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All Journal Journal of Information Technology and Computer Science Indonesian Journal of Artificial Intelligence and Data Mining Buletin Ilmiah Sarjana Teknik Elektro Equiva
Gusti Ahmad Fanshuri Alfarisy, Gusti Ahmad Fanshuri
Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mathematics

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Journal : Journal of Information Technology and Computer Science

 1 
Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System Alfarisy, Gusti Ahmad Fanshuri; Mahmudy, Wayan Firdaus
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.
Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization Fatyanosa, Tirana Noor; Sihananto, Andreas Nugroho; Alfarisy, Gusti Ahmad Fanshuri; Burhan, M Shochibul; Mahmudy, Wayan Firdaus
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 (879.719 KB) | DOI: 10.25126/jitecs.20161215

Abstract

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result
Co-Authors Andreas Nugroho Sihananto Bhagavatgita Mahardika Kesuma Putra Fadhliana, Nisa Rizqiza Helmi M Shochibul Burhan, M Shochibul Nugraha, Faizal Widya Nur Ali Rajab Nuryono, Aninditya Anggari Putra, Rizal Kusuma Richard Owen Hoan Rizky Amelia Saman, Muhamad Tirana Noor Fatyanosa, Tirana Noor Wayan Firdaus Mahmudy