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All Journal International Journal of Electrical and Computer Engineering Perfecting a Video Game with Game Metrics Journal of Information Technology and Computer Science
Gusti Ahmad Fanshuri Alfarisy
Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Good Parameters for PSO in Optimizing Laying Hen Diet Gusti Ahmad Fanshuri Alfarisy; Wayan Firdaus Mahmudy; Muhammad Halim Natsir
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 4: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (688.349 KB) | DOI: 10.11591/ijece.v8i4.pp2419-2432

Abstract

Manual formulation of poultry diet by taking into account the fulfillment of all nutrients requirement with least cost is a difficult task. Particle Swarm Optimization (PSO) shows promising technique to solve this problem. However, there is a lack of studying a good parameter for PSO to solve feed formulation problem since PSO is sensitive to control parameter which depends on the problem. Therefore, this study investigates good swarm size, total iterations, acceleration coefficients, and inertia weight to produce a better formula. PSO with proposed good parameters is compared with other parameters. The obtained result shows that PSO with good parameters choice produces the highest fitness. Furthermore, good parameters of PSO can be used as a reference for a software developer and for further research to optimize poultry diet using PSO.
Optimizing Laying Hen Diet using Multi-Swarm Particle Swarm Optimization Gusti Ahmad Fanshuri Alfarisy; Wayan Firdaus Mahmudy; Muhammad Halim Natsir
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.7765

Abstract

Formulating animal diet by accounting fluctuating cost, nutrient requirement, balanced amino acids, and maximum composition simultaneously is a difficult and complex task. Manual formulation and Linear Programming encounter difficulty to solve this problem. Furthermore, the complexity of laying hen diet problem is change through ingredient choices. Thus, an advanced technique to enhance formula quality is a vital necessity. This paper proposes the Multi-Swarm Particle Swarm Optimization (MSPSO) to enhance the diversity of particles and prevent premature convergence in PSO. MSPSO work cooperatively and competitively to optimize laying hen diet and produce improved and stable formula than Genetic Algorithm, Hybridization of Adaptive Genetic Algorithm and Simulated Annealing, and Standard Particle Swarm Optimization with less time complexity. In addition, swarm size, iteration, and inertia weight parameters are investigated and show that swarm size of 50 for each sub-swarm, total iteration of 16,000, and inertia weight of 6.0 should be used as a good parameter for MSPSO to optimize laying hen diet.
Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System Gusti Ahmad Fanshuri Alfarisy; Wayan Firdaus Mahmudy
Journal of Information Technology and Computer Science Vol. 1 No. 2: November 2016
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (590.164 KB) | DOI: 10.25126/jitecs.20161212

Abstract

Rainfall forcasting is a non-linear forecasting process that varies according to area and strongly influenced by climate change. It is a difficult process due to complexity of rainfall trend in the previous event and the popularity of Adaptive Neuro Fuzzy Inference System (ANFIS) with hybrid learning method give high prediction for rainfall as a forecasting model. Thus, in this study we investigate the efficient membership function of ANFIS for predicting rainfall in Banyuwangi, Indonesia. The number of different membership functions that use hybrid learning method is compared. The validation process shows that 3 or 4 membership function gives minimum RMSE results that use temperature, wind speed and relative humidity as parameters.
Co-Authors Muhammad Halim Natsir Wayan Firdaus Mahmudy