Scheduling Subject used Genetical Algoritms (Case Studies: SMP Negeri 1 Delitua)
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Published:
Feb 28, 2024
Keywords:
Genetic Algorithm, Roulette Wheels, Scheduling
Section
Computer Science
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Abstract
It is known that the preparation of subject scheduling at SMP Negeri 1 Deli Tua has gone through a process that is less efficient and effective due to several long procedures such as dividing study hours, adjusting teaching hours, and allowing for similarities in teaching hours or clashes between teaching hours. A genetic algorithm is an optimization technique whose working method imitates the process of evolution and changes in genetic structure in living things. The research aims to apply genetic algorithms to the subject scheduling system at SMP Negeri 1 Delitua. Research was conducted at SMP Negeri 1 Deli Tua. This research has used a genetic algorithm using the roulette wheel method and a GUI (Graphic User Interface) created with PHP and MySQL. The research results show that the application of genetic algorithms in scheduling systems can make scheduling more accurate and in a shorter time, with a mutation probability of 3.6 = 4 genes
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How to Cite
Siregar, S. A., Kurniawan R, R. . and Suhardi, . S. (2024) “Scheduling Subject used Genetical Algoritms (Case Studies: SMP Negeri 1 Delitua)”, Jurnal Mantik, 7(4), pp. 3634-3645. doi: 10.35335/mantik.v7i4.4792.
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Suyanto. (2018). Algoritma genetika dalam matlab. Andi Press.Yogyakarta.
Xie, L., Chen, Y., Chang, R. (2021). Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm. Appl. Sci., 11(12), 5531. https://doi.org/10.3390/app11125531
Abreu, L.R., Cunha, J.O., Prata, B.A., Framinan, J. M. (2020). A genetic algorithm for scheduling open shops with sequence-dependent setup times. Computers & Operations Research, 113, 104793.
Ananda, rizki. (2021). Penerapan algoritma genetika untuk perancangan aplikasi penjadwalan mata pelajaran. Skripsi. Universitas Islam Negeri Sumatera Utara, Medan
Chen, X., Yue, X.G., Li, R., Zhumadillayeva, A. & Liu, R. (2021). Design and application of an improved genetic algorithm to a class scheduling system. International Journal of Emerging Technologies in Learning (IJET), 16(1), 44–59.
Christioko, B. V., Asmiatun, S., & Susanto. (2019). Penjadwalan kegiatan praktikum menggunakan algoritma genetika (studi kasus: Jurusan Teknologi Informasi Universitas Semarang). Voice of Information (VoI), 11, 25–34.
Dai, J., Geng, N., Xie, X. (2021). Dynamic advance scheduling of outpatient appointments in a moving booking window. European Journal of Operational Research, 292(2), 622–632. https://www.sciencedirect.com/science/article/abs/pii/S0377221720309760
Elva, Y. (2019). Sistem penjadwalan mata pelajaran menggunakan algoritma genetika. Jurnal Teknologi Informasi (JurTI), 3, 49–57. http://jurnal.una.ac.id/index.php/jurti/article/view/687/599
Gen, M., Lin, L. (2023). Genetic algorithms and their applications. Springer Handbook of Engineering Statistics, 635–674.
George Amalarethinam DI, K. S. (2018). Rescheduling Enhanced Min-Min (REMM) Algorithm for Meta-task Scheduling in Cloud Computing. International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI), 895–902. https://link.springer.com/chapter/10.1007/978-3-030-03146-6_102#citeas
Hasugian, A. H. (2020). Edisi revisi i basis data. Skripsi. Universitas Islam Negeri Sumatera Utara, Medan
Irfan, M., Lubis, M. R., & Nasution, Z. M. (2022). Implementation of genetic algorithm for subject scheduling at sd taman cahya pematangsiantar. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 1(2), 151–158.
Jiang, Y., Mehrizi, H. A., Diao, Y. (2020). Data-driven analytics to support scheduling of multi-priority multi-class patients with wait time targets. European Journal of Operational Research, 281(3), 597–611.
KBBI. (n.d.). Retrieved July 3, 2022, from https://kbbi.web.id/jadwal
Liu, W.; Yue, X.-G.; Tchounwou, P. B. (2020). Response to the covid-19 epidemic: the chinese experience and implications for other countries. Int. J. Environ. Res. Public Health, 17, 2304.
Ma, Z.; Li, S.; Wang, Y.; Yang, Z. (2021). Component-level construction schedule optimization for hybrid concrete structures. Autom. Constr., 125, 103607.
Mouhamadou, LB, Bounama Gueye, Amadou Dahirou Gueye, Omar Kassé, M. H. W. M. (2019). impacts of the migration of cross-cutting courses of a traditional university in distance learning. International Journal of Engineering Pedagogy, 9(2).
Pirozmand, P., Hosseinabadi, A.S.R., Farrokhzad, M., Sadeghilalimi, M., Mirkamali, S., Slowik, A. (2021). Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing. Neural Computing and Applications, 33, 13075–13088.
Sagala, J. R. (2018). Model rapid application development (rad)dalam pengembangan sistem informasi penjadwalanbelajar mengajar. Jurnal Mantik Penusa, 2, 87–90.
Sardjono, W., Priatna, W., Nugroho, D.S., Rahmasari, A., Lusia, E. (2021). Genetic algorithm implementation for application of shifting work scheduling system. ICIC Express Letters, 15(7), 791–802.
Saure, A., Begen, M.A., Patrick, J. (2020). Dynamic multi-priority, multi-class patient scheduling with stochastic service times. European Journal of Operational Research, 280(1), 254–265. https://ruor.uottawa.ca/bitstream/10393/39651/1/cs_manuscript.pdf
Squires, M., Tao, X., Elangovan, S., Gururajan, R., Zhou, X., Archarya, U. . (2022). A novel genetic algorithm based system for the scheduling of medical treatments. Expert Systems with Applications, 195, 116464.
Sudaria, Putra, A. S., & Novembrianto, Y. (2021). Sistem manajemen pelayanan pelanggan menggunakan php dan mysql ( studi kasus pada toko surya). Tekinfo, 22(1), 100–117.
Sugeha, I. H. (2019). Optimasi penjadwalan menggunakan metode algoritma genetika pada proyek rehabilitasi puskesmas minanga | sugeha | jurnal sipil statik. Jurnal sipil statik. https://ejournal.unsrat.ac.id/index.php/jss/article/view/26145
Suyanto. (2018). Algoritma genetika dalam matlab. Andi Press.Yogyakarta.
Xie, L., Chen, Y., Chang, R. (2021). Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm. Appl. Sci., 11(12), 5531. https://doi.org/10.3390/app11125531
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