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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Fisika FLUX Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Nasional Komputasi dan Teknologi Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) Jurnal Mantik Technologia: Jurnal Ilmiah G-Tech : Jurnal Teknologi Terapan Jurnal Sistem Komputer dan Informatika (JSON) Journal of Computer Networks, Architecture and High Performance Computing IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) Sistem Pendukung Keputusan dengan Aplikasi International Conference on Sciences Development and Technology Innovative: Journal Of Social Science Research Jurnal Pengabdian Masyarakat (JAPAMAS)
Armansyah Armansyah
Computer Science Study Program, Universitas Islam Negeri Sumatera Utara
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Journal : J-SAKTI (Jurnal Sains Komputer dan Informatika)

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Algoritma Genetika Untuk Perancangan Aplikasi Penjadwalan Mata Pelajaran Mhd Furqan; A Armansyah; Rizki Ananda
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.476

Abstract

The schedule is one of the important activities to help the teaching and learning process in schools, the schedule planning process is still done manually so there are still conflicting schedules between classes. because of the large number of classes and a lot of time ordering a certain day so that sometimes up to 3 times the revision schedule, and the implementation of learning becomes late. To overcome this, one of the appropriate ones is used so that the scheduling process can run well. One of the algorithms used for scheduling the genetic algorithm is one of the improvement algorithms that can be used in various types of problems such as scheduling, the schedule will be tested on classes that clash, which are selected randomly. random or random in each class, the test will be asked to input or fill in the crossover probability number = 0.70 and mutation probability = 0.40 and the number of generations = 1000, then executed. After that it will occur and program execution in the form of selection, crossover, and mutation that will occur in the background of the screen, so that the results of applying 17 classes and 1 laboratory room using the genetic algorithm method can be used to compile a list of lessons.
Co-Authors Abdul Halim Hasugian Adnan Buyung Nasution Aidil Halim Lubis Anggara AlFaridzi Tamba Cici Armayani Devi Apriani Dimas Kurniawan Dita Desliani Fitri Handayani Siregar Guntur Syahputra Heri Santoso Ilka Zufria Ilka Zurfia Lely Sahrani Lisma Autia Masthura, Masthura Maulana Habib Mhd Furqan Moch Iswan Perangin-angin Muhammad Dedi Irawan Muhammad Ihsan Nugraha Muhammad Ikhsan Muhammad Ikhsan Muhammad Siddik Hasibuan Nur Adillah Nurhasanah Nurhasanah Razzaq H. Nur Wijaya Ridho Wahyudi Pulungan Rizki Ananda Siti Khalizah Suhardi Suhardi Toibatur Rahma Matondang Yustria Handika Siregar Yusuf Ramadhan Nasution Yusuf Ramadhan Nasution Zepi Yarti Simpa