TRAVEL AND TOURS MANAGEMENT SYSTEM: HUMAN RESOURCES III (LEAVE MANAGEMENT, TIME AND ATTENDANCE, CLAIMS AND REIMBURSEMENT, AND PAYROLL) WITH AUTOMATED SCHEDULING POWERED BY TENSORFLOW

Abstract

This capstone project introduces a Travel and Tours Management System with Human Resources III, developed for JVD Event and Travel Management Company. The system automates key HR functions, including leave management, time and attendance tracking, claims and reimbursement processing, and payroll computation.

A standout feature is the integration of biometric fingerprint data, manually imported via USB, to ensure accurate and secure attendance records. The system also leverages AI-powered scheduling using TensorFlow to enhance workforce planning and operational efficiency. This capstone project presents a Travel and Tours Management System with Human Resources III, developed for JVD Event and Travel Management Company. The system streamlines core HR operations such as leave management, time and attendance tracking, claims and reimbursement processing, and payroll computation.

The project adopts the Agile Scrum methodology, allowing for iterative development, continuous stakeholder feedback, and incremental refinement of system functionalities.

Technically, the system is built using PHP Laravel for the backend, JavaScript with Tailwind CSS for the frontend, and MySQL for database management. A RESTful API ensures seamless communication across system modules.

Attendance data is captured through biometric fingerprint devices, exported via flash drive, and manually imported into the system to maintain accuracy and security. For workforce optimization, the system integrates AI-powered scheduling using TensorFlow, which automates and enhances the planning of employee work shifts.

Comprehensive testing—including unit testing, system testing, and user acceptance testing (UAT)—was conducted to ensure system reliability, performance, and user satisfaction. The implemented system significantly improves Human Resource Management System (HRMS) procedures at JVD Event and Travel Management Company. The integration of biometric attendance tracking effectively eliminates fraudulent timekeeping, ensuring accurate and reliable employee records. The use of TensorFlow for automated scheduling optimizes workforce deployment, reducing scheduling conflicts and enhancing operational efficiency.

Furthermore, automated payroll and claims processing minimize manual errors and accelerate administrative workflows. User feedback, particularly from HR managers and employees, indicates high satisfaction levels, citing the system's user-friendly interface, enhanced transparency, and streamlined processes as key contributors to improved productivity and engagement. The findings underscore the substantial benefits of integrating AI-driven automation into Human Resource Management Systems, particularly within the travel and tour industry. By enhancing attendance tracking, leave management, and payroll processing, the system contributes to increased workforce productivity, improved financial accuracy, and greater administrative efficiency.

Despite its advantages, the project also faced challenges, including user adaptation to new technology and the need for regular system updates to maintain optimal performance. Addressing these concerns requires consistent user training, technical support, and a robust update mechanism.

For future research and development, the study recommends exploring:

Real-time biometric data synchronization to further streamline attendance tracking,

AI-driven predictive analytics for proactive workforce planning and shift allocation,

And expanding system integration with other enterprise tools for end-to-end HR and operations management.

Overall, the project highlights the critical need for continuous innovation in HR systems to meet the evolving demands of the travel and tourism sector and to ensure scalable, intelligent, and responsive workforce management solutions.