AI-Assisted Barcode Inventory Management for Plantation Warehouse Operations in a Desktop Environment - Bintang Florentina Aruan; Ritna Wahyuni; Ratu Mutiara Siregar (Academic Bright Collaboration, 2026)
Purpose – This study aims to Development of a Desktop-Based Warehouse Inventory Management Application with Barcode Technology Integration Methods – The study used a Research and Development approach with the Waterfall development model. Data were collected through literature study, observation, and interviews, while system functions were validated using Black Box Testing. Findings – The developed application supports item data management, category, unit, warehouse location, incoming goods, outgoing goods, stock adjustment, inventory reporting, minimum stock alerts, and an Inventory AI Assistant as a supporting database-search feature. The testing results indicate that the main functions run according to the planned scenarios. Research implications – The system can serve as an initial solution to support more orderly and traceable inventory administration in a plantation warehouse environment. Originality – This study positions barcode as an item-identification mechanism in a local desktop application and includes an Inventory AI Assistant that is limited to database-based information retrieval.