Main Article Content
Ghulam Asrofi Buntoro | firstname.lastname@example.org
Ghulam Asrofi Buntoro
The East Java Governor Election which will be held in 2018 is also felt in the virtual world especially Twitter social media. All people freely argue about their respective governor candidates, the memorandum raises many opinions, not only positive or neutral but also negative opinions. Media growth is so rapid, revealing a lot of online media from the news media to social media. Social media alone is Facebook, Twitter, Path, Instagram, Google+, Tumblr, Linkedin and many more. Today's social media is not only used as a means of friendship or making friends, but also for other activities. Promos of trading or buying and selling, until political party promos or campaigns of candidates for regents, governors, legislative candidates until presidential candidates. The research objective is to conduct a method of analyzing the sentiments of 2018 East Java Governor candidates on Twitter social media with optimal and maximum optimization. While the benefits are to help the community conduct research on opinions on twitter which contains positive, neutral or negative sentiments. Analysis of the sentiments of East Java Governor candidates in 2018 on twitter social media using non-conventional processes that save costs, time and effort. The results of Khofifah's dataset are 77% accuracy, 79.2% precision value, 77% recall value, 98.6% TP rate and 22.2% TN rate. For the results of Gus dataset, the accuracy is 76%, the precision value is 74.4%, the recall value is 76%, the TP rate is 93.8% and the TN rate is 52.9%.
 S. Aslam, “• Twitter by the Numbers (2018): Stats, Demographics & Fun Facts,” 01-Jan-2018.
 B. Liu, “Sentiment Analysis and Subjectivity” Handb. Nat. Lang. Process., vol. 2, pp. 627–666, 2010.
 B. Wagh, S. J. V., and W. N. R., “Sentimental Analysis on Twitter Data using Naive Bayes,” IJARCCE, vol. 5, no. 12, pp. 316–319, Dec. 2016.
 “RENSTRA PENELITIAN_Universitas_Muhammadiyah_Ponorogo.pdf.”
 M. Kaya, G. Fidan, and I. H. Toroslu, “Sentiment Analysis of Turkish Political News,” 2012, pp. 174–180.
 A. Pak and P. Paroubek, “Twitter as a corpus for sentiment analysis and opinion mining.,” in LREc, 2010, vol. 10.
 B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? sentiment classification using machine learning techniques,” in Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, 2002, pp. 79–86.
 G. A. Buntoro, (2017). Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter. INTEGER: Journal of Information Technology, 2(1).
 A. F. Hadi and M. Hasan, “TEXT MINING PADA MEDIA SOSIAL TWITTER STUDI KASUS: MASA TENANG PILKADA DKI 2017 PUTARAN 2.” Seminar Nasional Matematika dan Aplikasinya, Universitas Airlangga 2017.
 G. A. Buntoro, (2016). " Sentiment Analysis Candidates of Indonesian Presiden 2014 with Five Class Attribute" in International Journal of Computer Applications (0975 – 8887).
 N. Adiyasa, “Analisis Sentimen Pada Opini Berbahasa Indonesia Menggunakan Pendekatan Lexicon-Based,” Catatan Kecil, 2011. [Online]. Available: http://adiyasan.wordpress.com/2013/02/08/sentiment-analysis-menggunakan-pendekatan-lexicon-based/. [Accessed: 10-Mar-2014].
 ARFF files from Text Collections. http://weka.wikispaces.com/ARFF+files+from+Text+Collections.
 Class StringToWordVector. http://weka.sourceforge.net/doc.de.v/weka/filters/unsupervised/attribute/StringToWordVector.html.
 Ian H. Witten. (2013) Data Mining with WEKA. Department of Computer Science University of Waikato New Zealand.
 Kohavi, & Provost. (1998) Confusion Matrix http://www2.cs.uregina.ca/~dbd/cs831/notes/confusion_matrix/confusion_matrix.html