( Department of Mathematics Faculty of Mathematics and Science Institut Teknologi Sepuluh Nopember, Department of Information Systems Faculty of Information Technology Institut Teknologi Sepuluh Nopember )
Keywords: data mining,classification,Meteorological Data,Association,Sequential pattern
An increase in the growth of data as a result of the widely use of applications as well as information systems has made data mining an important task in knowledge discovery field of research. Several methods in data mining such as classification has been proposed based on renowned learning methods such as decision tree or neural network. Few studies explored the topic of classification combined with another task in data mining such as association and sequential pattern. Several algorithms that combine classification with other data mining tasks are Classification Based on Associations (CBA) that combines classification with association rules and Classify by Sequence (CBS) algorithm which combines classification with sequential patterns. However, none of studies analyzes the comparison between these two algorithms. In this paper, we explore and compare the performance of CBA algorithm and CBS algorithm in term of accuracy and running time. To do so, we use meteorological data for rain or dry season classification with average temperature, wind speed, relative humidity and air pressure set as our parameters. Based on our experimental evaluation, the CBS algorithm results in high accuracy than of the CBA algorithm. In term of runtime, the CBS algorithm is more efficient than the CBA algorithm.