Agung Mustika Rizki, Agung Mustika
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. 
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.
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.
Implementasi Simulated Annealing untuk Penentuan Rute pada Jaringan Yuliastuti, Gusti Eka; Prabiantissa, Citra Nurina; Rizki, Agung Mustika
MATICS Vol 13, No 2 (2021): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v13i2.12969

Abstract

Abstract—Recently computer networks are increasingly complex. It needs to be a supporting device for network management such as a router. Router is a device that plays an important role in the routing process. In a router stored information in the form of routing paths, where the information includes data and which routers will be passed. In certain cases, a router can have more than one gateway. This is because the router needs to send data packets to several networks that have different segments. Such cases can be handled by using the appropriate routing path selection rules. The routing problem can be regarded as a traveling salesman problem (TSP), where a mechanism is needed to determine the shortest route to be traversed. The author implements the Simulated Annealing Algorithm because it can produce an optimal solution with light computing, so that the routing process can be more effective and efficient. Index Terms—Computer Network, Routing, Simulated Annealing, Travelling Salesman Problem Abstrak–-Jaringan komputer saat ini semakin kompleks. Perlu adanya suatu perangkat pendukung untuk manajemen jaringan seperti router. Router merupakan perangkat yang berperan penting dalam proses routing. Pada sebuah router tersimpan informasi berupa jalur routing, dimana informasi tersebut mencakup data dan router mana saja yang akan dilewati. Pada kasus tertentu, router dapat memiliki lebih dari satu gateway. Hal ini disebabkan karena router perlu mengirimkan paket data ke beberapa jaringan yang memiliki segmen berbeda. Kasus tersebut dapat ditangani dengan menggunakan aturan pemilihan jalur routing yang tepat. Permasalahan routing dapat dikatakan sebagai suatu permasalahan travelling salesman problem (TSP), dimana diperlukan suatu mekanisme dalam menentukan rute terpendek untuk dilalui. Penulis mengimplementasikan Algoritma Simulated Annealing karena dapat menghasilkan solusi yang optimal dengan komputasi ringan, sehingga proses routing dapat lebih efektif dan efisien. Kata Kunci—Jaringan Komputer, Penentuan Rute, Travelling Salesman Problem, Algoritma Simulated Annealing 
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.