Implementation of Haversine, Mock Location Detection, and Face Recognition in Attendance Systems

Abstract

The advancement of information technology has encouraged organizations to adopt digitally integrated systems in managing human resources. However, several companies still implement manual attendance and payroll processes that are vulnerable to recording errors, inefficiency, and data manipulation. At PT Munggaran Jaya Utama, employee attendance is conducted through photo-based reporting and manually recapitulated using spreadsheet applications, resulting in potential inaccuracies in attendance and payroll calculations. This research aims to implement the Haversine Algorithm, Mock Location Detection, and Face Recognition within a web and mobile-based attendance system to improve accuracy, security, and operational efficiency. This study employed the Research and Development (R&D) method with the Agile Scrum framework. Data collection techniques included observation, interviews, documentation, and literature studies. The Haversine Algorithm was utilized to calculate the distance between employee coordinates and attendance locations. Mock Location Detection was applied to identify GPS manipulation attempts, while Face Recognition was implemented to verify employee identity through selfie authentication during attendance. The results indicate that the proposed system improves attendance validation accuracy, minimizes fraudulent activities such as fake GPS and proxy attendance, and enhances payroll data processing efficiency. Furthermore, the system supports real-time attendance monitoring and integrated data management.