K-MEANS ALGORITHM IMPLEMENTATION FOR NEWS CLUSTERING

Larson, Dicky (2017) K-MEANS ALGORITHM IMPLEMENTATION FOR NEWS CLUSTERING. Other thesis, Unika Soegijapranata.

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13.02.0001 Dicky Larson COVER.pdf

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Abstract

With rapid development of internet, there are a lot of online news which are made. With all the amount of news circulate, it will require a lot of time and effort to find news with the wanted topic. To make it easy for user to find news with the topic user want, the program is created to automate the news clustering so that user can easily find the related news. The program will implement the kmeans algorithm. K-means algorithm is one of the algorithm for clustering which has long existed. It has been proven in many implementations of program. K-means use euclidean distance to count data point distance between data and centroid. The euclidean distance between all the centroid compared to find the nearest distance. All the euclidean with the nearest distance with the same centroid will be searched for average. The average will be the new centroid data. The process is repeated until the new centroid data not changed or similar with the previous centroid data. The result is the program that can clustering online news article based on similar topic with user news article. User upload their own news article as the centroid. And the online news taken from kompas.com. Keyword: news clustering, k-means, data mining

Item Type: Thesis (Other)
Subjects: 000 Computer Science, Information and General Works > 004 Data processing & computer science
Divisions: Faculty of Computer Science > Department of Informatics Engineering
Depositing User: Mr Ign. Setya Dwiana
Date Deposited: 05 Dec 2017 07:59
Last Modified: 05 Dec 2017 07:59
URI: http://repository.unika.ac.id/id/eprint/15348

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