Klasifikasi Sentimen Komentar Politik dari Facebook Page Menggunakan Naive Bayes
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Feb 8, 2017
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Antonius Rachmat C
Universitas Kristen Duta Wacana
Yuan Lukito
Universitas Kristen Duta Wacana
Abstract
Seiring maraknya situs media sosial yang digunakan sebagai sarana kampanye politik online maka makin banyak pula daukungan kampanye dari dunia maya melalui berbagai cara. Cara kampanye yang digunakan para politisi diantaranya adalah melalui Twitter hashtag, petisi di Facebook, atau pembuatan Facebook Page di mana komentarnya dapat di-like/disline oleh para pendukungnya. Permasalahan yang dibahas pada tulisan ini adalah belum banyaknya sistem yang dapat mengklasifikasikan pro kontra dari komentar-komentar yang terdapat pada Facebook Page. Pada tulisan ini akan dibahas penggunaan metode Naive Bayes untuk melakukan klasifikasi sentimen positif atau negatif terhadap komentar dari status kampanye politik dari Facebook Page. Studi kasus yang digunakan pada penelitian ini adalah status dan komentar terhadap Facebok Page calon presiden Republik Indonesia pada Pemilu tahun 2014. Tahapan penelitian dilakukan dengan pengumpulan data 68 status (3400 komentar) selama masa kampanye, dengan kegiatan preprosesing tokenisasi, stemming, pembobotan token, kemudian dilanjutkan klasifikasi, dan pengujian menggunakan confusion matrix. Dari hasil implementasi dan pengujian, metode Naive Bayes memiliki tingkat akurasi klasifikasi sentimen mencapai lebih dari 83%.
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Rachmat C, A., & Lukito, Y. (2017). Klasifikasi Sentimen Komentar Politik dari Facebook Page Menggunakan Naive Bayes. Jurnal Informatika Dan Sistem Informasi, 2(2), 26–34. Retrieved from https://journal.uc.ac.id/index.php/JUISI/article/view/239
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Author Biographies
Antonius Rachmat C, Universitas Kristen Duta Wacana
Dosen Tetap Program Studi Teknik InformatikaYuan Lukito, Universitas Kristen Duta Wacana
Dosen Tetap Program Studi Teknik InformatikaReferences
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C. D. Manning, P. Raghavan and H. Schütze, Introduction to information retrieval, New York: Cambridge University Press, 2008.
K. V. Ghag and K. Shah, "Comparative analysis of effect of stopwords removal on sentiment," 2015 International Conference on Computer, Communication and Control (IC4), 2015.
G. Patil, V. Galande, V. Kekan and K. Dange, "Sentiment Analysis Using Support Vector Machine," International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, no. 1, pp. 2607-2612, 2014.
B. Liu, Sentiment analysis: mining opinions, sentiments, and emotions, New York: Cambridge University Press, 2015.
J. Han, M. Kamber and J. Pei, Classification: basic concepts. In Data mining Concepts and techniques, Amsterdam: Elsevier, 2012.
H. Hamilton, "www2.cs.uregina.ca," Computer Science Uregina, 2009. [Online]. Available: http://www2.cs.uregina.ca/~dbd/cs831/notes/confusion_matrix /confusion_matrix.html. [Accessed 4 February 2016].
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