IMPLEMENTASI ALGORITMA SUFFIX TREE CLUSTERING DAN NEAREST NEIGHBOR UNTUK MENGELOMPOKKAN BERITA PADA TIMELINE TWITTER; IMPLEMENTATION OF SUFFIX TREE CLUSTERING AND NEAREST NEIGHBOR ALGORITHMS FOR GROUPING NEWS ON TWITTER TIMELINE

JUMADI, Edi Winarko

2013 | Disertasi | PROGRAM STUDI S2 ILMU KOMPUTER

Dynamism news tweet content are disseminated by news organization providers on Twitter, causing the large number of tweets published every day. It can increase the length of Microblog web pages, and inflict to the classic problems that require page scrolling process is longer during the process of reading all of the existing text news tweets. The problems solving that can be done to reduce the length of the web pages is by grouping the existing text news thematic. Grouping system suitable for this problem is the clustering system. Based on some existing research, one good method in the process of clustering text documents is a Suffix Tree Clustering (STC). This method has a very high accuracy rate, because clusters create based on phrase-shared among documents existing text. But one of the existing research in the process of clustering algorithms using STC, still produce text documents Other Topics cluster members in large numbers and text documents members of this cluster are still relevant to the text document members of the existing clusters. Therefore, the text documents that is in the Other Topics cluster need to compare with all text documents in the existing clusters to determine the level of similarity. Thus, a text document Other Topics cluster members can be classified into one particular cluster by using the cosine similarity function based on the results of calculations using the method of Vector Space Model (VSM) which refers to the term frequency and the frequency of existing documents. Results of this calculation will be used by the Nearest Neighbor method in the classification process to determine the destination cluster displacement for text documents Other Topics cluster members. The main criteria of goal cluster as destination of displacement is the cluster with the highest number of members that have the highest similarity. The process of moving text document cluster members Other Topics impact on the reduction in the number of members of this cluster. Finally, if the Other Topics cluster has no members then this cluster can be eliminated.

Kata Kunci : Classification; Clustering; Cosine Similarity; Nearest Neighbor; Suffix Tree Clustering