Genetic Algorithm Optimization for Automatic Scheduling in the System at State Junior High School Four Binjai

Abstract

Binjai State Junior High School faces challenges in developing an optimal class schedule each semester. The manual process currently in place is often time-consuming, error-prone, and inflexible in adapting to sudden changes such as teacher changes or school policy changes. Furthermore, scheduling conflicts frequently occur, where a teacher is scheduled to teach two different classes at the same time, or a class has two subjects in one session. This scheduling process must consider various constraints, such as teacher availability, class size, subject matter, and limited learning space. The manual scheduling process is often time-consuming, error-prone, and difficult to adapt to sudden changes such as teacher changes or curriculum changes. To address these challenges, an automated system is needed that can generate schedules efficiently and optimally. Genetic Algorithms are a method in artificial intelligence that can solve optimization problems by mimicking biological evolutionary mechanisms such as selection, crossover, and mutation. By implementing Genetic Algorithms in the scheduling system, it is hoped that more optimal schedules can be produced by reducing schedule conflicts and increasing time efficiency in the scheduling process.