| dc.description.abstract | The Directorate of Digitalization and System Integration (Ditgrasi) of Universitas Sumatera Utara faces significant challenges in manually scheduling website training activities, which are complex, time-consuming, and prone to schedule conflicts. This research aims to develop an Android-based time management application that implements the Rank-based Ant System (RankedAS) algorithm to provide an automated scheduling solution for these issues within the Ditgrasi environment. The application's core scheduling function was tested using the Black Box Testing method through two complex scenarios. The results showed that the application successfully generated an optimal and entirely conflict-free schedule in the first scenario, which involved 20 training activities and 2 meeting schedules as constraints. In the second, more complex scenario with 30 training activities and 5 meeting schedules, the system's performance was deemed quite satisfactory despite two identified conflict case related to human resource allocation, in which a programmer was scheduled in two different rooms simultaneously. However, most schedules were successfully optimized according to the existing constraints. Furthermore, algorithm testing was performed to prove its accuracy by comparing the application's results with manual calculations in a simple scenario. In the first iteration, two solutions generated by the ants scored 100, with the first-ranked solution's pheromone updated by 200 and the second-ranked by 100. In the second iteration, both solutions also scored 100, and the pheromones were updated to 290 and 200 for the first rank and 280 for the second rank, matching the manual calculation. The results indicate that the application can produce accurate calculations. This research demonstrates that the developed automated scheduling application based on the Rank-based Ant System functions, as expected, shows great potential for improving efficiency and reducing the workload at Ditgrasi USU. Nevertheless, the application requires further optimization to handle constraints for high complexity and density scheduling scenarios. | en_US |
