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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Nurul Auliyah
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Optimasi Penjadwalan Moving Class Menggunakan Algoritma Genetika (Studi Kasus: SMA Negeri 1 Turatea) Nurul Auliyah; Rekyan Regasari Mardi Putri; Suprapto Suprapto
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 4 (2018): April 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Scheduling in the world of education is important due to the presence of a schedule so lessons can be run well. One example is the scheduling moving class, where it is a scheduling system for teaching and learning is characterized by students who came to guru processed. Process for preparing the schedule moving class is applied in SMA Negeri 1 Turatea that are still in the process of scheduling with the manual way which causes a lot of obstacles because of the scheduling process takes a long time and the results have not been definitely accurate. One of the optimization method that is suitable for this problem is through genetic algorithm approach. With the application of genetic algorithm to be performed the best solutions by using search operators (crossover (one-cut-point), mutations (reciprocal exchange mutation), selection (elitism) and evaluation). The best solution will be measured from the value of fitnessnya. In this study, there are 35 teachers who taught in 18 subjects in 12 classrooms. From these data will be made of chromosomes based on code teacher. From the test results that obtained the best results with the highest fitness value on the amount of population 100, crossover rate 0.4, mutation rate of 0.4, and the number of generations of 80. The end result of that is the best chromosome that produces the highest fitness value of scheduling. By applying genetic algorithms are expected to generate an optimized scheduling solution. Keywords: genetic algorithms, scheduling moving class.
Co-Authors Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Supraptoa Supraptoa