<![CDATA[Change.org is a website that is often used by people, which means for online delivering petitions and social campaignings. Campaign through social media had been proven that can make a change. The flow information of online petitions documents is updated daily in large numbers. It makes documents clustering being very important. Documents clustering is a process of grouping documents which have same topic. It aims to devide documents by its similarly, so the process of searching will be easier. This study uses hierarchical clustering UPGMA or unweighted pair-group method by arithmetic averages with adding feature reduction using latent semantic indexing method, that is the result of splitting singular value decomposition matrix. The result of this study conclude that latent semantic indexing method can solved the problem in high-dimensional data. The data conducted by 100 petitions. The result of performance testing which used cophenetic correlation coefficient obtained cophenetic value of 0.75959 at LSI matrix rank of 10 % and silhouette coefficient of 0.36862 with number of clusters as many as 2 clusters.]]>
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