Smart Attendance in Classroom (CObot): IoT and Facial Recognition for Educational and Entrepreneurial Impact

Zainuddin, Ahmad Anwar and Mohd Nor, Rizal and Handayani, Dini Oktarina Dwi and Mohd Tamrin, Mohd Izzuddin and Subramaniam, Krishnan and Nur Sadikan, Siti Fairuz (2024) Smart Attendance in Classroom (CObot): IoT and Facial Recognition for Educational and Entrepreneurial Impact. APTISI Transactions on Technopreneurship, 6 (3). pp. 608-622. ISSN 2656-8888

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Abstract

Current attendance methods, though simple, are prone to manipulation and can be time consuming for both educators and students. For instance, manual systems and QR code based methods allow students to register attendance on behalf of others due to the lack of unique identification. While calling names in- dividually improves security, it disrupts the learning process by consuming sig- nificant time. This study addresses these issues by developing an autonomous robot, CObot, equipped with a facial recognition system powered by a Rasp- berry Pi microcontroller. CObot navigates classrooms autonomously, avoiding obstacles, and efficiently records attendance without requiring movement from students or educators. The use of facial recognition ensures that only regis- tered individuals can mark attendance, creating a secure and tamper-proof sys- tem. Additionally, the integration of Internet of Things (IoT) technology enables real-time data transfer to Google Sheets, simplifying record-keeping and reduc- ing educators administrative workload. A 3D-printed, customizable car structure enhances the robot design, while the Raspberry Pi 5 was selected over alterna- tives like the ESP32-S3 for its superior processing power and faster data trans- fer speeds, ensuring smoother operations. In testing with 60 participants, the Raspberry Pi 5 demonstrated a 99% accuracy rate in facial recognition, outper- forming the ESP32-S3 90% accuracy. By saving time, improving security, and reducing manual effort, CObot enhances the classroom environment, benefiting both students and educators. While the improvement in attendance systems may appear incremental, CObot represents a meaningful step toward fostering a more efficient and effective learning environment.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Smart Attendance; System Facial Recognition; ESP32-S3; Raspberry Pi 5; Internet of Things
Subjects:	T Technology > T Technology (General) > T11.95 Industrial directories
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Ts.Dr. Ahmad Anwar Zainuddin
Date Deposited:	26 Jan 2025 17:25
Last Modified:	26 Jan 2025 17:25
URI:	http://irep.iium.edu.my/id/eprint/118820

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