Pengaruh Probabilitas Crossover Terhadap Kinerja Algoritma Genetika Dalam Optimasi Penjadwalan Matakuliah

https://doi.org/10.21063/jtif.2023.V11.2.69-74

Authors

Keywords:

Crossover probability, genetic algorithm, computation time, course scheduling

Abstract

Genetic Algorithm speed is determined by computation time. Computing time in AG for finding the optimum value is strongly influenced by the following parameters: population size, crossover probability (Pc), mutation probability (Pm), and the selected selection method. Pc is one of the essential parameters in AG. A chromosome that will reach the best solution can be obtained from the crossover process of the two parent chromosomes. The Pc value strongly influences the crossover process. Determining the appropriate and correct Pc value indicates how large the parent chromosome will experience crossover.The method used to analyze the effect of Pc on AG performance is changing the Pc value between 0.80-0.95. The simulation used MATLAB R2012b to obtain the best computational time for each Pc value. Meanwhile, the other AG parameters remained the same: Pm=0.05 and population size=100 for each change of Pc value.The test results using MATLABR2012b show that the fastest computing time is in the range of Pc values between 0.85-0.95 with an average computation time of 0.14564s. This indicates that for the case of optimizing the scheduling of courses in the Unimed Electrical Engineering study program, the Pc value between 0.85-0.95 will provide the fastest computation time.

Author Biography

Rudi Salman, Universitas Negeri Medan

I Introduce my Self 

My name is : Rudi Salman, ST., MT

My Religion is : Islam

Status: Married

Children: 3 people

Education: Bachelor's degree from Medan Institute of Technology, Department of Electrical Engineering, Electrical Power Engineering

Masters degree at Gadjah Mada University majoring in Electrical Engineering, Sub-Field of Electrical Power Systems

Occupation: Currently as a Lecturer (PNS) at Univ. Medan State (UNIMED) in the Electrical Engineering Study Program

References

Ongko,E., Analisis Performance atas Metode Arithmetic Crossover dalam Algoritma Genetika, Jurnal Teknologi Informasi dan Komunikasi, vol.4,No.2, hal. 76-87,Des 2015.

Elva.Y., Sistem Penjadwalan Mata Pelajaran Menggunakan Algoritma Genetika, Jur. Teknol. Inf., vol. 3, no. 1, p. 49, 2019.

Ardiansyah,H.,Junianto,M,B,S.,Penerapan Algoritma Genetika untuk Penjadwalan Mata Pelajaran,J. Media Inf. Budidarma.Vol.6,No.1, pp. 329-336, 2022.

Arkeman, Y., Seminar, K. B., dan Gunawan, H. Algoritma Genetika, Teori dan Aplikasinya untuk Bisnis dan Industri. Bogor: PT Penerbit IPB Press,2012.

Raghavendra,B.V., Effect of Crossover Probability on Proformance of Genetic Algorithm in Scheduling of Parallel Machines for BI-Criteria Objectives, Int.Journal of Engineering and Advanced Technology (IJEAT),vol.9, issue-1,2019.

Oktarina.D, dan Hajjah.A, Perancangan Sistem Penjadwalan Seminar Proposal dan Sidang Skripsi dengan Metode Algoritma Genetika, JOISIE (Journal Inf. Syst. Informatics Eng.), vol. 3, no.1, p.32 2019.

Ginantra.N.L.W.S.R., dan Anandita.I.B.G., Implementasi Algoritma Genetika Berbasis Web Pada Sistem Penjadwalan Mengajar Di Smk Dwijendra Denpasar, J. Teknol. Inf. dan Komput., vol. 5, no. 1, pp. 130–138, 2019.

Handayani.T., Fudholi.D.H., dan Rani.S., Kajian Algoritma Optimasi Penjadwalan Mata Kuliah, J. Pengkaj. Dan Penerapan Tek. Inform., vol. 13, no. 2, pp. 212–222, 2020.

Sugeha.I.H., dan Inkiriwang.R.L., Optimasi Penjadwalan Menggunakan Metode Algoritma Genetika Pada Proyek Rehabilitasi Puskesmas Minanga, J. Sipil Statik, vol. 7, no. 12, 2019.

Sobirin.S., Implementasi Algoritma Genetika untuk Penjadwalan Kuliah, Jutikomp, vol. 1, no. 2, pp. 188–194, 2018.

Nugroho.A., Priatna.W., dan Romli.I., Implementasi AlgoritmaGenetika Untuk Optimasi Penjadwalan Mata Kuliah, J.Teknol. dan Ilmu Komput. Prima, vol. 1, no. 2, pp. 35–41, 2018.

Jonathan.C.N.,et al., Implementasi Metode Algoritma Genetika Pada Penentuan Menu Makanan Untuk Membentuk Berat Badan Ideal, J.Teknol. Inf. dan Terap., vol. 6, no. 1, pp. 35–40, 2019.

Sari.Y., Alkaff.M., Wijaya.E.S., Soraya.S., dan Kartikasari.D.P., Optimasi Penjadwalan Mata Kuliah Menggunakan Metode Algoritma Genetika Dengan Teknik Tournament Selection, J. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 1, pp. 85–92,2019, doi: 10.25126/jtiik.201961262

Juwita.A.R., Pratama.A.R., dan Triono.T., Implementasi Algoritma Particle Swarm Optimization untuk Penjadwalan Perkuliahan di Fakultas Teknik Dan Ilmu Komputer Universitas Buana Perjuangan Karawang, J. Sisfotek Glob., vol.10, no. 1, pp. 18–26, 2020.

Ongko,E., Analisis pengaruh Mutasi terhadap Performance Algoritma Genetika, Jurnal Teknik Informatika Kaputama (JTIK), vol.1,No.1, Jan 2017.

Saud,A,T., Nugraha,D,W., dan Dodu,A,Y,E., Sistem Penjadwalan Perkuliahan Menggunakan Algoritma Genetika (Studi Kasus Pada Jurusan Teknologi Informasi Fakultas Teknik Universitas Tadulako), J. Ilm. Mat.dan Terapan., vol.14, no. 2, pp. 242–255, 2017

Published

2023-10-30

How to Cite

[1]
R. Salman, Suprapto, and Irfandi, “Pengaruh Probabilitas Crossover Terhadap Kinerja Algoritma Genetika Dalam Optimasi Penjadwalan Matakuliah”, JTIF, vol. 11, no. 2, pp. 69–74, Oct. 2023.