ANALISIS SENTIMEN MASYARAKAT PADA KEBIJAKAN VAKSINASI COVID-19 DI TWITTER MENGGUNAKAN METODE MESIN VEKTOR PENDUKUNG DENGAN KERNEL RADIAL BASIS FUNCTION BERBASIS FITUR LEKSIKON

Sri Mulyani; Sri Astuti Thamrin; Siswanto Siswanto

doi:10.34312/jjps.v3i2.16663

ANALISIS SENTIMEN MASYARAKAT PADA KEBIJAKAN VAKSINASI COVID-19 DI TWITTER MENGGUNAKAN METODE MESIN VEKTOR PENDUKUNG DENGAN KERNEL RADIAL BASIS FUNCTION BERBASIS FITUR LEKSIKON

Sri Mulyani, Sri Astuti Thamrin, Siswanto Siswanto

Abstract

Twitter is one of the popular social media used to get news quickly and briefly. After the outbreak of the COVID-19 virus and the government's policy to vaccinate against COVID-19 in Indonesia, more and more public opinion has been expressed through tweets. This makes the topic of COVID-19 vaccination interesting for sentiment analysis. Through sentiment analysis, information in the form of text data can be extracted to classify information related to positive or negative opinions. In this study, the classification of public opinion on COVID-19 vaccination was carried out using the supporting vector machine method without and with lexicon-based features. The manual labeling data used were 2981 tweets. The results of the classification of public opinion on COVID-19 vaccination in Indonesia with a supporting vector machine without the lexicon feature obtained accuracy, g-mean and AUC of 83%, 50% and 61.35%, respectively. Meanwhile, with lexicon-based features, the performance of the supporting vector machine method for classifying public opinion on COVID-19 vaccination in Indonesia obtained accuracy, g-mean and AUC of 90%, 86.63% and 87%, respectively. Based on these results, the performance of the supporting vector machine method with lexicon-based features provides better results for the performance of classifying of public opinion on COVID-19 vaccination compared to supporting vector machines without lexicon-based features.

Keywords

Analisis Sentimen, Fitur Berbasis Leksikon, Mesin Vektor Pendukung, Vaksinasi COVID-19

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References

Bekkar, M., Djemaa, H.K. and Alitouche, T.A. (2013), “Evaluation measures for models assessment over imbalanced data sets”, J Inf Eng Appl, Vol. 3 No. 10.

Buntoro, G.A., Adji, T.B. and Purnamasari, A.E. (2014), “Sentiment Analysis Twitter dengan Kombinasi Lexicon Based dan Double Propagation”, The 6th Conference on Information Technology and Electrical Engineering (CITEE), pp. 39–43.

Dhawan, V. and Zanini, N. (2014), “Big data and social media analytics”, Research Matters: A Cambridge Assessment Publication, Vol. 18 No. 7, pp. 36–41.

Gokgoz, E. and Subasi, A. (2015), “Comparison of decision tree algorithms for EMG signal classification using DWT”, Biomedical Signal Processing and Control, Elsevier, Vol. 18, pp. 138–144.

Gopi, A.P., Jyothi, R., Narayana, V.L. and Sandeep, K.S. (2020), “Classification of tweets data based on polarity using improved RBF kernel of SVM”, International Journal of Information Technology, Springer, pp. 1–16.

Kurniawan, A. and Indriati, S.A. (2019), “Analisis Sentimen Opini Film Menggunakan Metode Naïve Bayes dan Lexicon Based Features”, Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer E-ISSN, Vol. 2548, p. 964X.

Kurniawan, T. (2017), “Implementasi Text Mining Pada Analisis Sentimen Pengguna Twitter terhadap Media Mainstream Menggunakan Naïve Bayes Classifier dan Support Vector Machine”, Institut Teknologi Sepuluh Nopember.

Liu, B. (2012), “Sentiment analysis and opinion mining”, Synthesis Lectures on Human Language Technologies, Morgan & Claypool Publishers, Vol. 5 No. 1, pp. 1–167.

Nugroho, A.S., Witarto, A.B. and Handoko, D. (2003), “Support vector machine”, Proceeding Indones. Sci. Meeting Cent. Japan.

Nguyen, G. H., Abdesselam, B. & Son, P. L. (2009). Learning pattern classification tasks with imbalanced data sets. Pattern recognition, pp. 193-208.

Pisner, D.A. and Schnyer, D.M. (2020), “Support vector machine”, Machine Learning, Elsevier, pp. 101–121.

Sari, P.D. (2017), “Analisis Credit Scoring Menggunakan Regresi Logistik LASSO dan Support Vector Machine (SVM).”, Bogor Agricultural University (IPB).

Septian, J.A., Fachrudin, T.M. and Nugroho, A. (2019), “Analisis Sentimen Pengguna Twitter Terhadap Polemik Persepakbolaan Indonesia Menggunakan Pembobotan TF-IDF dan K-Nearest Neighbor”, INSYST: Journal of Intelligent System and Computation, Vol. 1 No. 1, pp. 43–49.

Vidya, N.A., Fanany, M.I. and Budi, I. (2015), “Twitter sentiment to analyze net brand reputation of mobile phone providers”, Procedia Computer Science, Elsevier, Vol. 72, pp. 519–526.

Wahid, D.H. and Azhari, S.N. (2016), “Peringkasan sentimen ekstraktif di twitter menggunakan hybrid TF-IDF dan cosine similarity”, IJCCS (Indonesian Journal of Computing and Cybernetics Systems), Vol. 10 No. 2, pp. 207–218.

Williams, G. (2011), Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery, Springer Science & Business Media.

DOI: https://doi.org/10.34312/jjps.v3i2.16663

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