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p-Index From 2021 - 2026
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All Journal Journal of Deep Learning, Computer Vision and Digital Image Processing
Muhammad Fathur Rachman
Universitas Negeri Makassar
Computer Science & IT

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Design and Evaluation of an ESP32-Based Smart Helmet Locker Using Student Identity Card Authentication Muhammad Fathur Rachman; Haripuddin; Satria Gunawan Zain; Jumadi Mabe Parenreng; Sanatang
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 1 March 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i1.1522

Abstract

Purpose – To develop and evaluate an ESP32-based smart helmet locker using student identity card (KTM) authentication. Methods – Development followed the ADDIE model. The prototype integrated RFID authentication, a solenoid lock, Google Sheets logging, and Telegram notifications. Controlled tests addressed selected ISO/IEC 25010 quality characteristics, and 24 students completed a study-specific usability questionnaire. Findings – Functional and repeated-operation tests each achieved 100% success under the defined conditions. Mean card-detection, LCD-response, and Telegram-notification times were 1.85, 3.55, and 3.25 seconds. The questionnaire’s mean raw total was 45.00 out of 50; because negative items were not reverse-scored, this value is not a validated overall usability or feasibility score. Research implications – The prototype supports controlled locker access and monitoring in the tested scenarios. A single prototype, internet dependence, and limited user data constrain generalizability. Helmet-theft reduction, comprehensive security, and long-term field reliability were not established. Originality – The study integrates established authentication, control, logging, and notification technologies for a campus helmet-locker application.
Co-Authors Haripuddin Jumadi Mabe Parenreng Sanatang