Application of genetic algorithm for class scheduling (Case study: Faculty of science and technology UIN Jakarta)

The objective of this research is about building a class scheduling application using genetic algorithm. The parameters used in genetic algorithms namely: iteration, PM (Probability Mutation), PC (Probability crossover). In Faculty of Science and Technology UIN Jakarta, class schedule built by a staff. The staff faces numbers of obstacles when building the schedule, among others: the limitations of the classroom and schedule of lectures, and large number of students. However, it is able to overcome the obstacles but it takes quite a long time to build. To optimize the tasks, an application using genetic algorithm is suggested. In this research, interviews and literature studies are used as research methodology, Waterfall as application development methods and genetic algorithms for scheduling. This application was developed using HTML, PHP, and MySQL. To get the optimal parameter values,_some variance of iteration (ns), PC, PM values were tested. The results show that the optimal value for gaining the best schedule are maximum of 20 iterations, PC 0.8 and PM 0.01. Using this values, the class scheduling application generates approximately 1,201 without any clash among the data and this application can facilitate the building of class schedule.