PREDIKSI KEJADIAN PETIR DENGAN ARTIFICIAL NEURAL NETWORK DI WILAYAH KABUPATEN KEPULAUAN TANIMBAR

Abstract

Various research efforts have been made to determine thunderstorm prediction methods, one of which involves using upper air data. However, the use of atmospheric stability threshold values as a reference does not always apply uniformly to all locations due to differences in the characteristics of each region. Therefore, a more objective and precise approach is needed in predicting thunderstorm events, including the application of artificial neural network (ANN) techniques. In this study, the Artificial Neural Network (ANN) method, which is an implementation of artificial intelligence, is used to predict thunderstorm events in the Saumlaki region. The ANN input not only relies on raw data in the form of atmospheric instability index values but also uses feature selection processing to reduce the dimensionality of multivariate input data, minimizing the loss of input data. This process focuses only on essential information and eliminates linear dependencies between features, a technique known as Principal Component Analysis (PCA). The research results indicate that ANN with PCA technique has a higher level of accuracy in predicting thunderstorm events in the Saumlaki region.