Gusti Eka Yuliastuti, Gusti Eka
Brawijaya University

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IMPLEMENTATION OF EVOLUTION STRATEGIES (ES) ALGORITHM TO OPTIMIZATION LOVEBIRD FEED COMPOSITION Rizki, Agung Mustika; Mahmudy, Wayan Firdaus; Yuliastuti, Gusti Eka
Scientific Journal of Informatics Vol 4, No 1 (2017): May 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i1.9003

Abstract

Lovebird current society, especially popular among bird lovers. Some people began to try to develop the cultivation of these birds. In the cultivation process to consider the composition of feed to produce a quality bird. Determining the feed is not easy because it must consider the cost and need for vitamin Lovebird. This problem can be solved by the algorithm Evolution Strategies (ES). Based on test results obtained optimal fitness value of 0.3125 using a population size of 100 and optimal fitness value of 0.3267 in the generation of 1400.
Implementation of Evolution Strategies (ES) Algorithm to Optimization Lovebird Feed Composition Rizki, Agung Mustika; Mahmudy, Wayan Firdaus; Yuliastuti, Gusti Eka
Scientific Journal of Informatics Vol 4, No 1 (2017): May 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i1.9003

Abstract

Lovebird current society, especially popular among bird lovers. Some people began to try to develop the cultivation of these birds. In the cultivation process to consider the composition of feed to produce a quality bird. Determining the feed is not easy because it must consider the cost and need for vitamin Lovebird. This problem can be solved by the algorithm Evolution Strategies (ES). Based on test results obtained optimal fitness value of 0.3125 using a population size of 100 and optimal fitness value of 0.3267 in the generation of 1400. 
Implementation of Genetic Algorithm to Solve Travelling Salesman Problem with Time Window (TSP-TW) for Scheduling Tourist Destinations in Malang City Yuliastuti, Gusti Eka; Mahmudy, Wayan Firdaus; Rizki, Agung Mustika
Journal of Information Technology and Computer Science Vol 2, No 1: June 2017
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (617.611 KB) | DOI: 10.25126/jitecs.20172122

Abstract

In doing travel to some destinantions, tourist certainly want to be able to visit many destinations with the optimal scheduling so that necessary in finding the best route and not wasting lots of time travel. Several studies have addressed the problem but does not consider other factor which is very important that is the operating hours of each destination or hereinafter referred as the time window. Genetic algorithm proved able to resolve this travelling salesman problem with time window constraints. Based on test results obtained solutions with the fitness value of 0,9856 at the time of generation of 800 and the other test result obtained solution with the fitness value of 0,9621 at the time of the combination CR=0,7 MR=0,3.
OPTIMASI MULTI TRAVELLING SALESMAN PROBLEM (M-TSP) UNTUK DISTRIBUSI PRODUK PADA HOME INDUSTRI TEKSTIL DENGAN ALGORITMA GENETIKA Rizki, Agung Mustika; Mahmudy, Wayan Firdaus; Yuliastuti, Gusti Eka
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 4, No 2 (2017)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v4i2.86

Abstract

In the field of textile industry, the distribution process is an important factor that can affect the cost of production. For that we need optimization on the distribution process to be more efficient. This problem is a model in the Multi Travelling Salesman Problem (M-TSP). Much research has been done to complete the M-TSP model. Among several methods that have been applied by other researchers, genetic algorithms are a workable method for solving this model problem. In this article the authors chose the genetic algorithm is expected to produce an optimal value with an efficient time. Based on the results of testing and analysis, obtained the optimal population amount of 120. For the optimal generation amount is 800. The test results related to the number of population and the number of generations are used as input to test the combination of CR and MR, obtained the optimal combination of CR = 0 , 4 and MR = 0.6 with a fitness value of 2.9964.Keywords: Textile Industry, Multi Travelling Salesman Problem (M-TSP), Genetic AlgorithmPada bidang industri tekstil, proses distribusi merupakan satu faktor penting yang dapat berpengaruh terhadap biaya produksi. Untuk itu diperlukan optimasi pada proses distribusi agar menjadi lebih efisien. Masalah seperti ini merupakam model dalam Multi Travelling Salesman Problem (M-TSP). Banyak penelitian telah dilakukan untuk menyelesaikan model M-TSP. Diantara beberapa metode yang telah diterapkan oleh peneiti lain, algoritma genetika adalah metode yang bisa diterapkan untuk penyelesaian permasalahan model ini. Dalam artikel ini penulis memilih algoritma genetika diharapkan dapat menghasilkan nilai yang optimal dengan waktu yang efisien. Berdasarkan hasil pengujian dan analisis, didapatkan jumlah populasi yang optimal sebesar 120. Untuk jumlah generasi yang optimal adalah sebesar 800. Hasil pengujian terkait jumlah populasi dan jumlah generasi tersebut dijadikan masukan untuk melakukan pengujian kombinasi  CR dan MR, didapatkan kombinasi yang optimal yakni CR=0,4 dan MR=0,6 dengan nilai fitness sebesar 2,9964.Kata kunci: Industri Tekstil, Distribusi, Multi Travelling Salesman Problem (M-TSP), Algoritma Genetika
Penanganan Fuzzy Time Window pada Travelling Salesman Problem (TSP) dengan Penerapan Algoritma Genetika Yuliastuti, Gusti Eka; Mahmudy, Wayan Firdaus; Rizki, Agung Mustika
MATICS Vol 9, No 1 (2017): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (725.243 KB) | DOI: 10.18860/mat.v9i1.4072

Abstract

The route of the travel tour packages offered by travel agents is not considered optimum, so the level of satisfaction the tourist is not maximal. Selection of the route of the travel packages included in the traveling salesman problem (TSP). The problem that occurs is uncertain tourists visiting destinations at the best destinations timing hereinafter be referred to as the fuzzy time window problem. Therefore, the authors apply the genetic algorithm to solve the problem. Based on test results obtained optimum solution with the fitness value of 1.3291, a population size of 100, the number of generations of 1000, a combination of CR=0,4 and MR=0.6.
OPTIMASI MULTI TRAVELLING SALESMAN PROBLEM (M-TSP) UNTUK DISTRIBUSI PRODUK PADA HOME INDUSTRI TEKSTIL DENGAN ALGORITMA GENETIKA Rizki, Agung Mustika; Mahmudy, Wayan Firdaus; Yuliastuti, Gusti Eka
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 4, No 2 (2017)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v4i2.86

Abstract

In the field of textile industry, the distribution process is an important factor that can affect the cost of production. For that we need optimization on the distribution process to be more efficient. This problem is a model in the Multi Travelling Salesman Problem (M-TSP). Much research has been done to complete the M-TSP model. Among several methods that have been applied by other researchers, genetic algorithms are a workable method for solving this model problem. In this article the authors chose the genetic algorithm is expected to produce an optimal value with an efficient time. Based on the results of testing and analysis, obtained the optimal population amount of 120. For the optimal generation amount is 800. The test results related to the number of population and the number of generations are used as input to test the combination of CR and MR, obtained the optimal combination of CR = 0 , 4 and MR = 0.6 with a fitness value of 2.9964.Keywords: Textile Industry, Multi Travelling Salesman Problem (M-TSP), Genetic AlgorithmPada bidang industri tekstil, proses distribusi merupakan satu faktor penting yang dapat berpengaruh terhadap biaya produksi. Untuk itu diperlukan optimasi pada proses distribusi agar menjadi lebih efisien. Masalah seperti ini merupakam model dalam Multi Travelling Salesman Problem (M-TSP). Banyak penelitian telah dilakukan untuk menyelesaikan model M-TSP. Diantara beberapa metode yang telah diterapkan oleh peneiti lain, algoritma genetika adalah metode yang bisa diterapkan untuk penyelesaian permasalahan model ini. Dalam artikel ini penulis memilih algoritma genetika diharapkan dapat menghasilkan nilai yang optimal dengan waktu yang efisien. Berdasarkan hasil pengujian dan analisis, didapatkan jumlah populasi yang optimal sebesar 120. Untuk jumlah generasi yang optimal adalah sebesar 800. Hasil pengujian terkait jumlah populasi dan jumlah generasi tersebut dijadikan masukan untuk melakukan pengujian kombinasi  CR dan MR, didapatkan kombinasi yang optimal yakni CR=0,4 dan MR=0,6 dengan nilai fitness sebesar 2,9964.Kata kunci: Industri Tekstil, Distribusi, Multi Travelling Salesman Problem (M-TSP), Algoritma Genetika
Determining Optimum Production Quantity on Multi-Product Home Textile Industry by Simulated Annealing Yuliastuti, Gusti Eka; Rizki, Agung Mustika; Mahmudy, Wayan Firdaus; Tama, Ishardita Pambudi
Journal of Information Technology and Computer Science Vol 3, No 2: November 2018
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (733.457 KB) | DOI: 10.25126/jitecs.20183264

Abstract

Production planning is a plan aimed at controlling the quantity of products produced. Production planning is very important to be carried out by the company so that the production will always be controlled. It is very difficult to plan production with a variety of product variations because each product certainly has a different demand value from its customers. This has become a complex problem so an algorithm is needed to overcome these problems. Simulated Annealing can produce optimal solutions more effectively and efficiently. Production costs generated by applying Simulated Annealing are Rp. 6,902,406,000, - for all types of products, which is better than existing condition.
Variable Neighborhoods Search for Multi-Site Production Planning Rizki, Agung Mustika; Yuliastuti, Gusti Eka; Mahmudy, Wayan Firdaus; Tama, Ishardita Pambudi
Journal of Information Technology and Computer Science Vol 3, No 2: November 2018
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (205.791 KB) | DOI: 10.25126/jitecs.20183265

Abstract

In the home textile industry, production planning needs to be done so that the production costs incurred by the company can be well controlled. Production planning is a problem that cannot be solved in a short time. Problems are more complex if the company has several production branches in other cities, with rules and standards that are certainly very different from one city to another. Based on this background, an algorithm is needed that can solve production planning problems for companies with many production branches in order to obtain optimal solutions. VNS is applied by the author and produces an optimal and efficient solution because the time needed is relatively short compared to the planning carried out previously by the company.
Model Fuzzy Gaussian Untuk Optimasi Akurasi Estimasi Waktu Proyek Perangkat Lunak Putri, Rahmi Rizkiana; Sodik, Anwar; yuliastuti, Gusti Eka; Rachman, Andy
Prosiding Seminar Nasional Sains dan Teknologi Terapan 2019: Menuju Penerapan Teknologi Terbarukan pada Industri 4.0: Perubahan Industri dan Transformasi P
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (348.165 KB)

Abstract

Perkiraan waktu yang akurat akan memberikan dampak yang baik pada manajemen proyekperangkat lunak. Jika perkiraan waktu kurang akurat maka akan berpengaruh pada kualitasmanajemen proyek perangkat lunak termasuk proses selama pengembangan proyek yang kurangefektif dan efisien. Pada tahun 2000 Barry Boehm memperkenalkan adanya penambahan costdriver yang baru pada COCOMO II, yang diharapkan akan dapat memberikan hasil akurasi yanglebih baik. Sedangkan dalam penelitian ini tidak hanya menggunakan akurasi waktu berdasarkanCOCOMO II saja, tetapi juga menggunakan metode fuzzy Gaussian dan perubahan nilaiparameter untuk menghitung perkiraan waktu. Metode fuzzy gaussian yang digunakan dalampenelitian ini dimaksudkan memberikan hasil akurasi yang lebih baik daripada penelitianlainnya. Selain itu juga perubahan nilai parameter C dan D pada COCOMO II dilakukan denganmenurunkan nilainya sebanyak 0,0001 dari nilai awal, karena pengurangan nilai sebesar 0,0001adalah nilai optimal. Berdasarkan hasil uji coba dan implementasi yang diusulkan dalampenelitian ini maka didapatkan kesalahan akurasi sebesar 83,83%, yang artinya bahwa akurasiperkiraan waktu proyek perangkat lunak meningkat sebanyak 2,89%.