Invigilator Examination Scheduling using Partial Random Injection and Adaptive Time Variant Genetic Algorithm
DOI:
https://doi.org/10.25126/jitecs.20183250Abstract
Abstract. Examination for every semester is a routine activity for faculties to do. Academic division of faculty responsible to make the schedule for every subject that is going to be tested, and prepare rooms for the test. Meanwhile, coordinators of invigilator committee responsible to make the schedule in FILKOM UB. This research focuses on scheduling the invigilator’s schedule in FILKOM UB. Scheduling with conventional method or manual takes much time because it needs to consider many rules on scheduling it. That is the reason why we need a system to schedule it. The purpose of making this system is to help the committee to schedule their invigilator’s time line. This research offers a concept of solution from using genetic algorithm. Genetic algorithm is an algorithm to find the optimum solution. The system of scheduling that use this genetic algorithm method can produce invigilator’s schedule that is having the least troubles on the arrangement. The data that is used in this research is the final test’s schedule of the odd semester in 2015/2016, lecturer and the employee’s data of FILKOM UB. The optimal genetic parameter that is obtained from the test consists of 900 population, 3000 generations, and a combination of crossover rate and mutation rate value which are 0,4 and 0,6. The system that is built in making this invigilator’s schedule is close to the optimum point with 0,877 fitness value.
Keywords: scheduling, invigilator, partial random injection, adaptive time variant genetic algorithm.
References
Collins Concise Dictionary. (2018). “Definition of 'schedule'â€. https://www.collinsdictionary.com/dictionary/english/schedule.
N. K. Mawaddah and W. F. Mahmudy. (2006). "Optimasi Penjadwalan Ujian Menggunakan Algoritma Genetika," Kursor, vol. 2, no. 2, pp. 1-8.
W. A. Puspaningrum, A. Djunaidy and R. A. Vinarti. (2013)."Penjadwalan Mata Kuliah Menggunakan Algoritma Genetika di Jurusan Sistem Informasi ITS," JURNAL TEKNIK POMITS, vol. 2, no. 1, pp. 127-131.
S. K. Jha. (2014). "Exam Timetabling Problem Using Genetic Algorithm," IJRET : International Journal of Research in Engineering and Technology, vol. 03, no. 05, pp. 649-654.
Jain, A., Jain, S. & Chande, P., (2010). “Formulation of Genetic Algorithm to Generate Good Quality Course Timetableâ€. International Journal of Innovation and Technology, 1(3), pp. 248-251.
W. F. Mahmudy. (2013). Algoritma Evolusi, Malang: Universitas Brawijaya.
Pusat Bahasa Departemen Pendidikan Nasional Republik Indonesia. (2008). "KBBI (Kamus Besar Bahasa Indonesia) Daring". [Online]. Available: http://badanbahasa.kemdikbud.go.id/kbbi/. [Accessed 24 Desember 2015].
A. Qoiriah. (2014). "Penjadwalan Ujian Akhir Semester dengan Algoritma Genetika (Studi Kasus Jurusan Teknik Informatika UNESA)," Jurnal Manajemen Informatika, vol. 03, no. 02, pp. 33-38.
R. Perzina and J. Ramik. (2013). "Self-Learning Genetic Algorithm for a Timetabling Problem with Fuzzy Constraints," International Journal of Innovative Computing, Information and Control, vol. 9, no. 11, pp. 4565-4582.
T. Sutojo, E. Mulyanto and V. Suhartono. (2011). Kecerdasan Buatan, 1st ed., Yogyakarta: ANDI.
W. F. Mahmudy. (2013). Algoritma Evolusi, Malang: Program Teknologi Informasi dan Ilmu Komputer (PTIIK) Universitas Brawijaya.
W. F. Mahmudy, R. M. Marian and L. H. S. Luong. (2012). "Solving Part Type Selection and Loading Problem Flexible Manufacturing System Using Real Code Genetic Algorithms - Part I : Modeling," World Academy of Science, Engineering and Technology, vol. 69, pp. 773-779.
W. F. Mahmudy. (2014). "Optimasi Penjadwalan Two-Stage Assembly Flowshop Menggunakan Algoritma Genetika yang Dimodifikasi," STMIK Dipanegara Makassar.
W. F. Mahmudy, R. M. Marian and L. H. S. Luong. (2014). "Hybrid Genetic Algorithms for Part Type Selection and Machine Loading Problems with Alternative Production Plans in Flexible Manufacturing System," ECTI TRANSACTIONS ON COMPUTER AND INFORMATION TECHNOLOGY, vol. 8, no. 1, pp. 81-93.
Cholissodin I., Khusna R.A., Wihandika R.C., (2016). Optimization Of Equitable Distributions Of Teachers Based On Geographic Location Using General Series Time Variant PSO. 2nd International Symposium on Geoinformatics (ISyG).
H. Y. B. W. G. e. a. Chen. (2011). "A Novel Bankruptcy Prediction Model Based on an Adaptive Fuzzy K-Nearest Neighbor Method," College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Downloads
Published
How to Cite
Issue
Section
License
 Creative Common Attribution-ShareAlike 3.0 International (CC BY-SA 3.0)
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).