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Jurnal SINTA 2

Penjadwalan Kapal Penyeberangan Menggunakan Algoritma Genetika

Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 3 No. 1 (2016) · pp. 43–50

DOI
10.25126/jtiik.201631169
ISSN
2528-6579, 2355-7699
Published
2016-03-17
Publisher
Fakultas Ilmu Komputer, Universitas Brawijaya
Language
en

Abstract

AbstrakPenyusunan penjadwalan kapal penyeberangan di Pelabuhan Ketapang – Gilimanuk sangatlah penting agar para penumpang pengguna jasa kapal laut mendapatkan pelayanan yang maksimal. Karena pembuatan penjadwalan masih dibuat secara manual sehingga memungkinkan ada nama kapal yang sama dalam sehari yang beroperasi dan tidak adanya keadilan pada pembagian porsi masing-masing kapal. Untuk mengatasi permasalahan tersebut diperlukan suatu sistem komputerisasi penjadwalan kapal guna mempercepat pengaturan jadwal pemberangkatan kapal penyeberangan Ketapang – Gilimanuk. Penerapan metode algoritma genetika dalam permasalahan penjadwalan kapal mampu menghasilkan solusi yang baik dengan menggunakan representasi kromosom permutasi bilangan integer, metode crossover menggunakan one cut-point crossover, mutasi menggunakan reciprocal exchange mutation, dan seleksi menggunakan elitism selection. Dari pengujian parameter didapat hasil yaitu antara lain ukuran populasi sebesar 180, banyaknya generasi 200, serta kombinasi crossover rate=0,6 dan mutation rate=0,4.Kata kunci: kapal, penjadwalan, algoritma genetika.AbstractArrangement schedule for dispatching ships in Ketapang – Gilimanuk Harbor is strongly important to make all the passengers get the best service. As the schedule arrangement is made manually, it is possible that there are ships with same name work in a day; another problem is there is no equality in distributing the portion for each ships. To solve that kind of problems, dispatching schedule using computerization is needed in order to make the dispatching schedule of the ships faster in Ketapang – Gilimanuk Harbor. The use of genetic algorithm method in dispatching schedule of ships produces a good solution with using representative of numeral integer chromosome permutation, crossover method using one cut-point crossover, mutation using reciprocal exchange mutation, and selection using elitism selection. From parameter test’s result, there are some outcomes such as 180 population size, 200 the amount of generation, and also the combination of crossover rate=0,6 and mutation rate=0,4.Keywords: ship, schedule, genetic algorithm.

Collaboration network of the authors

25 authors · 35 co-authorship links

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  • University of Brawijaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
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Co-authorOrganisationJoint articles
Asyrofa RahmiUniversity of Brawijaya2
Yusuf Priyo AnggodoUniversity of Brawijaya2
Syaiful AnamUniversity of Brawijaya2
Firnanda Al Islama Achyunda PutraUniversity of Brawijaya1
Fitri UtaminingrumUniversity of Brawijaya1
Ida WahyuniUniversity of Brawijaya1
Ervin YohannesUniversity of Brawijaya1
Hilman Nuril HadiUniversity of Brawijaya1
Rizal Setya PerdanaUniversity of Brawijaya1
Ghenniy RachmansyahUniversity of Brawijaya1
Vitara Nindya Putri HasanUniversity of Brawijaya1
Mohammad Zoqi SarwaniUniversity of Brawijaya1
Luthfi HidayatUniversity of Brawijaya1
Rifki Setya ArmandaUniversity of Brawijaya1
Garsinia Ely RianiUniversity of Brawijaya1
Fita Lathifatul Mu’asyarohAffiliation unknown1
Agus Wahyu WidodoUniversity of Brawijaya1
Diva KurnianingtyasUniversity of Brawijaya1
Muchammad Chandra Cahyo UtomoUniversity of Brawijaya1
Adyan Nur AlfiyatinUniversity of Brawijaya1
Candra Fajri AnandaUniversity of Brawijaya1
Farhanna Mar’iUniversity of Brawijaya1
Cleoputri YusainyUniversity of Brawijaya1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://jtiik.ub.ac.id/index.php/jtiik/en/oai, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="id">Penjadwalan Kapal Penyeberangan Menggunakan Algoritma Genetika</dc:title>
	<dc:creator xml:lang="en">Febriyana, Ria</dc:creator>
	<dc:creator xml:lang="en">Mahmudy, Wayan Firdaus</dc:creator>
	<dc:description xml:lang="id">AbstrakPenyusunan penjadwalan kapal penyeberangan di Pelabuhan Ketapang – Gilimanuk  sangatlah penting agar para penumpang pengguna jasa kapal laut mendapatkan pelayanan yang maksimal. Karena pembuatan penjadwalan masih dibuat secara manual sehingga memungkinkan ada nama kapal yang sama dalam sehari yang beroperasi dan tidak adanya keadilan pada pembagian porsi masing-masing kapal. Untuk mengatasi permasalahan tersebut diperlukan suatu sistem komputerisasi penjadwalan kapal guna mempercepat pengaturan jadwal pemberangkatan kapal penyeberangan Ketapang – Gilimanuk. Penerapan metode algoritma genetika dalam permasalahan penjadwalan kapal mampu menghasilkan solusi yang baik dengan menggunakan representasi kromosom permutasi bilangan integer, metode crossover menggunakan one cut-point crossover, mutasi menggunakan reciprocal exchange mutation, dan seleksi menggunakan elitism selection. Dari pengujian parameter didapat hasil yaitu antara lain ukuran populasi sebesar 180, banyaknya generasi 200, serta kombinasi crossover rate=0,6 dan mutation rate=0,4.Kata kunci: kapal, penjadwalan, algoritma genetika.AbstractArrangement schedule for dispatching ships in Ketapang – Gilimanuk Harbor is strongly important to make all the passengers get the best service. As the schedule arrangement is made manually, it is possible that there are ships with same name work in a day; another problem is there is no equality in distributing the portion for each ships. To solve that kind of problems, dispatching schedule using computerization is needed in order to make the dispatching schedule of the ships faster in Ketapang – Gilimanuk Harbor. The use of genetic algorithm method in dispatching schedule of ships produces a good solution with using representative of numeral integer chromosome permutation, crossover method using one cut-point crossover, mutation using reciprocal exchange mutation, and selection using elitism selection. From parameter test’s result, there are some outcomes such as 180 population size, 200 the amount of generation, and also the combination of crossover rate=0,6 and mutation rate=0,4.Keywords: ship, schedule, genetic algorithm.</dc:description>
	<dc:publisher xml:lang="en">Fakultas Ilmu Komputer, Universitas Brawijaya</dc:publisher>
	<dc:date>2016-03-17</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:identifier>https://jtiik.ub.ac.id/index.php/jtiik/article/view/169</dc:identifier>
	<dc:identifier>10.25126/jtiik.201631169</dc:identifier>
	<dc:source xml:lang="id">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 3 No 1: Maret 2016; 43-50</dc:source>
	<dc:source xml:lang="en">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol. 3 No. 1: Maret 2016; 43-50</dc:source>
	<dc:source>2528-6579</dc:source>
	<dc:source>2355-7699</dc:source>
	<dc:source>10.25126/jtiik.201631</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://jtiik.ub.ac.id/index.php/jtiik/article/view/169/pdf</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2016 Jurnal Teknologi Informasi dan Ilmu Komputer</dc:rights>
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