Analysis and implementation of cross lingual short message service spam filtering using graph-based k-nearest neighbor

Abstract

SMS (Short Message Service) is on e of the communication services that still be the main choice, although now the phone grow with various applications. Along with the development of various other communication media, some countries lowered SMS rates to keep the interest of mobile users. It resulted in increased spam SMS that used by several parties, one of them for advertisement. Given the kind of multi-lingual documents in a message SMS, the Web, and others, necessary for effective multilingual or cross-lingual processing techniques is becoming increasingly important. The steps that performed in this research is data / messages first preprocessing then represented into a graph model. Then calculated using GKNN method. From this research we get the maximum accuracy is 98.86 with training data in Indonesian language and testing data in indonesian language with K 10 and threshold 0.001.