Abstract: This research aims to get optimal collision of schedule by using certainty variables. Courses scheduling is conducted by ant colony algorithm. Setting parameters for intensity is bigger than 0, visibility track is bigger than 0, and evaporation of ant track is 0.03. Variables are used such as a number of lecturers, courses, classes, timeslot and time. Performance of ant colony algorithms is measured by how many schedules same time and class collided. Based on executions, with a total of 175 schedules, the average of a cycle is 9 cycles (exactly is 9.2 cycles) and an average of time process is 29.98 seconds. Scheduling, in nine experiments, has an average of time process of 19.99 seconds. Performance of ant colony algorithm is given scheduling process more efficient and predicted schedule collision.

Rangga Sidik, Mia Fitriawati, Syahrul Mauluddin and A.Nursikuwagus. “A Schedule Optimization of Ant Colony Optimization to Arrange Scheduling Process at Certainty Variables ”. International Journal of Advanced Computer Science and Applications (IJACSA) 9.12 (2018). http://dx.doi.org/10.14569/IJACSA.2018.091246

@article{Sidik2018,
title = {A Schedule Optimization of Ant Colony Optimization to Arrange Scheduling Process at Certainty Variables },
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.091246},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091246},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {12},
author = {Rangga Sidik and Mia Fitriawati and Syahrul Mauluddin and A.Nursikuwagus}
}