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Yusuf Eka Wicaksana
Universitas Budi Luhur
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Implementation of TF-IDF Algorithm and K-mean Clustering Method to Predict Words or Topics on Twitter Muhammad Darwis; Gatot Tri Pranoto; Yusuf Eka Wicaksana; Yaddarabullah Yaddarabullah
JISA(Jurnal Informatika dan Sains) Vol 3, No 2 (2020): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v3i2.831

Abstract

The social media time line, especially Twitter, is still interesting to follow. Various tweets delivered by the public are very informative and varied. This information should be able to be used further by utilizing the topic of conversation trends at one time. In this paper, the authors cluster the tweet data with the TF-IDF algorithm and the K-Mean method using the python programming language. The results of the tweet data clustering show predictions or possible topics of conversation that are being widely discussed by netizens. Finally, the data can be used to make decisions that utilize community sentiment towards an event through social media like Twitter.  
Co-Authors Gatot Tri Pranoto Muhammad Darwis Yaddarabullah, Yaddarabullah