SENTIMENT ANALYSIS OF MERDEKA BELAJAR KAMPUS MERDEKA POLICY USING SUPPORT VECTOR MACHINE WITH WORD2VEC

  • Nurul Rezki PT. Kioser Teknologi Indonesia, Indonesia
  • Sri Astuti Thamrin Department of Statistics, Faculty of Mathematics and Natural Sciences, University of Hasanuddin, Indonesia https://orcid.org/0000-0002-2512-0266
  • Siswanto Siswanto Department of Statistics, Faculty of Mathematics and Natural Sciences, University of Hasanuddin, Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss1pp0481-0486
Keywords: Merdeka Belajar Kampus Merdeka Policy, Sentiment Analysis, Support Vector Machine, Twitter, Word2Vec

Abstract

Sentiment analysis is a data text analysis that classifies data into positive and negative sentiments. This study aims to obtain the results of sentiment classification related to Merdeka Belajar Kampus Merdeka policy on Twitter using support vector machine algorithm with Word2Vec feature extraction. Support Vector Machine is a classification algorithm that separates data classes using the optimum hyperplane. Text data used in sentiment analysis must change its numerical form by performing feature extraction. In this study, the feature extraction used is Word2Vec which represents words in vector form. Data in this study are tweets with the keyword "Kampus Merdeka" uploaded on Twitter as many as 10000 tweets. After preprocessing text data, data used to analyze sentiment was 1579 tweets. Sentiment classification resulted in classification model accuracy 89.87%, precision 91.20%, recall 84.44% and F-Measure 87.68%. Classification sentiment using support vector machine with Word2Vec feature extraction in this study produces a good model.

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Published
2023-04-20
How to Cite
[1]
N. Rezki, S. Thamrin, and S. Siswanto, “SENTIMENT ANALYSIS OF MERDEKA BELAJAR KAMPUS MERDEKA POLICY USING SUPPORT VECTOR MACHINE WITH WORD2VEC”, BAREKENG: J. Math. & App., vol. 17, no. 1, pp. 0481-0486, Apr. 2023.
Issue
Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Section
Articles

Copyright (c) 2023 Nurul Rezki, Sri Astuti Thamrin, Siswanto Siswanto

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