Android-Based Inventory System and Near Field Communication for Asset Inventory at SMAN 8 Malang
DOI:
https://doi.org/10.33795/jartel.v15i2.6059Keywords:
Android, Firebase, Inventory Management System, Machine Learning, NFC, NTAG213, OCRAbstract
SMAN 8 Malang currently employs manual methods and barcode/QR code technology, which are inefficient. The purpose of this study is to design and develop an Android application that integrates Near Field Communication (NFC) with NTAG213 chips as unique asset identifiers, and Optical Character Recognition (OCR) supported by machine learning from Firebase for data entry automation. The system also establishes cloud-based data storage using Firebase to ensure data accessibility and security. Through the Research and Development (R&D) method with the Waterfall model, testing was conducted by measuring NFC data transfer speed and OCR accuracy. Results indicate improved efficiency and accuracy in asset data collection, with an average NFC transfer rate of 81.6 kbps and 100% OCR accuracy. In conclusion, integrating NFC and OCR in an Android application is a promising solution to enhance inventory management in educational environments, contributing to the development of more modern, efficient, and accurate inventory systems, and has the potential to be implemented in various educational institutions.
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