University Courses timetabling is scientifically known as a nondeterministic polynomial time (NP)-hard problem and is still an exciting topic to study due to the difficulty to find an exact algorithm that can solve the problem in polynomial time. Prior studies in the scientific literature have recognized the importance of automation and optimization of course timetabling problems, especially for the university level that require a fast and effective method to timetable thousands of courses at the beginning of the academic period the complexity of this problem has attracted the interest of competition, namely the International Timetabling Competition (ITC) 2019, to raise ideas of algorithms to solve the problem. This study investigates the performance of Iterated Local Search-Hill Climbing (ILS-HC) and Iterated Local Search-Simulated Annealing (ILS-SA), Algorithms within hyper-heurism in solving university course timetabling problem of ITC 2019 problem domain and datasets. Tested over tiny and small datasets of ITC 2019 problems, the experimental results show that ILS-SA outperforms ILS-HC for both datasets. Specifically, over tiny dataset, ILS-SA could minimize the objective function to 6 compared to 37 as result of ILS-HC algorithm. While over small datasets ILS-SA outperforms ILSHC by 776 compared to 1034.