Design of a Mobile UK-Clinic Application Using Svelte Front-End

Authors

  • Marchel Tombeng Universitas Klabat
  • Regi Najoan Universitas Klabat
  • Nancy Lidya Sampouw Universitas Klabat
  • Yesenia Ketty Dalos Universitas Klabat

DOI:

https://doi.org/10.31154/isc12.v12i7.234.1401-1411

Keywords:

Mobile Application, UK-Clinic, Svelte, UI/UX, Medical Records

Abstract

This paper focuses on the design of a mobile UK-Clinic application, developed to address the healthcare needs of the clinic at Universitas Klabat. The application's architecture is proposed using Svelte for the front-end, outlining comprehensive functionalities for both staff/admin (doctors) and students (patients). Key design features for staff/admin include planned schedule management, prescription management, additional document handling, chat functionality, and profile management. For students, the design provides for appointment booking, medical record access, additional document viewing, chat, notifications, and a clinic location map. The UI/UX design emphasizes ease of use, with clear proposed flows from the login process to feature interactions, laying the groundwork for its future implementation

References

Nguyen, P. Q., Sharma, K., & Zhang, Y. (2021). Deep learning-based image encryption: A

survey. IEEE Access, 9, 122345–122367. https://doi.org/10.1109/ACCESS.2021.3105437

Alqahtani, F., & Kavakli-Thorne, M. (2023). Blockchain in education: A systematic literature

review. Computers & Education: Artificial Intelligence, 4,

100111. https://doi.org/10.1016/j.caeai.2023.100111

Kumar, R., & Li, J. (2023). Real-time detection of phishing attacks using deep neural networks.

Cybersecurity and Privacy, 3(1), Article 9. https://doi.org/10.3390/cyber3010009

Bryman, A. (2021). Social research methods (6th ed.). Oxford University Press.

Goodfellow, I., Bengio, Y., & Courville, A. (2020). Deep learning (Adaptive computation and

machine learning series). MIT Press.

Ahmed, S., & Rahman, M. (2020). AI-driven healthcare diagnostics. In K. Kapoor & S. Singh

(Eds.), Emerging trends in artificial intelligence applications (pp. 43–68). Springer.

https://doi.org/10.1007/978-3-030-56417-4_3

Lee, H. J., & Tan, K. (2022). A gamified app to support academic motivation among first-year

students. In Proceedings of the 14th International Conference on Education and New Learning

Technologies (pp. 9283–9289). IATED.

Gonzalez, M. R., & Rivera, D. (2021, July 12–15). Enhancing collaborative writing using AI

feedback tools. Paper presented at the 2021 International Conference on Computer-Supported

Collaborative Learning, Bochum, Germany.

Thomas, R. J. (2022). The role of machine learning in early disease prediction: A case study

on diabetes (Master’s thesis). University of Melbourne. https://doi.org/10.26188/234567

Lee, C. H. (2020). Privacy-preserving data sharing protocol for IoT devices (U.S. Patent No.

10,876,234). United States Patent and Trademark Office.

International Organization for Standardization. (2020). Information technology — Cloud

computing — Overview and vocabulary (ISO/IEC 17788:2020).

https://www.iso.org/standard/73664.html

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Published

2026-02-19

How to Cite

Tombeng, M., Najoan, R., Sampouw, N. L., & Dalos, Y. K. (2026). Design of a Mobile UK-Clinic Application Using Svelte Front-End. 12th International Scholars Conference 2025, 12(7), 1401–1411. https://doi.org/10.31154/isc12.v12i7.234.1401-1411