Please use this identifier to cite or link to this item: http://repository.uinjkt.ac.id/dspace/handle/123456789/47250
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dc.contributor.authorNurhayatiid
dc.contributor.authorNadika Sigit Sinatryaid
dc.contributor.authorLuh Kesuma Wardhaniid
dc.contributor.authorBusmanid
dc.date.accessioned2019-10-02T09:43:02Z-
dc.date.available2019-10-02T09:43:02Z-
dc.date.issued2018-03-28-
dc.identifier.isbn978-1-5386-5436-1-
dc.identifier.urihttp://repository.uinjkt.ac.id/dspace/handle/123456789/47250-
dc.description.abstractThis research’s goal is to find out the better performance algorithm between K-Means and K-Medoids algorithm. The performance of both algorithm are compared by testing data using Java-based application, Hadoop, and Hive. comparison was conducted in terms of accuracy, execution time and time complexity of the algorithm. In terms of accuracy, K-Medoids is better than K-Means with an average accuracy of 63.24%, while K-Means is 52.11%. In terms of execution time, K-Medoids also has better performance with average speed of 3.1 ms, while K-Means is 3.45 ms. In terms of time complexity algorithms, both algorithms have the result of O (n2). K-Medoids has better performance than K-Means, which K-Medoids has an average value of 310.157, while K-Means has greater value than K-Medoids of 377,886. So the K-Medoids algorithm is superior to K-Means in terms of accuracy, execution time and time complexity.id
dc.description.urihttps://ieeexplore.ieee.org/document/8674251id
dc.language.isoenid
dc.publisherIEEEid
dc.titleAnalysis of K-Means and K-Medoids’s Performance Using Big Data Technologyid
dc.typeArticleid
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