Subject scheduling system using Ant Colony Optimization at MAN 3 Palembang

Muhammad Rizky Al Ashri, Kanda Januar Miraswan, Annisa Darmawahyuni, Meylani Utari

Abstract

In preparing the subject schedule, it must be done correctly because all teaching and learning activities are between teachers and students. So far, the subject scheduling process at MAN 3 Palembang is carried out manually so that clashes often occur between subjects and teachers who teach can teach in different classes at the same time resulting in the teaching and learning process being slightly disrupted. One of the common metaheuristic algorithms The solution used for optimization problems is the Ant Colony Optimization algorithm or commonly known as the ant algorithm. The application system or users of this application to create schedules using the Ant Colony Optimization algorithm method is useful for operators who create schedules in schools. This system can also be applied in cases where schedules conflict, namely teachers teaching in the same room and teachers teaching the same subject teaching in different classes at the same hours. This makes it easier for operators to create schedules so that they can be resolved more easily and quickly. This application was successfully developed into a subject scheduling system and managed to run optimally. From the results of implementing scheduling using the Ant Colony Optimization algorithm method used in compiling subject rosters, it can help the MAN 3 Palembang school which previously carried out schedule preparation manually.

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References

Jain, A,. Jain, S., and Chande, P. (2010). Formulation of Genetic Algorithm to Generate Good Quality Course Timetable. International Journal of Innovation, Management and Technology 1, 248-251.

Nugraha, D., and Kosala, R. (2014). A Comparative Study Of Evolutionary Algorithms For School Scheduling Problem. Journal Of Theoretical & Applied Information Technology, 67(3).

Alamsyah, and Wardoyo, R. (2004). Optimalisasi Penjadwalan Multi Constraint menggunakan Logika Fuzzy = Multi Constraint Scheduling Optimization Using Fuzzy Logic. Sains dan Sibernatika, 17.

Tiwari, P. K., and Vidyarthi, D. P. (2016). Improved auto control ant colony optimization using lazy ant approach for grid scheduling problem. Future Generation Computer Systems, 60, 78-89.

Jay, H., and Render, B. (1996). 3rd Edition. Production and Operations Management: Strategies and Tactics. Englewood Cliffs, New Jersey: Prentice-Hall.

Raio, C. C., Modayil, V., Cassara, M., Dubon, M., Patel, J., Shah, T., and Liu, Y. T. (2009). Can emergency medical services personnel identify pneumothorax on focused ultrasound examinations?. Critical Ultrasound Journal, 1, 65-68.

Sugioko, A. (2013). Perbandingan Algoritma Bee Colony dengan Algoritma Bee Colony Tabu List dalam Penjadwalan Flow Shop. Jurnal Metris, 14(02), 113-120.

Reddy, S. S., and Bijwe, P. R. (2016). Efficiency Improvements in Meta-heuristic Algorithms to Solve the Optimal Power Flow Problem. International Journal of Electrical Power & Energy Systems, 82, 288-302.

Fernandez, A., Handoyo, E., and Somantri, M. (2011). “Pembangunan Aplikasi Penyusunan Jadwal Kuliah Meggunakan Algoritma Semut.” Teknik Elektro Fakultas Teknik UNDIP.

DOI: https://doi.org/10.36706/sjia.v5i2.119

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