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OJT AtTRACK – On the Job Trainee Remote Attendance Monitoring System using Face Recognition System

Epifelward Niño O. Amora1, Kennery V. Romero2, Rennan C. Amoguis3,
Evangeline N. Olandria4, Joseph James Olavides5
Bohol Island State University – Candijay Campus, Cogtong, Candijay, Bohol, Philippines 1-5




The checking and monitoring of On-the-Job Trainee’s Attendance (OJT) faced several problems in the end of the OJT coordinators and the home institution. Some of the problems are checking the attendance of the OJT validation and verification of students’ attendance, tracking the students’ real-time OJT progress and generating up to date reports. It becomes even very alarming when the OJT Coordinators lost track the students’ OJT progress. This study sought to investigate and implement a solution using technology innovation mainly the internet of things to make checking of attendance and monitoring of progress effective and convenient. Using the Agile: Scrum methodology of software engineering, the researchers conducted a series of stakeholder’s meetings with the OJT coordinators to collect user stories. After the collection, the researchers developed a schedule of activities, staffing, hardware and software requirements in order to carry out all the tasks needed to deliver the fully functional product. To keep track the progress of the project, the Scrum master conducted a daily standup meeting with the team in order to express the points that might hinder the pace of the development. The team leader also communicates daily with the clients to give them a comprehensive update. Findings revealed that the current manual system used to check the OJT attendance and track OJT progress which is prone to fraud and tampering which causes record irregularities. Thus, there is a need to develop an internet of things based (IOT) based hardware and software application that checks the attendance and view reports in real-time. By this means, frauds on attendances and records were eliminated as the attendance system is secured.

Keywords: Face Recognition, Internet of Things, Microcomputing, OpenCV, CoVid Response

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