Scheduling lecturers is a complex problem that offer occurs on campus. Scheduling is
needed to manage work time, so that the schedule is as efficient as possible. A scheduling will
look easy if the number of scheduled components is relatively small, but solving the problem
of scheduling lectures in very large numbers until now is still a complicated problem to be
solved manually.
Several processes are needed so that the schedule will be formed optimally according
to the rules given. The rule in making this course scheduling is that courses for the same class
are not allowed to be held simultaneously, one space is only for one course, for practicum
subjects are in the laboratory, the possibility that the lecturer will teach more than one subject,
there is a possibility that the number of courses and the number of lecturers is not comparable,
so solutions must be considered so that lecturers do not teach two different courses on the day
and the same time. There is also a possibility of excessive teacher teaching hours. In addition,
the availability of classrooms must also be considered so that lecturing activities can be carried
out. For this reason, a method is needed to solve the complexity of scheduling lectures so that
learning activities can be carried out optimally.
Genetic algorithms can be used as an alternative solution to solve the subject scheduling
problem. This information system with genetic algorithms can process lecturer data, courses,
rooms, and lecture time slots into optimal lecture schedules, which there are no more clashes
in courses and the availability of efficient use of space for all existing courses.