DOI: https://doi.org/10.14710/jtsiskom.8.1.2020.64-68

Kombinasi SOM-RBF untuk prediksi drought code berdasarkan data curah hujan dan suhu udara

Combination of SOM-RBF for drought code prediction using rainfall and air temperature data

Department of Computer Engineering, Universitas Tanjungpura, Indonesia

Received: 31 Jan 2019; Revised: 7 Nov 2019; Accepted: 18 Nov 2019; Published: 31 Jan 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
This study aims to predict Drought Code (DC) in Kabupaten Kubu Raya using a combination of SOM-RBF. The final weight value of SOM was used as a center on the RBF network. The input data variables are rainfall data and air temperature data for three days with three binary outputs to predict DC values. This study also observed the effect of the number of neurons, learning rates, and the number of iterations on the results of the SOM-RBF network training. The smallest MSE of training result from the SOM-RBF network was 0.159933 using 65 neurons in the hidden layer, learning rate 0.007, and epoch 45000. The detection accuracy of SOM-RBF was 91.34 % from 245 test data.
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Keywords: drought code prediction; self organizing map; radial basis function
Funding: Universitas Tanjungpura

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Section: Original Research Articles
Language : ID
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