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Journal : Journal of Information Technology and Computer Science

Rainfall Forecasting Using Backpropagation Neural Network Sihananto, Andreas Nugroho; Mahmudy, Wayan Firdaus
Journal of Information Technology and Computer Science Vol. 2 No. 2: November 2017
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (390.659 KB) | DOI: 10.25126/jitecs.2017229

Abstract

Rainfall already became vital observation object because it affects society life both in rural areas or urban areas. Because parameters to predict rainfall rates is very complex, using physics based model that need many parameters is not a good choice. Using alternative approach like time-series based model is a good alternative. One of the algorithm that widely used to predict future events is Neural Network Backpropagation. On this research we will use Nguyen-Widrow method to initialize weight of Neural Network to reduce training time. The lowest MSE achieved is {0,02815;  0,01686; 0,01934; 0,03196} by using 50 maximum epoch and 3 neurons on hidden layer.
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