PRINCIPAL COMPONENT ANALYSIS-VECTOR AUTOREGRESSIVE INTEGRATED (PCA-VARI) MODEL USING DATA MINING APPROACH TO CLIMATE DATA IN THE WEST JAVA REGION

Abstract

Over a long time, atmospheric changes have been caused by natural phenomena. This study uses the Principal Component Analysis (PCA) model combined with Vector Autoregressive Integrated (VARI) called the PCA-VARI model through the data mining approach. PCA reduces ten variables of climate data into two principal components during ten years (2001-2020) of climate data from NASA Prediction Of Worldwide Energy Resources. VARI is a non-stationary multivariate time series to model two or more variables that influence each other using a differencing process. The Knowledge Discovery in Database (KDD) method was conducted for empirical analysis. Pre-processing is an analysis of raw climate data. The data mining process determines the proportion of each component of PCA and is selected as variables in the VARI process. The postprocessing is by visualizing and interpreting the PCA-VARI model. Variables of solar radiation and precipitation are strongly correlated with each measurement location data. A forecast of the interaction of variables between locations is shown in the results of Impulse Response Function (IRF) visualization, where the climate of the West Java region, especially the Lembang and Bogor areas, has strong response climate locations, which influence each other.