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Research in Education, Technology, and Multiculture
Published by Institute of Multidisciplinary Research and Community Service
ISSN : -     EISSN : 30256763     DOI : http://doi.org/10.61436/rietm
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Research in Education, Technology, and Multiculture is an open-access, peer-reviewed journal that provides a comprehensive platform for the dissemination of scholarly works across three primary pillars: Technology and Applied Sciences, Technology-Enhanced Education, and Ethnics and Multiculturalism. The journal accommodates a broad spectrum of studies within these fields, encouraging both independent explorations and multidisciplinary approaches. The journal welcomes original research articles, conceptual papers, case studies, and community service reports that contribute to academic development and practical knowledge. Topics of interest comprehensively cover, but are not limited to: • Technology and Applied Sciences: information and communication technology, artificial Intelligence, block chain and digital transformation, STEM disciplines, alongside industrial and practical applications of applied sciences, as well as fundamental and applied research in mathematics and natural sciences. • Technology-Enhanced Education: Digital learning environments, educational technology (EdTech) innovations, and ICT based pedagogical strategies. • Ethnics and Multiculturalism: Multiculturalism in society and educational settings, ethnic relations, immigration and migrant workers’ studies, intercultural communication, cultural heritage, and diversity in social life.
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Articles 1 Documents
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Search results for "esp32-based inventory tracker for goods recording with barcode identification" : 1 Documents clear
ESP32-Based Inventory Tracker for Goods Recording with Barcode Identification Sitti Wetenriajeng Sidehabi; Wahidah Wahidah; St. Nurhayati Jabir; Muh Rizal Wahyudin; Muhammad Fauzan Suharman
Research in Education, Technology, and Multiculture Vol 5, No 3 (2026): Research in Education, Technology, and Multiculture
Publisher : Institute of Multidisciplinary Research and Community Service

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61436/rietm/v5i3.pp208-221

Abstract

Manual inventory recording remains vulnerable to delayed updates, transcription errors, and repeated reconciliation, particularly in warehouse units that still rely on spreadsheet-based data entry. This study aims to design and evaluate a Smart Inventory Tracker for recording goods at the PT Berkah Industri Mesin Angkat Site Kendari by integrating barcode scanning, ESP32-based edge processing, and cloud-based storage. The prototype used an ESP32 development board, a GM66 barcode scanner communicating through a Universal Asynchronous Receiver-Transmitter (UART) interface, a thin-film transistor (TFT) display driven through the Serial Peripheral Interface (SPI) protocol, a 12 V DC power supply with step-down regulation, a JSON reference database hosted on GitHub, and a Google Apps Script endpoint for writing validated transactions to Google Spreadsheets. The evaluation followed an engineering research approach covering hardware assembly, software integration, database matching, transaction logging, and functional testing. System performance was assessed through barcode readability distance testing, registered and unregistered barcode validation, end-to-end response-time measurement, and paired comparison with manual Google Sheets recording. Barcode reading was reliable at 3.7-16.4 cm, while scans at 3.6 cm and 16.5 cm failed outside the empirically verified optical working range. Ten registered barcode samples were successfully displayed and recorded, with an average end-to-end cloud logging time of 4.58 s per item. Manual recording required 45.57 s per item on average; therefore, the automated method reduced the observed recording duration by 40.99 s per item, equivalent to an 89.94% time reduction. Unregistered barcodes were rejected and were not appended to the spreadsheet. These findings indicate that the architecture supports faster and more consistent recording for small- to medium-sized warehouse operations. However, component-level latency logging, offline buffering, and broader network-condition testing remain necessary before large-scale commercial deployment. Keywords: Inventory management, ESP32, Barcode identification, Internet of Things (IoT), Google spreadsheets.

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