RAINFALL PREDICTION USING DATA MINING TECHNIQUES - Riko Herwanto; Rosyana Fitria Purnomo; Sriyanto - (Proceeding International Conference on Information Technology and Business, 2017)
Rainfall is an important factor in agrarian countries such as Indonesia. Rainfall prediction has become one of the most challenging technological challenges and challenges in the world. And also the most significant and difficult task for researchers in recent years. In Data Mining, the classification algorithm is primarily used to predict rainfall, temperature, various methods of available rainfall estimation that will be used to determine the cultivation time for a particular crop, a particular crop varieties.The reliability of this prediction depends on accuracy in choosing correlated variables. If existing historical databases fail to record the most correlated variables, then the reliability of these data-driven forecast approaches is questionable. In this paper, an attempt has been made to develop a methodological framework that leverages the power of a predefined data mining analysis (decision tree). The decision-based rainfall prediction model developed maps climate variables, namely; a) temperature, b) humidity, and c) wind speed over the observed rainfall database.This paper uses data mining techniques such as Clustering Technique, Decision Tree and classification for rainfall prediction. Keywords: Rainfall, Rainy Season, Data Mining, Classification, Decision Tree, Bayesian Technique.