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Internet is becoming an increasingly important platform for ordinary life and work. It is expected that keyword extraction can help people quickly find hot spots on the web, since keywords in a document provide important information about the content of the document. In this paper, we propose to use text clustering
methods for Indonesian corpus is rather small. Brace well's algorithm has been proven effective in identifying topics in English and Japanese corpora with high accuracy. This paper implements a method for TID based on Brace well's keywords similarity algorithm and the top-n keywords selection for Indonesian news documents
citation recommendation suffers with the following three limitations. First, most of the existing approaches for citation recommendation require input in the form of either the full article or a seed set of citations, or both. Nevertheless, obtaining the recommendation for citations given a set of keywords is extremely useful
proposed a collective collaborative tagging (CCT) service architecture in which both service providers and individual users can merge folksonomy data (in the form of keyword tags) stored in different sources to build a larger, unified repository. We have also examined a range of algorithms that can be applied to different
In this research, we used a proxy server to search for information related to the userpsilas browsed Web pages. From the records of the proxy server we constructed a profile of the userpsilas browsing habits. At the end of the userpsilas search subsystem, we will use content based concept to extract keywords to obtain
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.