BibTex Citation Data :
@article{J.Gauss14733, author = {Syaifudin Karyadi and Hasbi Yasin and Moch. Mukid}, title = {ANALISIS KECENDERUNGAN INFORMASI DENGAN MENGGUNAKAN METODE TEXT MINING (Studi Kasus: Akun twitter @detikcom)}, journal = {Jurnal Gaussian}, volume = {5}, number = {4}, year = {2016}, keywords = {text mining, clustering,, k-means , dunn index, and twitter.}, abstract = { The internet is an extraordinary phenomenon. Starting from a military experiment in the United States, the internet has evolved into a 'need' for more than tens of millions of people worldwide. The number of internet users is large and growing, has been creating internet culture. One of the fast growing social media twitter. Twitter is a microblogging service that stores text database called tweets. To make it easier to obtain information that is dominant discussed, then sought the topic of twitter tweet using clustering. In this research, grouping 500 tweets from twitter account @detikcom using k-means clustering. The results of this study indicate that the maximum index Dunn, the best grouping K-means clustering to obtain the dominant topic as many as three clusters, namely the government, Jakarta, and politics. Keywords: text mining, clustering,, k-means , dunn index, and twitter. }, issn = {2339-2541}, pages = {763--770} doi = {10.14710/j.gauss.5.4.763-770}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/14733} }
Refworks Citation Data :
The internet is an extraordinary phenomenon. Starting from a military experiment in the United States, the internet has evolved into a 'need' for more than tens of millions of people worldwide. The number of internet users is large and growing, has been creating internet culture. One of the fast growing social media twitter. Twitter is a microblogging service that stores text database called tweets. To make it easier to obtain information that is dominant discussed, then sought the topic of twitter tweet using clustering. In this research, grouping 500 tweets from twitter account @detikcom using k-means clustering. The results of this study indicate that the maximum index Dunn, the best grouping K-means clustering to obtain the dominant topic as many as three clusters, namely the government, Jakarta, and politics.
Keywords: text mining, clustering,, k-means , dunn index, and twitter.
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