SENTIMEN ANALISIS TERHADAP KEBIJAKAN PEMBELAJARAN JARAK JAUH SELAMA PANDEMI COVID-19 MENGGUNAKAN ALGORITMA NAÏVE BAYES

Darsono Nababan

Abstract


Social media is a means to convey aspirations directly, but every aspiration is from social media users. Everyone who expresses opinions on social media contains positive, negative, and neutral sentiments. The implementation of the Ministry of Education and Culture's policy on the implementation of distance learning policies during the pandemic COVID-19 received various responses from the people of Indonesia. neutral as many as 894 comments, then 52 comments with negative sentiment, and 32 comments with positive sentiment with an accuracy value of 98.79%


Keywords


Covid-19; Education; Ministry of Education and Culture; PJJ

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References


Kementerian Pendidikan dan Kebudayaan Republik Indonesia. 2020. Surat Edaran Nomor 4 tahun 2020 Tentang Pelaksanaan Pendidikan dalam Masa Darurat Covid-19. https://kemdikbud.go.id/main/blog/2020/03/mendikbud-terbitkan-se-tentang-pelaksanaan-pendidikan-dalam-masa-darurat-covid-19.

H. Tuhuteru, ‘Analisis Sentimen Masyarakat Terhadap Pembatasan Sosial Berksala Besar Menggunakan Algoritma Support Vector Machine’, Inf. Syst. Dev., vol. 5, no. 2, pp. 7–13, 2020.

V. Vyas and V. Uma, ‘An Extensive study of Sentiment Analysis tools and Binary Classification of tweets using Rapid Miner’, Procedia Comput. Sci., vol. 125, pp. 329–335, 2018.

P. Algoritma, ‘Analisa Sentimen Menggunakan Naïve Bayes Untuk Mengetahui Presentase Komentar Pada Aplikasi Go-Jek’, no. 128..

Laksana, J. and Purwarianti, A., 2014, August. Indonesian Twitter text authority classification for government in Bandung. In 2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA) (pp. 129-134). IEEE..

Liu, B., 2012. Sentiment Analysis and OpinionMining. [e-book]. Chicago: Morgan & Claypool Publisher..

A. Kumar and T. M. Sebastian, ‘Sentiment Analysis: A Perspective on its Past, Present and Future’, Int. J. Intell. Syst. Appl., vol. 4, no. 10, pp. 1–14, 2012.

Ling, Juen, I Putu Eka N. Kencana, and Tjokorda Bagus Oka. (2014) “Analisis Sentimen menggunakan Metode Naïve Bayes Classifier dengan Seleksi Fitur Chi Square [Title in English: Sentiment Analysis Using Naïve Bayes Classifier Method with Feature Selection of Chi.

Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In Mining text data (pp. 415-463). Springer, Boston, MA.

Liu, B. (2010). Sentiment analysis: A multi-faceted problem. IEEE Intelligent Systems, 25(3), 76-80.

Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253.

Cambria, E., Schuller, B., Liu, B., Wang, H., & Havasi, C. (2013). Knowledge-based approaches to concept-level sentiment analysis. IEEE intelligent systems, 28(2), 12-14.

Zhang, L., Ghosh, R., Dekhil, M., Hsu, M., & Liu, B. (2011). Combining lexicon-based and learning-based methods for Twitter sentiment analysis. HP Laboratories, Technical Report HPL-2011, 8




DOI: https://doi.org/10.15408/jti.v14i1.17002 Abstract - 0 PDF - 0

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