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|dc.contributor.author||Nadika Sigit Sinatrya||id|
|dc.contributor.author||Luh Kesuma Wardhani||id|
|dc.description.abstract||This 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.title||Analysis of K-Means and K-Medoids’s Performance Using Big Data Technology||id|
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