Optimizing Exam Scheduling at AMIK Medicom Campus Using Genetic Algorithms

Abstract

Scheduling is one of the common problems in higher education institutions, particularly in arranging semester exams that involve multiple constraints such as lecturer availability, room capacity, and student schedules. At AMIK Medicom Campus, the exam scheduling process is still conducted manually, which often leads to schedule conflicts, time inefficiency, and suboptimal resource utilization. This research aims to optimize the exam scheduling process by implementing a Genetic Algorithm (GA). The method begins with chromosome representation, where each chromosome consists of genes representing course codes, lecturers, rooms, days, and time slots. The initial population is generated randomly, followed by fitness evaluation using a penalty-based function to minimize conflicts. The selection process uses the roulette wheel method, while crossover and mutation operators are applied to generate new offspring. The algorithm runs through several generations until the optimal schedule is achieved. The results show that the Genetic Algorithm can produce a conflict-free exam schedule more efficiently compared to the manual method. The system is able to reduce scheduling time significantly while accommodating all hard constraints such as lecturer and room availability. This study concludes that the Genetic Algorithm is effective in solving the exam scheduling problem at AMIK Medicom Campus and can be developed into a web-based application for academic administrative use.