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

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