Kolumbano i-Leave: Online Leave Management System with Trends using Data Visualization
Human Resources (HR) plays a vital role in overseeing recruitment, training, and employee benefits, ensuring organizations adapt to changing business requirements while maintaining workforce productivity. Among its responsibilities, HR manages employee records, including the monitoring of leave availability, which requires skilled personnel. This study developed a web-based system to manage employees’ leave benefits, categorized into two types: Statutory Leave and Institutional Leave. Statutory Leave refers to leave types mandated by law, such as maternity or paternity leave, while Institutional Leave is determined by the organization, such as academic or professional development leave. The system incorporated a multi-level approval process tailored to an academic setting. For leave applications, teachers required approval from their Dean before HR finalizes the request, while sick leave passes through the clinic for notation before HR approval. Non-teaching staff followed a similar process with their immediate department Unit Heads. Deans and Unit Heads followed the same process, with their respective Vice Presidents approving regular leave and the clinic noting sick leave before HR’s finalization. Additionally, the system leveraged data visualization tools to monitor leave availability and trends, providing valuable insights for unit heads. These features enhanced transparency, operational efficiency, and employee satisfaction. Testing outcomes validated the system’s reliability, usability, and scalability, achieving an average of 94.26% for non-functional requirements and 100% for functional requirements, surpassing acceptable performance standards. Future recommendations included the development of a mobile application to improve portability and implementation.
Publication Details
- Journal
- International Journal of Science and Applied Information Technology
- Published
- 2025-04-08
- DOI
- https://doi.org/10.30534/ijsait/2025/031422025
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00