Twitter Sentiment Analysis on 2013 Curriculum Using Ensemble Features and K-Nearest Neighbor

M. Rizzo Irfan, M. Ali Fauzi, Tibyani Tibyani, Nurul Dyah Mentari


2013 curriculum is a new curriculum in the Indonesian education system which has been enacted by the government to replace KTSP curriculum. The implementation of this curriculum in the last few years has sparked various opinions among students, teachers, and public in general, especially on social media twitter. In this study, a sentimental analysis on 2013 curriculum is conducted. Ensemble of several feature sets were used twitter specific features, textual features, Parts of Speech (POS) features, lexicon based features, and Bag of Words (BOW) features for the sentiment classification using K-Nearest Neighbor method. The experiment result showed that the the ensemble features have the best performance of sentiment classification compared to only using individual features. The best accuracy using ensemble features is 96% when k=5 is used.


Sentiment Analysis; KNN; Ensemble Features; Text Classification; Education; Twitter

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ISSN 2088-8708, e-ISSN 2722-2578