@thesis{thesis,
		author={WASKITO  ADHI OKTAFIANTO},
		title ={PERBANDINGAN PERFORMANSI ALGORITMA GENETIKA DAN ALGORITMA KOLONI SEMUT PADA JADWAL PERJALANAN KERETA API                   JALUR CIREBON - JAKARTA},
		year={2019},
		url={https://repository.ittelkom-pwt.ac.id/5699/},
		abstract={ABSTRACT 
The problems that occur in Indonesian railways are delays in train travel times, time delays caused by factors operating additional trains, official trains, and natural factors . In this study, using two m etode namely genetic algorithms, this algorithm is a method of searching or metaheuristic based on the mechanism of natural selection and biological evolution. The second method is the ant colony algorithm which is the method for solve the problem by imitating the behavior of ants in their foraging. Both of these algorithms, including the metaheuristic algorithm, are algorithms that are suitable for solving scheduling problems. The algorithm metaheuristic is an algorithm that has a lot of ways to solve the problems to the boundaries of the optimal solution. In this study, researchers used eclipse software and used the JAVA programming language . Based on experiments conducted with the ant colony algorithm using parameters α = 1, β = 1, ρ = 0.9 and the use of the number of ants (m) = 1 000 is the optimal number of ants . The use of the ant colony algorithm produces an optimal solution in the third iteration with a stop station sequence namely, Pasar Senen, Jatinegara, and Cirebon. The genetic algorithm produces an optimal solution in the first iteration in the form of a train sequence as follows: Argo Anggrek, Sawunggalih, Cirebon Express, Gumarang, Bogowonto, Tegal Bahari, Gajahwong, Argo Lawu, Purwojaya, Jakatingkir, Bima, Gayabaru, Argo Dwipangga, Bengawan, Krakatau , Tawang Jaya, Brantas, Fajar Utama Yk, Taksaka, Cirebon Express, Argo Jati, Bangunkarta, Tegal Ekspres. The ant colony algorithm computation time is 00.04.33 seconds and the genetic algorithm requires computing time 00.05.18 seconds.   Keywords : Algorithm, Genetic Algorithm, Ant Colony Algorithm,                      Train , Scheduling.}
		}