Sentiment Analysis on Tweets of Kanjuruhan Tragedy Using Deep Learning IndoBERTweet
DOI:
https://doi.org/10.30865/mib.v7i3.6115Keywords:
Kanjuruhan, Sentiment Analysis, Twitter, IndoBERTweet, NaïveAbstract
The incident that occurred in Indonesian football at the Kanjuruhan Stadium was caused by unscrupulous supporters who entered the field and unscrupulous officers who fired tear gas into the stands. With this incident, many responses and opinions were given by the Indonesian people through social media Twitter in the form of positive, negative, and neutral opinions. This difference in opinion occurred because of the many victims who died or were injured, with many supporters who did not like the actions taken by the authorities during the riots. With this incident, the government must make decisions to ease the concerns of the community. Therefore, research will be conducted to analyze the sentiment of public opinion regarding the Kanjuruhan tragedy using the IndoBERTweet method with a comparison using naive Bayes. The results of this study using the IndoBERTweet method get better results than naive Bayes method. With the results of the IndoBERTweet method 88% accuracy, 82% precision value, 85% recall value, and 84% f1-score value, naive the Naive Bayes results are 62% accuracy, 59% Precision Value, 61% Recall Value, and f1-Score of 59%.
References
S. A. Azzahra, “Human Rights Violation in The Rioting of Supporters: Case of Kanjuruhan Football Stampede.†[Online]. Available: https://news.detik.com/berita/d-6331229/total-korban-jiwa-tragedi-kanjuruhan-dan-rinciannya-data-5-
Y. Mogot, E. Agus, W. B. Riset, I. Nasional, and O. Solihin, “GERAKAN SOSIAL VIRTUAL MENYIKAPI TRAGEDI KANJURUHAN,†Dewantara : Jurnal Pendidikan Sosial Humaniora, vol. 1, no. 4, 2022.
F. Fitriansyah Program Studi Penyiaran Akademi Komunikasi BSI Jakarta and C. Sitasi, “Efek Komunikasi Massa Pada Khalayak (Studi Deskriptif Penggunaan Media Sosial dalam Membentuk Perilaku Remaja),†Cakrawala, vol. 18, no. 2, pp. 171–178, 2018, doi: 10.31294/jc.v18i2.
E. Dwi and S. Watie, “Komunikasi dan Media Sosial (Communications and Social Media),†2011. [Online]. Available: http://id.wikipedia.org/wiki/Media_sos
R. Ferdiana, F. Jatmiko, D. D. Purwanti, A. Sekar, T. Ayu, and W. F. Dicka, “Dataset Indonesia untuk Analisis Sentimen,†2019.
L. Dey, S. Chakraborty, A. Biswas, B. Bose, and S. Tiwari, “Sentiment Analysis of Review Datasets using Naïve Bayes’ and K-NN Classifier.†[Online]. Available: www.imdb.com
I. R. Hidayat and W. Maharani, “General Depression Detection Analysis Using IndoBERT Method,†International Journal on Information and Communication Technology (IJoICT), vol. 8, no. 1, pp. 41–51, Aug. 2022, doi: 10.21108/ijoict.v8i1.634.
V. Rahmayanti Setyaning Nastiti and S. Basuki, “Klasifikasi Sinopsis Novel Menggunakan Metode Naïve Bayes Classifier,†vol. 1, no. 2, pp. 125–130, 2019.
S. Busono, “Optimasi Naive Bayes Menggunakan Algoritma Genetika Sebagai Seleksi Fitur Untuk Memprediksi Performa Siswa,†Jurnal Ilmiah Teknologi Informasi Asia, vol. 14, no. 1, 2020.
F. Fernández-MartÃnez, C. Luna-Jiménez, R. Kleinlein, D. Griol, Z. Callejas, and J. M. Montero, “Fine-Tuning BERT Models for Intent Recognition Using a Frequency Cut-Off Strategy for Domain-Specific Vocabulary Extension,†Applied Sciences (Switzerland), vol. 12, no. 3, Feb. 2022, doi: 10.3390/app12031610.
B. Juarto, “International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING Indonesian News Classification Using IndoBert.†[Online]. Available: www.ijisae.org
D. Jurafsky and J. H. Martin, “Speech and Language Processing An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition Third Edition draft Summary of Contents.â€
J. Engel et al., “Breaking with trends in pre-processing?,†TrAC - Trends in Analytical Chemistry, vol. 50. Elsevier B.V., pp. 96–106, 2013. doi: 10.1016/j.trac.2013.04.015.
M. A. Rosid, A. S. Fitrani, I. R. I. Astutik, N. I. Mulloh, and H. A. Gozali, “Improving Text Preprocessing for Student Complaint Document Classification Using Sastrawi,†in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Jul. 2020. doi: 10.1088/1757-899X/874/1/012017.
A. Y. S. H. W. D. F. A. B. N. Y. Kuncahyo Setyo Nugroho, “BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews,†Association for Computing Machinery, vol. 21, no. 6, pp. 258–264, Sep. 2021.
W. Shi and V. Demberg, “Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains.†[Online]. Available: https://github.com/google-research/
I. Rish, “An empirical study of the naive Bayes classifier.â€
“Duda_Pattern_classificationâ€.
H. Annur, “KLASIFIKASI MASYARAKAT MISKIN MENGGUNAKAN METODE NAÃVE BAYES,†2018.
I. Nawangsih, I. Melani, S. Fauziah, and A. I. Artikel, “PELITA TEKNOLOGI PREDIKSI PENGANGKATAN KARYAWAN DENGAN METODE ALGORITMA C5.0 (STUDI KASUS PT. MATARAM CAKRA BUANA AGUNG,†Jurnal Pelita Teknologi, vol. 16, no. 2, pp. 24–33, 2021.
D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,†2021.
D. Alita and A. Rahman, “Pendeteksian Sarkasme pada Proses Analisis Sentimen Menggunakan Random Forest Classifier,†2020.
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