Classification of rainfall data using support vector machine

Abstract

Classification problems can be based on cross-section or time-series data. In general, some of the characteristics found in time series data are data that are very susceptible to containing noise and outliers. In this paper, time series data-based classification was carried out using the support vector machine (SVM) method. The SVM method is the most popular binary classification technique in machine learning. The advantage of this method is that it can find a global optimum solution and always achieve the same solution for every running. Another advantage is that it can solve the over-fitting problem by minimizing the upper limit of generalization errors. SVM classification model performance can be seen from classification accuracy and sensitivity and specificity tests. The results showed that based on the level of classification accuracy used the SVM method resulted in an accuracy rate of the prediction results with training data of 96,3% and the accuracy of the prediction results with testing data of 90,08%. Therefore, the classification of rainfall data using the SVM method has a very good performance.