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Seisarrina, Maulidya Larasaty
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Invigilator Examination Scheduling using Partial Random Injection and Adaptive Time Variant Genetic Algorithm Seisarrina, Maulidya Larasaty; Cholissodin, Imam; Nurwarsito, Heru
Journal of Information Technology and Computer Science Vol. 3 No. 2: November 2018
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (406.613 KB) | DOI: 10.25126/jitecs.20183250

Abstract

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.
Co-Authors Heru Nurwarsito Imam Cholissodin