Students' Attendance Monitoring System with SMS Notification
Students’ attendance in a university is commonly being monitored by lecturers and the Academic Affairs Division (AAD) as it may help to identify students’ problems at an early stage. This project aims to monitor the students who have the possibility to be absent from classes for more than the permi...
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Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis
2020
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oai:doaj.org-article:aab1883334854a87a6349ba4ceb826bb2021-11-06T02:24:18ZStudents' Attendance Monitoring System with SMS Notification2600-8793https://doaj.org/article/aab1883334854a87a6349ba4ceb826bb2020-10-01T00:00:00Zhttp://repeater.my/index.php/jcrinn/article/view/159https://doaj.org/toc/2600-8793 Students’ attendance in a university is commonly being monitored by lecturers and the Academic Affairs Division (AAD) as it may help to identify students’ problems at an early stage. This project aims to monitor the students who have the possibility to be absent from classes for more than the permissible percentage. Thus, the purpose of this research is to develop a students’ attendance monitoring system with Short Message Services (SMS) notification (SAMS). This system helps the AAD to manage the absenteeism report from lecturers and it automatically sends the information through SMS notification to the parents and the students themselves. The system has been developed using the Waterfall Model methodology that consists of five phases which are analysis, design, implementation, testing, and documentation. The results from the usability testing show that SAMS can help lecturers to monitor students’ absenteeism more easily and efficiently. Furthermore, the integration of the system with SMS is very useful as it can directly notify the parents regarding their children’s attendance problem. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA PerlisarticleProbabilities. Mathematical statisticsQA273-280TechnologyTTechnology (General)T1-995ENJournal of Computing Research and Innovation, Vol 5, Iss 1 (2020) |
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Probabilities. Mathematical statistics QA273-280 Technology T Technology (General) T1-995 |
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Probabilities. Mathematical statistics QA273-280 Technology T Technology (General) T1-995 Students' Attendance Monitoring System with SMS Notification |
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Students’ attendance in a university is commonly being monitored by lecturers and the Academic Affairs Division (AAD) as it may help to identify students’ problems at an early stage. This project aims to monitor the students who have the possibility to be absent from classes for more than the permissible percentage. Thus, the purpose of this research is to develop a students’ attendance monitoring system with Short Message Services (SMS) notification (SAMS). This system helps the AAD to manage the absenteeism report from lecturers and it automatically sends the information through SMS notification to the parents and the students themselves. The system has been developed using the Waterfall Model methodology that consists of five phases which are analysis, design, implementation, testing, and documentation. The results from the usability testing show that SAMS can help lecturers to monitor students’ absenteeism more easily and efficiently. Furthermore, the integration of the system with SMS is very useful as it can directly notify the parents regarding their children’s attendance problem.
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format |
article |
title |
Students' Attendance Monitoring System with SMS Notification |
title_short |
Students' Attendance Monitoring System with SMS Notification |
title_full |
Students' Attendance Monitoring System with SMS Notification |
title_fullStr |
Students' Attendance Monitoring System with SMS Notification |
title_full_unstemmed |
Students' Attendance Monitoring System with SMS Notification |
title_sort |
students' attendance monitoring system with sms notification |
publisher |
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis |
publishDate |
2020 |
url |
https://doaj.org/article/aab1883334854a87a6349ba4ceb826bb |
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1718443999834931200 |