PERBANDINGAN RADIAL BASIS FUNCTION DAN RECURRENT NEURAL NETWORK PADA PREDIKSI CURAH HUJAN DI PALEMBANG

SIAGIAN, DEWI PUTRI and Sazaki, Yoppy and Saputra, Danny Matthew (2019) PERBANDINGAN RADIAL BASIS FUNCTION DAN RECURRENT NEURAL NETWORK PADA PREDIKSI CURAH HUJAN DI PALEMBANG. Undergraduate thesis, Sriwijaya University.

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Abstract

Abstract-Rainfall plays a major role in economic growth of every region. Its predetermined and accurate are very important to many sectors especially to agriculture in Palembang. In this paper, two types of artificial neural networks (ANNs), Radial Basis Function (RBF) and Recurrent Neural Network (RNN) were used to predict the monthly rainfall values based on the data collected from Agency for Meteorology, Climatology and Geophysics in Kenten district (Palembang). Moreover, accuracy and root mean square error (RMSE) are the performance indices used for the comparative analysis. Experimental results showed that the recurrent neural network acts better prediction model with higher accuracy values but have 0,07 higher root mean square error values than radial basis function. Furthermore, we should use this method to predict the future data based on previously collected data.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Radial Basis Function, Reccurent Neural Network, RMSE, Akurasi
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D3.H3474 Databases--Handbooks, manuals, etc. Web databases--Handbooks, manuals, etc. Information retrieval--Handbooks, manuals, etc. Electronic data processing--Handbooks, manuals, etc. Data structures (Computer science)--Handbooks, manuals, etc.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 3957 not found.
Date Deposited:	07 Jan 2020 08:21
Last Modified:	07 Jan 2020 08:22
URI:	http://repository.unsri.ac.id/id/eprint/23251

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