Web Content Mining Menggunakan Partitional Clustering K-Means Pada News Aggregator
Abstract
News aggregator is one type of aggregator system (collector) which collects news from various sources, and then presented back to the user in a single entity so that users no longer need to venture out to various news sites for just looking for information. The system requires a news aggregator a way to show the same news information from the websites of news services. Based on that, this paper used Web Content Mining (WCM) for information retrieval news from online news sites and partitional K-Means clustering system for processing news aggregator in objective the system to display collection of information based on keyword input from the user.
From the test results using confusion matrix methods with a number of documents as many as 132 documents were taken from crawling indicate that the method partitional clustering K-Means can be applied to a system news aggregator for classifying news information with keyword "education" with an average accuracy of the classification of 98%.
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References
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