Algorithm selection is the main key in producing a system, especially an artificial intelligence-based system, for example, a course scheduling system that involves many constraints in producing an optimal schedule. The use of inertia weight in the Modified Particle Swarm Optimization (MIPSO) algorithm which is used as a scheduling algorithm in other research can be optimized by using a combination of inertia weight and constriction factor that involved in Inertia Constriction Weight Factor Particle Swarm Optimization (ICWFPSO) algorithm. Based on that fact, the aim of this research is to develop a scheduling system using the ICWFPSO algorithm and measuring the performance of ICWFPSO compared to the scheduling system that was previously built using MIPSO in the terms of time consumption, CPU, and memory usage. Scheduling system development is carried out using the waterfall method with research steps namely problem and needs analysis, data collection and literature study, system design, system implementation, and ends with testing the performance of the algorithms that are applied to the scheduling system. Based on the tests carried out, it can be concluded that the ICWFPSO algorithm provides 200% of better performance compared to the MIPSO algorithm in terms of optimal schedule generation time. Meanwhile, tests carried out on CPU and RAM usage showed that there was no significant impact on these two parameters through the implementation of MIPSO and ICWFPSO in the course scheduling system created.