IMPLEMENTATION OF SUPPORT VECTOR MACHINE METHOD FOR SENTIMENT ANALYSIS TWEET OF HALAL LOGO CHANGE IN INDONESIA
Abstract
The Halal Product Assurance Agency (BPJPH) is an agency under the auspices of the Ministry of Religion with the task of ensuring the halalness of products in Indonesia. BPJPH has become a public concern after establishing the new halal logo. On February 10, 2022 the new halal logo was ratified by the Head of BPJPH, Muhammad Aqil Irham. This has become a topic of public discussion either directly or through social media, one of which is social media twitter. The number of opinion tweets about the change of the halal logo can be used as a data source to obtain information about public opinion on the change of the halal logo through sentiment analysis. Sentiment analysis can be done by machine learning approach, one of these is the SVM algorithm . In this research, oversampling and undersampling are applied to handle data that has an unbalanced sentiment class. The results showed that the Support Vector Machine (SVM) model using oversampling training data got the highest accuracy, recall, precision, and f1-score, namely 71% accuracy, 67% precision, 61% recall, and 61% f1-score while training using undersampling training data has the lowest performance, namely getting 56% accuracy, 51% precision, 57% recall, and 52% f1-score.References
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[4] detikcom, “Kenapa Logo Halal Diganti dan Hal-hal yang Perlu Diketahui soal Label Baru.” https://news.detik.com/berita/d-5983823/kenapa-logo-halal-diganti-dan-hal-hal-yang-perlu-diketahui-soal-label-baru (accessed Apr. 12, 2022).
[5] A. R. Kamalina, “Ada Logo Halal Baru, Ekonom: Standardisasi Sertifikasi Juga Harus Berubah - Ekonomi Bisnis.com.” https://ekonomi.bisnis.com/read/20220314/12/1510636/ada-logo-halal-baru-ekonom-standardisasi-sertifikasi-juga-harus-berubah (accessed Apr. 12, 2022).
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[8] R. D. Himawan and E. Eliyani, “Perbandingan Akurasi Analisis Sentimen Tweet terhadap Pemerintah Provinsi DKI Jakarta di Masa Pandemi,” J. Edukasi dan Penelit. Inform., vol. 7, no. 1, p. 58, Apr. 2021, doi: 10.26418/jp.v7i1.41728.
[9] R. H. Muhammadi, T. G. Laksana, and A. B. Arifa, “Combination of Support Vector Machine and Lexicon-Based Algorithm in Twitter Sentiment Analysis,” Khazanah Inform. J. Ilmu Komput. dan Inform. Vol. 8 No. 1 April 2022DO - 10.23917/khif.v8i1.15213 , Mar. 2022, [Online]. Available: https://journals.ums.ac.id/index.php/khif/article/view/15213
[10] M. Rathi, A. Malik, D. Varshney, R. Sharma, and S. Mendiratta, “Sentiment Analysis of Tweets Using Machine Learning Approach,” 2018 11th International Conference on Contemporary Computing, IC3 2018. 2018. doi: 10.1109/IC3.2018.8530517.
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[13] A. A. Mir, S. Rathinam, and S. Gul, “Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati,” Libr. Hi Tech, 2021, doi: 10.1108/LHT-08-2021-0261.
[14] J. Rejito, A. Atthariq, and A. S. Abdullah, “Application of text mining employing k-means algorithms for clustering tweets of Tokopedia,” Journal of Physics: Conference Series, vol. 1722, no. 1. 2021. doi: 10.1088/1742-6596/1722/1/012019.
[15] A. R. Lubis, M. K. M. Nasution, O. S. Sitompul, and E. M. Zamzami, “The effect of the TF-IDF algorithm in times series in forecasting word on social media,” Indones. J. Electr. Eng. Comput. Sci., vol. 22, no. 2, pp. 368–376, 2020, doi: 10.11591/ijeecs.v22.i2.pp368-376.
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Published
2023-07-14
How to Cite
[1]
Widi Afandi, Tri Ginanjar Laksana, and Nia Annisa Ferani Tanjung, “IMPLEMENTATION OF SUPPORT VECTOR MACHINE METHOD FOR SENTIMENT ANALYSIS TWEET OF HALAL LOGO CHANGE IN INDONESIA”, ELKOM, vol. 16, no. 1, pp. 44-52, Jul. 2023.
Section
Articles
