Application of Genetic Algorithm with Roulette Wheel Selection Method for International Certification Scheduling Optimization at ITCC ITPLN

##plugins.themes.academic_pro.article.sidebar##

Published Dec 31, 2022
https://doi.org/10.37339/e-komtek.v6i2.2325
Download
PDF
Statistic

Downloads

Download data is not yet available.
Vol. 6 No. 2 (2022)

##plugins.themes.academic_pro.article.main##

Hendra Jatnika
Institute Teknologi PLN
Rakhmadi Irfansyah Putra
Institut Teknologi PLN
Muhammad Zaid Al-Khair
Institut Teknologi PLN
Arfani Lovina Br. Stendel
Institut Teknologi PLN
Mohamad Tanwirul Akbar
Institut Teknologi PLN

Abstract

Information Technology Certification Center (ITCC) is a division within the PLN Institute of Technology, authorized to conduct international training and certification. During the scheduling process, it was identified that the current schedule creation was not optimal. Therefore, an optimization algorithm was required to address the scheduling issues. The chosen algorithm was the genetic algorithm with roulette wheel selection, applied to a prototype, followed by black-box testing of the prototype and ANOVA for the algorithm’s. The findings of the research suggest that the genetic algorithm with roulette wheel selection can generate an optimal schedule for international certification activities while adhering to established rules

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Jatnika, H., Rakhmadi Irfansyah Putra, Muhammad Zaid Al-Khair, Arfani Lovina Br. Stendel, & Mohamad Tanwirul Akbar. (2022). Application of Genetic Algorithm with Roulette Wheel Selection Method for International Certification Scheduling Optimization at ITCC ITPLN. Jurnal E-Komtek (Elektro-Komputer-Teknik), 6(2), 360-369. https://doi.org/10.37339/e-komtek.v6i2.2325

References

[1] M. Farid Rifai, H. Jatnika, B. Valentino, and S. Tinggi Teknik PLN, “Penerapan Algoritma Naïve Bayes Pada Sistem Prediksi Tingkat Kelulusan Peserta Sertifikasi Microsoft Office Specialist (MOS),” vol. 12, no. 2, 2019.
[2] T. Alam, S. Qamar, A. Dixit, and M. Benaida, “Genetic Algorithm: Reviews, Implementations, and Applications,” Aug. 03, 2020. doi: 10.36227/techrxiv.12657173.
[3] R. T. Subagio, Kusnadi, T. E. Putri, P. Sokibi, S. Z. Harahap, and Darmansah, “Application of Genetic Algorithm to Optimize Lecture Scheduling Based on Lecturers’ Teaching Day Willingness,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Mar. 2021. doi: 10.1088/1742-6596/1842/1/012007.
[4] F. Mone and J. E. Simarmata, “APLIKASI ALGORITMA GENETIKA DALAM PENJADWALAN MATA KULIAH,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 15, no. 4, pp. 615–628, Dec. 2021, doi: 10.30598/barekengvol15iss4pp615-628.
[5] A. P. Engelbrecht, “Fitness function evaluations: A fair stopping condition?,” in 2014 IEEE Symposium on Swarm Intelligence, 2014, pp. 1–8. doi: 10.1109/SIS.2014.7011793.
[6] Y. Pratama, “Optimalisasi Penjadwalan Karyawan Paruh Waktu Berdasarkan Nilai Fitness Terbaik Menggunakan Algoritma Genetika,” Jurnal Nasional Informatika (Junif), vol. 2, no. 2, pp. 114–142, 2021.
[7] W. Supriana et al., “IMPLEMENTASI DUA MODEL CROSSOVER PADA ALGORITMA GENETIKA UNTUK OPTIMASI PENGGUNAAN RUANG PERKULIAHAN,” Online, 2021, Available: http://jurnal.stiki-indonesia.ac.id/index.php/jurnalresistor
[8] A. Hassanat, K. Almohammadi, E. Alkafaween, E. Abunawas, A. Hammouri, and V. B. S. Prasath, “Choosing mutation and crossover ratios for genetic algorithms-a review with a new dynamic approach,” Information (Switzerland), vol. 10, no. 12, Dec. 2019, doi: 10.3390/info10120390.
[9] R. Bierig, S. Brown, E. Galván, and J. Timoney, Essentials of Software Testing. Cambridge: Cambridge University Press, 2021. doi: DOI: 10.1017/9781108974073.
[10] G. Box, S. Hunter, and W. Hunter, Statistics for experimenters. Design, innovation, and discovery. 2nd ed, vol. 2. 2005.

Most read articles by the same author(s)