Forecasting Solar Activities based on Sunspot Number Using Support Vector Regression (SVR)

Suwanto Suwanto, Dian Candra Rini Novitasari

Abstract


Progress on the 4.0 industrial revolution was the most significant is the entire computer and robot is connected to the internet connection. Satellites as one of the internet network transmitters have the threat of destruction if a solar storm occurs. The size of the activity that is on the sun can be known by observing sunspots. Solar activity in the future is known by forecasting sunspot numbers. This research will forecast sunspot numbers using support vector regression (SVR), its aimed to minimized adverse effects on the earth as an outcome of solar storms. The best SVR results on forecast sunspot numbers are on annual sunspot number obtained using RBF kernel. Measurement results from MSE, RMSE, and MAAPE respectively by 35.32, 5.94, and 0.12. Forecasting concluded accurately based on MAAPE value, on 2020 and 2021 indicated potentially flare because the result of forecasting sunspot numbers is more than twenty.

 

DOI: http://dx.doi.org/10.17977/um024v5i12020p006


Keywords


Forecasting; sunspot; SVR.

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Copyright (c) 2020 Suwanto Suwanto, Dian Candra Rini Novitasari

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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.