Prediction of Netizen Tweets Using Random Forest, Decision Tree, Naïve Bayes, and Ensemble Algorithm

Main Article Content

Yan Rianto Antonius Yadi Kuntoro
Corresponding Author:
Antonius Yadi Kuntoro | antonius.aio@nusamandiri.ac.id

Copyright (C):
Yan Rianto, Antonius Yadi Kuntoro

Abstract

The current Governor of DKI Jakarta, even though he has been elected since 2017 is always interesting to talk about or even comment on. Comments that appear come from the media directly or through social media. Twitter has become one of the social media that is often used as a media to comment on elected governors and can even become a trending topic on Twitter social media. Netizens who comment are also varied, some are always Tweeting criticism, some are commenting Positively, and some are only re-Tweeting. In this research, a prediction of whether active Netizens will tend to always lead to Positive or Negative comments will be carried out in this study. Model algorithms used are Decision Tree, Naïve Bayes, Random Forest, and also Ensemble. Twitter data that is processed must go through preprocessing first before proceeding using Rapidminer. In trials using Rapidminer conducted in four trials by dividing into two parts, namely testing data and training data. Comparisons made are 10% testing data: 90% Training data, then 20% testing data: 80% training data, then 30% testing data: 70% training data, and the last is 35% testing data: 65% training data. The average Accuracy for the Decision Tree algorithm is 93.15%, while for the Naïve Bayes algorithm the Accuracy is 91.55%, then for the Random Forest algorithm is 93.41, and the last is the Ensemble algorithm with an Accuracy of 93, 42%. here.

Keyword: Decision Tree, Naïve Bayes, Random Forest, Set, Twitter

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How to Cite
RIANTO, Yan; KUNTORO, Antonius Yadi. Prediction of Netizen Tweets Using Random Forest, Decision Tree, Naïve Bayes, and Ensemble Algorithm. Sinkron : Jurnal dan Penelitian Teknik Informatika, [S.l.], v. 5, n. 1, p. 58-71, sep. 2020. ISSN 2541-2019. Available at: <http://jurnal.polgan.ac.id/index.php/sinkron/article/view/10565>. Date accessed: 26 oct. 2020. doi: https://doi.org/10.33395/sinkron.v5i1.10565.
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