<![CDATA[Earthquakes are a type of natural disaster that currently cannot be predicted. Predicting the value of earthquake magnitude for related parties such as government and National Disaster Management Authority is very important. Furthermore, the results of earthquake predictions by several parties are used as indicators in post-earthquake response in minimizing the risks that will occur. Several studies have applied machine learning methods to predict earthquakes such as deep neural networks and parallel Support Vector Regression. In this article, we propose a data mining method using the Support Vector Machine (SVM) algorithm accompanied by the optimization of the windowing parameter value in the model that is applied to predict the value of the earthquake magnitude. Based on its advantages, the SVM model was chosen because it has been applicable in time series data processing. In the experimental stage process, parameter settings are first carried out, namely setting the kernel type, sampling type, and number of windowing to optimize the level of accuracy of the resulting model. The results showed that the best model with the smallest Root Mean Square Error (RMSE) was 0.712.]]>
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