Integrating Information Systems and Mathematical Models for UI/UX Design in Web-Based Digital Archives

Adina  Apriyani; Abdullah  Ardi; Mega Wahyu  Rhamadani

doi:10.33365/teknoinfo.v20i1.852

Adina Apriyani Politeknik Negeri Banjarmasin
Abdullah Ardi Politeknik Hasnur
Mega Wahyu Rhamadani Sekolah Tinggi Teknologi Pekerjaan Umum

DOI: https://doi.org/10.33365/teknoinfo.v20i1.852

Keywords: Digital Archiving, Design Thinking, UI/UX, Information Systems, Public Sector Digital Transformation

Abstract

The rapid growth of digital transformation in public institutions has underscored the urgency of developing effective and user-friendly digital archiving systems. In Indonesia, many government agencies, including the Regional Financial and Asset Management Agency (BKAD) of Kapuas Regency, still face inefficiencies in manual document management, with retrieval times averaging 15–20 minutes per file and high risks of data loss. This study aims to design and evaluate a web-based digital archive system that integrates information systems engineering with mathematical usability assessment, thereby addressing both functional and experiential challenges. The research employed the Design Thinking framework, progressing through empathize, define, ideate, prototype, and testing stages. Prototypes were developed using high-fidelity design tools, while usability evaluations combined subjective and objective measures through the System Usability Scale (SUS), Mission Usability Score (MIUS), and Maze Usability Score (MAUS). The findings demonstrate that the proposed system reduced retrieval times by 90 percent (from 20 minutes to 2 minutes) and achieved an SUS score of 82.5 (Excellent), a MIUS of 76.2 (Good), and a MAUS of 78.6 (Good), all surpassing benchmarks reported in previous studies. These results confirm that combining user-centered design with quantitative evaluation yields reliable outcomes. The study concludes that the hybrid evaluation framework provides both theoretical and practical contributions, while recommending further research on advanced features such as AI-based classification and large.

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