PREDIKSI TWEET NETIZEN MENGGUNAKAN ALGORITMA RANDOM FOREST, DECISION TREE, NAÏVE BAYES, DAN ENSEMBLE (STUDI KASUS GUBERNUR DKI JAKARTA)

PREDIKSI TWEET NETIZEN MENGGUNAKAN ALGORITMA RANDOM FOREST, DECISION TREE, NAÏVE BAYES, DAN ENSEMBLE (STUDI KASUS GUBERNUR DKI JAKARTA)

• By Yadi Kuntoro, Antonius;
• Magister Manajemen
• 270

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%.
Key Words: Decision Tree, Naive Bayes, Random Forest, Ensemble, Twitter.

• Katakunci
• URL http://
• Penerbit ,

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