OPTIMASI PENJADWALAN KEGIATAN BELAJAR MENGAJAR MENGGUNAKAN ALGORITMA GENETIKA
(STUDI KASUS : SMKN 8 MALANG)
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Institusion
STIKI Malang
Author
GUSTI, DANI ARIANTO
Subject
Education
Datestamp
2019-08-16 06:56:16
Abstract :
Keywords : scheduling optimization, artificial intelligent, genetic algorithms. Artificial Intelligent an artificial intelligence engine that resembles the behavior performed by human beings, one of the methods contained in Artificial Intelligent is a method of genetic algorithms. Genetic algorithm is a search technique with a variety of data is done by generating a number of solutions or known as a population. It is also experienced by SMKN 8 Malang, where data subjects each semester is always diverse and generates quite a lot of data, taking into account the time and diverse class to establish a time schedule of teaching with the best combination. In using a genetic algorithm method must first obtain an initial population that its future will be calculated fitness value. If you already generated a fitness value, then do the formation of a new population or the new generation with the calculation of 20% elitism, 70% crossover (crossover), 10% mutation, the process will be conducted to find the fitness value of 1 or fitness value converges. If it had been obtained by the calculation, the time resource scheduling process has been completed.
Keywords : scheduling optimization, artificial intelligent, genetic algorithms. Artificial Intelligent an artificial intelligence engine that resembles the behavior performed by human beings, one of the methods contained in Artificial Intelligent is a method of genetic algorithms. Genetic algorithm is a search technique with a variety of data is done by generating a number of solutions or known as a population. It is also experienced by SMKN 8 Malang, where data subjects each semester is always diverse and generates quite a lot of data, taking into account the time and diverse class to establish a time schedule of teaching with the best combination. In using a genetic algorithm method must first obtain an initial population that its future will be calculated fitness value. If you already generated a fitness value, then do the formation of a new population or the new generation with the calculation of 20% elitism, 70% crossover (crossover), 10% mutation, the process will be conducted to find the fitness value of 1 or fitness value converges. If it had been obtained by the calculation, the time resource scheduling process has been completed.
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