PENERAPAN TEKNIK DATA MINING DENGAN ALGORITMA CLASSIFICATION TREE UNTUK PREDIKSI HUJAN

Abstrak

Classification is a data mining technique used to predict the relationship between data in a dataset. Prediction is done by classifying data into several different classes by considering certain factors. Classification is one of the empirical approaches that can be used for shortterm weather prediction. The classification algorithm used in this study is the Classification Tree utilizing software of Orange Data Mining 3.3.12. Furthermore, the algorithm is used to predict rain with the Confusion Matrix test parameters. The input data is a synoptic data from the Kemayoran Meteorological Station, Jakarta (96745) for 10 years (2006 - 2015) as many as 3528 datasets and consists of 8 attributes. Based on a series of processing, selection and testing of the model shows that the accuracy of the Classification Tree algorithm is 74.7% with a fair classification category where the number of correct predictions is 818 datasets out of
the total amount of data tested that is 1095 datasets. The dominant weather attributes in the formation of rain respectively are humidity (RHavg), minimum temperature (Tmin), maximum temperature (Tmax), average temperature (Tavg) and wind direction (ddd).