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Penerapan Algoritma ACO untuk Penjadwalan Kuliah Pengganti pada Perguruan Tinggi (Studi Kasus: Program Studi Informatika, Universitas Multimedia Nusantara)
Author(s) -
Indah Noviasari,
Andre Rusli,
Seng Hansun
Publication year - 2019
Publication title -
ultima infosys/ultimainfosys
Language(s) - English
Resource type - Journals
eISSN - 2549-4015
pISSN - 2085-4579
DOI - 10.31937/si.v9i2.1062
Subject(s) - ant colony optimization algorithms , computer science , schedule , scheduling (production processes) , informatics , multimedia , ant , ant colony , operations research , engineering management , operating system , artificial intelligence , operations management , engineering , electrical engineering
Students and scheduling are both essential parts in a higher educational institution. However, after schedules are arranged and students has agreed to them, there are some occasions that can occur beyond the control of the university or lecturer which require the courses to be cancelled and arranged for replacement course schedules. At Universitas Multimedia Nusantara, an agreement between lecturers and students manually every time to establish a replacement course. The agreement consists of a replacement date and time that will be registered to the division of BAAK UMN which then enter the new schedule to the system. In this study, Ant Colony Optimization algorithm is implemented for scheduling replacement courses to make it easier and less time consuming. The Ant Colony Optimization (ACO) algorithm is chosen because it is proven to be effective when implemented to many scheduling problems. Result shows that ACO could enhance the scheduling system in Universitas Multimedia Nusantara, which specifically tested on the Department of Informatics replacement course scheduling system. Furthermore, the newly built system has also been tested by several lecturers of Informatics UMN with a good level of perceived usefulness and perceived ease of use. Keywords—scheduling system, replacement course, Universitas Multimedia Nusantara, Ant Colony Optimization

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