Development of Adaptive Lecture Scheduling System using Genetic Algorithm Case Study: Ahmad Dahlan Institute of Technology and Business

Optimal course scheduling is a crucial aspect in supporting the efficiency of the teaching and learning process in higher education. In many institutions, lecture scheduling is still done manually or with static methods that are not adaptive to changing needs and limited resources. This research aims to develop an adaptive lecture scheduling system using genetic algorithms, with a case study at ITB Ahmad Dahlan. Genetic algorithms were chosen because of their ability to solve complex optimization problems with high efficiency, such as managing dynamic variables such as lecturer availability, rooms, and lecture time preferences. In this research, data related to courses, lecturers, time, classroom availability, and curriculum requirements are integrated into the designed system to generate an optimal course schedule. The development process involved several key stages, including requirements analysis, system design, algorithm implementation, and performance evaluation. Genetic algorithm implementation is done by simulating various scheduling scenarios to find the most optimal solution. The results show that the developed system is able to produce a more efficient and clash-free course schedule compared to traditional scheduling methods. In addition, the system also allows higher flexibility in adjusting the schedule to changes that may occur, such as the addition or reduction of classes. Thus, this research makes a significant contribution in improving the quality of educational services at ITB Ahmad Dahlan as well as offering solutions that can be adopted by other educational institutions facing similar challenges.