Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Vol 2 No 12 (2018): Desember 2018

Optimasi Jumlah Produksi Metal Roof Menggunakan Algoritme Genetika (Studi Kasus: PT. Comtech Metalindo Terpadu)

Febri Ramadhani (Fakultas Ilmu Komputer, Universitas Brawijaya)
Budi Darma Setiawan (Fakultas Ilmu Komputer, Universitas Brawijaya)
Candra Dewi (Fakultas Ilmu Komputer, Universitas Brawijaya)



Article Info

Publish Date
21 Aug 2018

Abstract

Manufacturing industry in Indonesia continues to increase, especially in the molding industry. PT. Comtech Metalindo Terpadu is one of molded goods industry company located in Pekanbaru City. The company is an industrial company that produces metal roof. The metal roof is printed using Prepainted Galvalum (PPGL) raw material or more commonly referred to as coil, the raw material is imported from other countries. The ordering of raw materials takes 2 months until the raw material arrives. There are 3 types of metal roof products sold are spandek, zigzag and zigzag charcoal. All three items have the composition of raw materials, as well as providing benefits that are different. Setting the right amount of production is the thing that must be taken into account by the owner of the company in order to obtain optimal benefits. Based on these problems to get the right amount of production on the use of the remaining raw materials, it is necessary to optimize the number of metal roof production based on the existing demand and the remaining stock of raw materials. Optimization is used to regulate the amount of existing production so that the remaining raw materials can be used optimally and provide optimal benefits as well. Genetic Algorithms are used to optimize the 3 genes that represent each product. The value of the gene represents the original value of the existing query with the integer type. In the reproduction, the crossover method that used is the extended intermediate crossover. Whereas the mutation is performed by reviving the gene values of a randomly selected chromosome. For the selection process used elitism selection to screen the best individual and used random injection method to prevent early convergence. Based on testing of parameters that have been done with 5 times each parameter is got the best population size 90, the combination of cr = 0.1 and mr = 0.9, and total of best generation equal to 225 with average fitness value 7.12126.

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Journal Info

Abbrev

j-ptiik

Publisher

Subject

Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Description

Jurnal Pengembangan Teknlogi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya merupakan jurnal keilmuan dibidang komputer yang memuat tulisan ilmiah hasil dari penelitian mahasiswa-mahasiswa Fakultas Ilmu Komputer Universitas Brawijaya. Jurnal ini diharapkan dapat mengembangkan penelitian ...