Nabilla, Syntia2021-07-082021-07-082020-06wahyu sari yenihttps://repository.unri.ac.id/handle/123456789/10006Understanding the final assignment with multiple pages takes a long time and it’s not efficient sometimes. This research was made to create automatic summarizing. The data used is chapter IV of the final assignment of the student Information System who graduated in 2018.. Process of the text preprocessing is divided into several parts, case folding, filtering, stemming and tokenizing. After the data passed through the text preprocessing process, the data will be weighted using Term Frequency-Inverse Document Frequency (TF-IDF) method. Then the process of grouping can be done by using k-means clustering method. Testing use compression rate showed 89.06% information in the document successfully summarized and testing use silhouette coefficient shows an average value of 0.202920986 with the mean quality of the cluster in the good category. KeywordsenCompression RateK-MeansSilhouette CoefficientText PreprocessingText MiningTF-IDF.Article