Stanley Putra Pratama, Muhammad
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Rancangan Sistem Informasi Inventory Mesin EDC (Electronic Data Capture) PT. Ezeelink Indonesia Fridayanthie, Eka Wulansari; Susila, Mochammad Nandi; ., Hariyanto; Stanley Putra Pratama, Muhammad; ., Riski
Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya Lubuklinggau Vol 8 No 1 (2026): Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya
Publisher : STMIK Bina Nusantara Jaya Lubuk Linggau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52303/jb.v8i1.190

Abstract

Ezeelink Indonesia, as a company engaged in electronic transaction services and the provision of Electronic Data Capture (EDC) machines , faces challenges in managing EDC machine inventory, which is still carried out manually using spreadsheet files. This manual approach has the potential to cause data duplication, input errors, information loss, and difficulty in tracking EDC units that have been loaned or are under maintenance. This research aims to design and build an integrated web-based EDC machine inventory management information system , to improve time and cost efficiency in managing EDC machine inventory, facilitate tracking of EDC status and location, and provide accurate and up-to-date data reports. The research method used is a descriptive approach with data collection through direct observation at PT. Ezeelink Indonesia , interviews with operational staff , and literature studies. The system development method uses the waterfall model , which includes planning, analysis, design, development, testing, implementation, and maintenance phases. The system design uses Unified Modeling Language (UML) for use case and activity diagrams , as well as Entity Relationship Diagram (ERD) and Logical Record Structure (LRS) for database design. The designed system has one user role, Admin, who has full control over all system functions, from login, managing item data, history, reports, to other user management. With this system, it is expected that PT. Ezeelink Indonesia can optimally manage EDC machines, reduce human errors in data recording , and support accurate and up-to-date data-driven decision-making.