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Jeffer Oliando
Fakultas Teknologi dan Ilmu Komputer, Universitas Prima Indonesia
Arts Humanities Computer Science & IT Law, Crime, Criminology & Criminal Justice Social Sciences

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PENGELOMPOKKAN BERITA KESEHATAN PADA SOSIAL MEDIA TWITTER DENGAN METODE K-MEANS CLUSTERING Anita Anita; Jeffer Oliando
Ensiklopedia of Journal Vol 4, No 3 (2022): Vol 4 No. 3 Edisi 1 April 2022
Publisher : Lembaga Penelitian dan Penerbitan Hasil Penelitian Ensiklopedia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (238.893 KB) | DOI: 10.33559/eoj.v4i3.877

Abstract

Twitter is one of the most influential social media in the world with 15.7 million users in Indonesia and is ranked 6th as the country with the most Twitter users. Besides being used as social media, Twitter is also used as a medium to read or send news. News information that continues to increase causes users to have difficulty in finding certain news information. One solution that can be applied to overcome this problem is through clustering of news information on Twitter. In this study, the researcher used quantitative research with the K-Means Clustering method, which is one of the clustering methods used in grouping data. The data used in this study is a dataset taken from the UCI Machine Learning Repository, namely Health News in Twitter Data Set as many as 14,970 tweet data. The results showed that the determination of the best cluster using the Elbow method on the dataset resulted in empirical evidence that the best cluster was K=3. The results of grouping health news on Twitter social media using the K-Means Clustering method with K=3 resulted in the number of clusters, namely C1 as many as 4,991 data tweets, C2 as many as 4,482 tweets data, and C3 as many as 5,497 tweets.Keywords: Health News Grouping; Twitter Social Media; Elbow Method; K-Means Clustering Method.
Co-Authors Anita Anita