodal emotion recognition using cross-modal audio-video fusion with attention and deep metric learning. Image and Vision Computing, 133, 104624. https://doi.org/10.1016/j.imavis.2023.104624

Moorthy, S., & Moon, Y. K. (2025). Hybrid multi-attention network for audio–visual emotion recognition through multimodal feature fusion. Mathematics, 13(7), 1100. https://doi.org/10.3390/math13071100

Nagrani, A., Yang, S., Arnab, A., Jansen, A., Schmid, C., & Sun, C. (2021). Attention bottlenecks for multimodal fusion. Advances in Neural Information Processing Systems, 34, 14200–14213.

Niu, Z., Zhong, G., & Yu, H. (2021). A review on the attention mechanism of deep learning. Neurocomputing, 452, 48–62. https://doi.org/10.1016/j.neucom.2021.03.091

Parcalabescu, L., & Frank, A. (2023). MM-SHAP: A performance-agnostic metric for measuring multimodal contributions in vision and language models. Proceedings of the ACL, 4032–4059. https://doi.org/10.18653/v1/2023.acl-long.220

Pressman, R. S., & Maxim, B. R. (2021). Software Engineering: A Practitioner’s Approach (9th ed.). McGraw-Hill.

Sommerville, I. (2022). Software Engineering (11th ed.). Pearson.

R. Khair, “Application of Rapid Application Development (RAD) in the E-Career System: A Startup Approach,” The Indonesian Journal of Computer Science, vol. 13, no. 6, Dec. 2024, doi: 10.33022/ijcs.v13i6.4450

Vamsidhar, D., Desai, P., Shahade, A. K., Patil, S., & Deshmukh, P. V. (2025). Hierarchical cross-modal attention and dual audio pathways for enhanced multimodal sentiment analysis. Scientific Reports, 15(1), 25440. https://doi.org/10.1038/s41598-025-25440-0

Zhao, F., Zhang, C., & Geng, B. (2024). Deep multimodal data fusion. ACM Computing Surveys, 56(9), 1–36. https://doi.org/10.1145/3631234

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Published

2026-06-22

How to Cite

Development of a Web-Based TOEFL E-Certificate System with QR Code Verification and AES–SHA-256 Security Framework. (2026). Journal of Information Systems and Technology, 2(1), 58-73. https://doi.org/10.64845/jistech.v2i1.316

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