Berliana, Vania (2020) DECISION SUPPORT SYSTEM BASED ON GENETIC ALGORITHM FOR COURSE SCHEDULING PROBLEMS. S1 thesis, Universitas Atma Jaya Yogyakarta.
|
Text (Vania Berliana)
1607089181.pdf Download (3MB) | Preview |
|
![[img]](http://e-journal.uajy.ac.id/style/images/fileicons/text.png) |
Text
1607089184.pdf Restricted to Registered users only Download (948kB) |
|
|
Text
1607089185.pdf Restricted to Registered users only Download (1MB) |
Abstract
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.
| Item Type: | Thesis (S1) |
|---|---|
| Uncontrolled Keywords: | Genetic Algorithms, Course Scheduling, Method, Lecture, Optimally, Complexity |
| Subjects: | Teknik Informatika > Soft Computing |
| Divisions: | Fakultas Teknologi Industri > Teknik Informatika |
| Depositing User: | Editor UAJY |
| Date Deposited: | 22 Feb 2021 11:46 |
| Last Modified: | 22 Feb 2021 11:46 |
| URI: | http://e-journal.uajy.ac.id/id/eprint/23331 |
Actions (login required)
 |
View Item |
