Web-Based Equipment Borrowing Management Application for Kelanara Studio with QR Code Integration and Multi-Level Approval

Abstract

Manajemen inventaris dan peminjaman alat di Kelanara Studio masih dilakukan secara konvensional sehingga berisiko menyebabkan inkonsistensi pencatatan, keterlambatan pelaporan kerusakan, dan lemahnya otorisasi. Penelitian ini bertujuan untuk mengembangkan aplikasi manajemen peminjaman alat berbasis web dengan integrasi persetujuan berjenjang dan QR Code menggunakan metode Scrum, yang mendukung pengelolaan inventaris, pengajuan-persetujuan peminjaman, verifikasi serah terima dan pengembalian alat, serta pelaporan kerusakan. Aplikasi dikembangkan sebagai prototipe frontend berbasis React dengan penyimpanan data LocalStorage untuk validasi konsep. Hasil penelitian menunjukkan sistem berpotensi meningkatkan akuntabilitas, mempermudah pelacakan inventaris, dan mendukung pengelolaan alat secara terstruktur. Pengujian black-box menunjukkan seluruh fungsi utama berjalan sesuai kebutuhan, sedangkan beta testing pada lima indikator memperoleh persentase kelayakan 89,33%–94,67%, seluruhnya berkategori Sangat Layak.

Equipment inventory and borrowing at Kelanara Studio are still managed manually, resulting in inconsistent records, delayed damage reporting, and weak authorization control. This study aims to develop a web-based equipment borrowing management application integrating a multi-level approval workflow and QR Code technology using the Scrum framework. The system supports inventory management, borrowing requests and approvals, QR Code-based handover verification, equipment returns, and damage reporting. The application was developed as a React-based frontend prototype using LocalStorage for concept validation. The results indicate that the application has the potential to improve borrowing accountability, facilitate inventory tracking, and support more structured equipment management. Black-box testing confirmed that all core functions operated as expected, while beta testing across five distinct evaluation indicators consistently scored between 89.33% and 94.67%, all categorized as Highly Feasible.