SIMULASI CURAH HUJAN BULANAN KOTA PALEMBANG DENGAN JARINGAN SYARAF TIRUAN
Abstract
Rainfall is one of the important data used in the involvement of infrastructure development, irrigation, agriculture and others.The development of science and technology has reached the stage of progress towards the era of digitalization.Consequently new software is needed to support the progress of science and technology.One of the software that has been developed at this time is matlab software. The method used in the simulation is Artificial Neural Networks (ANN) which are analyzed using Matlab. Artificial Neural Network is a method that has the ability to imitate the input data entered into a simulation.Artificial Neural Networks has been widely used in research.Therefore in this research, it will use Artificial Neural Networks to process rainfall data Palembang city.Rainfall data used is monthly rainfall data from 2016 until 2018.In the results of research that has been carried out obtained the smallest error of 1.84% and stopped at 25000 epoch trial.The distribution of monthly rainfall data in the dry season affects the ANN simulation, causing an error to be large in the dry month.
References
Agrawal, V., Nagar, R., & Sancheti, G. (2011). Application of artificial neural network in conceptual design of communication towers. International Conference on Electrical, Electronics and Civil Engineering (ICECCE’2011). Pattaya.
Aryastana, P., Tanaka, T., & Mahendra, M. S. (2012). Characteristic of rainfall pattern before flood occur in Indonesia based on rainfall data from GSMaP. ECOTROPHIC : Jurnal Ilmu Lingkungan, 7(2), 100–110.
Liu, C.-Y., Aryastana, P., Liu, G.-R., & Huang, W.-R. (2020). Assessment of satellite precipitation product estimates over Bali Island. Atmospheric Research, 105032. https://doi.org/10.1016/j.atmosres.2020.105032
Meon, M. S., Anuar, M. A., Ramli, M. H. M., Kuntjoro, W., & Muhammad, Z. (2012). Frame Optimization using Neural Network. International Journal on Advanced Science, Engineering and Information Technology, 2(1), 28–33. https://doi.org/10.18517/ijaseit.2.1.148
Setiawati, M. D., Miura, F., & Aryastana, P. (2016). Validation of Hourly GSMaP and ground base estimates of precipitation for flood monitoring in Kumamoto, Japan. In P. K. Srivastava, P. C. Pandey, P. Kumar, A. S. Raghubanshi, & D. Han (Eds.), Geospatial Technology for Water Resource Applications (pp. 130–143). https://doi.org/10.1201/9781315370989
Siang, J. J. (2014). Jaringan Saraf Tiruan & Pemrogramannya Menggunakan MATLAB. Yogyakarta: ANDI.
Sultan, M. H. (2014). Optimasi parameter neural network pada data time series untuk memprediksi rata-rata kekuatan gempa per periode (studi kasus gempa bumi di Maluku Utara). CAUCHY: Jurnal Matematika Murni Dan Aplikasi, 3(2), 59–71.
Yudhistira, A. T. (2014). Prediksi penurunan kapasitas struktur atas jembatan rangka baja menggunakan metode artificial neural network. Universitas Gadjah Mada.
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