PERFORMANCE OF SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE ON SUPPORT VECTOR MACHINE AND K-NEAREST NEIGHBOR FOR SENTIMENT ANALYSIS OF METAVERSE IN INDONESIA

Roy Antonio, Hironimus Leong

Abstract


The metaverse is one of the most discussed things on social media, Twitter in Indonesia. This view can be both positive and negative in Indonesian society, hence the need for sentiment analysis. However, creating a sentiment classification model with unbalanced data will reduce performance. For this reason, Synthetic Minority Oversampling is needed in Support Vector Machine and K-Nearest Neighbor algorithms. The results of Synthetic Minority Oversampling can improve the accuracy of the Support Vector Machine and K-Nearest Neighbor algorithms.


Keywords


Metaverse, Synthetic Minority Oversampling, Support Vector Machine, K-Nearest Neighbor

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References


Ö. AĞRALI and Ö. AYDIN, “Tweet Classification and Sentiment Analysis on Metaverse Related Messages,” Journal of Metaverse, vol. 1, no. 1, pp. 25–30, 2021.

A. Ahmad and W. Gata, “Sentimen Analisis Masyarakat Indonesia di Twitter Terkait Metaverse dengan Algoritma Support Vector Machine,” jtik, vol. 6, no. 4, pp. 548–555, Mar. 2022, doi: 10.35870/jtik.v6i4.569.

N. M. S. Hadna, P. I. Santosa, and W. W. Winarno, “Studi literatur tentang perbandingan metode untuk proses analisis sentimen di Twitter,” Semin. Nas. Teknol. Inf. dan Komun, vol. 2016, pp. 57–64, 2016.

A. T. J. Harjanta, “Preprocessing Text untuk Meminimalisir Kata yang Tidak Berarti dalam Proses Text Mining,” Jurnal Informatika Upgris, vol. 1, no. 1 Juni, 2015, [Online]. Available: http://journal.upgris.ac.id/index.php/JIU/article/view/804

D. Muhidin and A. Wibowo, “Perbandingan Kinerja Algoritma Support Vector Machine dan K-Nearest Neighbor Terhadap Analisis Sentimen Kebijakan New Normal,” STRING, vol. 5, no. 2, p. 153, Dec. 2020, doi: 10.30998/string.v5i2.6715.

M. R. A. Nasution and M. Hayaty, “Perbandingan Akurasi dan Waktu Proses Algoritma KNN dan SVM dalam Analisis Sentimen Twitter,” Jurnal Informatika, vol. 6, no. 2, pp. 226–235, 2019.

F. S. Pamungkas and I. Kharisudin, “Analisis Sentimen dengan SVM, NAIVE BAYES dan KNN untuk Studi Tanggapan Masyarakat Indonesia Terhadap Pandemi Covid-19 pada Media Sosial Twitter,” in PRISMA, Prosiding Seminar Nasional Matematika, 2021, vol. 4, pp. 628–634. [Online]. Available: https://journal.unnes.ac.id/sju/index.php/prisma/article/view/45038

M. W. Pertiwi, “Analisis sentimen opini publik mengenai sarana dan transportasi mudik tahun 2019 pada twitter menggunakan algoritma naïve bayes, neural network, KNN dan SVM,” Inti Nusa Mandiri, vol. 14, no. 1, pp. 27–32, 2019.

S. Tunca, B. SEZEN, and Y. S. BALCIOĞLU, “TWITTER ANALYSIS FOR METAVERSE LITERACY’, 4,” in INTERNATIONAL NEW YORK ACADEMIC RESEARCH CONGRESS, 2022. [Online]. Available: https://www.researchgate.net/profile/Sezai-Tunca2/publication/358045545_TWITTER_ANALYSIS_FOR_METAVERSE_LITERACY/link s/61ee6aed8d338833e38f33f5/TWITTER-ANALYSIS-FOR-METAVERSELITERACY.pdf

I. Zulfa and E. Winarko, “Sentimen Analisis Tweet Berbahasa Indonesia Dengan Deep Belief Network,” IJCCS, vol. 11, no. 2, p. 187, Jul. 2017, doi: 10.22146/ijccs.24716.




DOI: https://doi.org/10.24167/proxies.v6i2.12459

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