Smart Attendance in Classroom (CObot): IoT and Facial Recognition for Educational and Entrepreneurial Impact
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 nam...
Published in: | APTISI Transactions on Technopreneurship |
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Pandawan Sejahtera
2024
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2-s2.0-85216779862 Zainuddin A.A.; Nor R.M.; Handayani D.; Mohd. Tamrin M.I.; Subramaniam K.; Sadikan S.F.N. Smart Attendance in Classroom (CObot): IoT and Facial Recognition for Educational and Entrepreneurial Impact 2024 APTISI Transactions on Technopreneurship 6 3 10.34306/att.v6i3.497 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216779862&doi=10.34306%2fatt.v6i3.497&partnerID=40&md5=86bd849da2bc5f73b82917c790d90030 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 individually improves security, it disrupts the learning process by consuming significant time. This study addresses these issues by developing an autonomous robot, CObot, equipped with a facial recognition system powered by a Raspberry 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 registered individuals can mark attendance, creating a secure and tamper-proof system. Additionally, the integration of Internet of Things (IoT) technology enables real-time data transfer to Google Sheets, simplifying record-keeping and reducing educators administrative workload. A 3D-printed, customizable car structure enhances the robot design, while the Raspberry Pi 5 was selected over alternatives like the ESP32-S3 for its superior processing power and faster data transfer speeds, ensuring smoother operations. In testing with 60 participants, the Raspberry Pi 5 demonstrated a 99% accuracy rate in facial recognition, outperforming 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. © 2024 Authors. Pandawan Sejahtera 26558807 English Article All Open Access; Gold Open Access |
author |
Zainuddin A.A.; Nor R.M.; Handayani D.; Mohd. Tamrin M.I.; Subramaniam K.; Sadikan S.F.N. |
spellingShingle |
Zainuddin A.A.; Nor R.M.; Handayani D.; Mohd. Tamrin M.I.; Subramaniam K.; Sadikan S.F.N. Smart Attendance in Classroom (CObot): IoT and Facial Recognition for Educational and Entrepreneurial Impact |
author_facet |
Zainuddin A.A.; Nor R.M.; Handayani D.; Mohd. Tamrin M.I.; Subramaniam K.; Sadikan S.F.N. |
author_sort |
Zainuddin A.A.; Nor R.M.; Handayani D.; Mohd. Tamrin M.I.; Subramaniam K.; Sadikan S.F.N. |
title |
Smart Attendance in Classroom (CObot): IoT and Facial Recognition for Educational and Entrepreneurial Impact |
title_short |
Smart Attendance in Classroom (CObot): IoT and Facial Recognition for Educational and Entrepreneurial Impact |
title_full |
Smart Attendance in Classroom (CObot): IoT and Facial Recognition for Educational and Entrepreneurial Impact |
title_fullStr |
Smart Attendance in Classroom (CObot): IoT and Facial Recognition for Educational and Entrepreneurial Impact |
title_full_unstemmed |
Smart Attendance in Classroom (CObot): IoT and Facial Recognition for Educational and Entrepreneurial Impact |
title_sort |
Smart Attendance in Classroom (CObot): IoT and Facial Recognition for Educational and Entrepreneurial Impact |
publishDate |
2024 |
container_title |
APTISI Transactions on Technopreneurship |
container_volume |
6 |
container_issue |
3 |
doi_str_mv |
10.34306/att.v6i3.497 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216779862&doi=10.34306%2fatt.v6i3.497&partnerID=40&md5=86bd849da2bc5f73b82917c790d90030 |
description |
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 individually improves security, it disrupts the learning process by consuming significant time. This study addresses these issues by developing an autonomous robot, CObot, equipped with a facial recognition system powered by a Raspberry 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 registered individuals can mark attendance, creating a secure and tamper-proof system. Additionally, the integration of Internet of Things (IoT) technology enables real-time data transfer to Google Sheets, simplifying record-keeping and reducing educators administrative workload. A 3D-printed, customizable car structure enhances the robot design, while the Raspberry Pi 5 was selected over alternatives like the ESP32-S3 for its superior processing power and faster data transfer speeds, ensuring smoother operations. In testing with 60 participants, the Raspberry Pi 5 demonstrated a 99% accuracy rate in facial recognition, outperforming 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. © 2024 Authors. |
publisher |
Pandawan Sejahtera |
issn |
26558807 |
language |
English |
format |
Article |
accesstype |
All Open Access; Gold Open Access |
record_format |
scopus |
collection |
Scopus |
_version_ |
1825722578053890048 |