1. Home
  2. JUITA Vol. 11 No. 2, November 2023
  3. Parhusip

Sentiment Analysis of the Public Towards the Kanjuruhan Tragedy with the Support Vector Machine Method

Martin Parhusip, Sudianto Sudianto, Tri Ginanjar Laksana

Abstract


A tragedy occurred in the Indonesian football world during the Arema vs. Persebaya match on October 1, 2022, resulting in the loss of approximately 714 lives, including 131 fatalities and 583 injuries. The tragedy is believed to have been caused by tear gas in the spectator stands and the closure of exits at the Kanjuruhan stadium. This event sparked a diverse range of public responses on social media, which can be analyzed through sentiment analysis. In this study, we employed the Support Vector Machine (SVM) algorithm, known for its speed and accuracy in text classification, to process and analyze tweets from October 1 to 31, 2022, as well as YouTube comments related to the Kanjuruhan tragedy from October 1 to November 20, 2022. Among the different SVM kernels, the RBF kernel exhibited the highest accuracy, precision, recall, and F1 scores, reaching 76.40%, 75.74%, 76.40%, and 75.18% respectively, when predicting data with three labels. Furthermore, the RBF kernel showed the best performance for data with two labels, achieving the highest accuracy, precision, recall, and F1-Score, which increased to 81.54%, 81.56%, 81.54%, and 81.56%, respectively.


Keywords


analysis, classification, Kanjuruhan, sentiment, SVM

References


[1] “Update Data Korban Tragedi Kanjuruhan: 131 Meninggal, 583 Luka,” CNN Indonesia, 2022. https://www.cnnindonesia.com/nasional/20221009180425-20-858262/update-data-korban-tragedi-kanjuruhan-131-meninggal-583-luka (accessed Oct. 20, 2022).

[2] Vitorio Mantalean, “Komnas HAM Tegaskan Gas Air Mata Jadi Pemicu Jatuhnya Ratusan Korban Tragedi Kanjuruhan,” Kompas.com, 2022. https://nasional.kompas.com/read/2022/10/12/15044611/komnas-ham-tegaskan-gas-air-mata-jadi-pemicu-jatuhnya-ratusan-korban-tragedi (accessed Oct. 21, 2022).

[3] K. (Kamus B. B. Indonesia), “Sentimen,” 2016. https://kbbi.kemdikbud.go.id/entri/sentimen (accessed Oct. 20, 2022).

[4] H. Ghorbel and D. Jacot, “Sentiment analysis of french movie reviews,” Studies in Computational Intelligence, vol. 361, pp. 97–108, 2011, doi: 10.1007/978-3-642-21384-7_7.

[5] G. Isabelle, W. Maharani, and I. Asror, “Analysis on Opinion Mining Using Combining Lexicon-Based Method and Multinomial Naïve Bayes,” vol. 2, no. IcoIESE 2018, pp. 214–219, 2019, doi: 10.2991/icoiese-18.2019.38.

[6] C. S. G. Khoo and S. B. Johnkhan, “Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons,” Journal of Information Science, vol. 44, no. 4, pp. 491–511, 2018, doi: 10.1177/0165551517703514.

[7] P. Chauhan, “Sentiment Analysis: A Comparative Study of Supervised Machine Learning Algorithms Using Rapid miner,” International Journal for Research in Applied Science and Engineering Technology, vol. V, no. XI, pp. 80–89, 2017, doi: 10.22214/ijraset.2017.11011.

[8] C. Villavicencio, J. J. Macrohon, X. A. Inbaraj, J. H. Jeng, and J. G. Hsieh, “Twitter sentiment analysis towards covid-19 vaccines in the Philippines using naïve bayes,” Information (Switzerland), vol. 12, no. 5, May 2021, doi: 10.3390/info12050204.

[9] T. Hariguna, W. Maulana Baihaqi, and A. Nurwanti, “Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm,” International Journal of Informatics and Information Systems, vol. 2, no. 2, pp. 48–55, 2019.

[10] Y. Al Amrani, M. Lazaar, and K. E. El Kadirp, “Random forest and support vector machine based hybrid approach to sentiment analysis,” in Procedia Computer Science, Elsevier B.V., 2018, pp. 511–520. doi: 10.1016/j.procs.2018.01.150.

[11] R. Ren, D. D. Wu, and D. D. Wu, “Forecasting stock market movement direction using sentiment analysis and support vector machine,” IEEE Systems Journal, vol. 13, no. 1, pp. 760–770, 2019, doi: 10.1109/JSYST.2018.2794462.

[12] M. Wongkar, “Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler: Twitter,” Proceedings of 2019 4th International Conference on Informatics and Computing, ICIC 2019. 2019. doi: 10.1109/ICIC47613.2019.8985884.

[13] A. Borg and M. Boldt, “Using VADER sentiment and SVM for predicting customer response sentiment,” Expert Systems with Applications, vol. 162, p. 113746, 2020, doi: 10.1016/j.eswa.2020.113746.

[14] M. Ahmad, S. Aftab, M. S. Bashir, and N. Hameed, “Sentiment analysis using SVM: A systematic literature review,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 2, pp. 182–188, 2018, doi: 10.14569/IJACSA.2018.090226.

[15] F. Firmansyah et al., “Comparing Sentiment Analysis of Indonesian Presidential Election 2019 with Support Vector Machine and K-Nearest Neighbor Algorithm,” in 6th International Conference on Computing, Engineering, and Design, ICCED 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020. doi: 10.1109/ICCED51276.2020.9415767.

[16] M. A. Fauzi, “Random forest approach fo sentiment analysis in Indonesian language,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 12, no. 1, pp. 46–50, Oct. 2018, doi: 10.11591/ijeecs.v12.i1.pp46-50.

[17] “Support Vector Machine Algorithm.” https://www.javatpoint.com/machine-learning-support-vector-machine-algorithm (accessed Nov. 12, 2022).

[18] S. Sudianto, P. Wahyuningtias, H. W. Utami, U. A. Raihan, and H. N. Hanifah, “Comparison Of Random Forest And Support Vector Machine Methods On Twitter Sentiment Analysis ( Case Study : Internet Selebgram Rachel Vennya Escape From Quarantine ) Perbandingan Metode Random Forest Dan Support Vector Machine Pada Analisis Sentimen Twitt,” Jutif, vol. 3, no. 1, pp. 141–145, 2022.

[19] S. Sudianto, A. D. Sripamuji, I. R. Ramadhanti, R. R. Amalia, J. Saputra, and B. Prihatnowo, “Penerapan Algoritma Support Vector Machine dan Multi-Layer Perceptron pada Klasifikasi Topik Berita,” Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI, vol. 11, no. 2, pp. 84–91, 2022.

[20] W. Wongso, “Indonesian RoBERTa Base Sentiment Classifier,” 2021. https://huggingface.co/w11wo/indonesian-roberta-base-sentiment-classifier (accessed Nov. 10, 2022).

[21] W. Afandi, S. N. Saputro, A. M. Kusumaningrum, H. Ardiansyah, M. H. Kafabi, and S. Sudianto, “Klasifikasi Judul Berita Clickbait menggunakan RNN-LSTM,” Jurnal Pengembangan IT, vol. 7, no. 2, pp. 85–89, 2022.

[22] S. Sudianto, J. A. Marseli, N. Nugroho, R. W. A. Rumpoko, and Z. Akhmad, “Comparison of Support Vector Machines and K-Nearest Neighbor Algorithm Analysis of Spam Comments on YouTube Covid Omicron,” JTI, vol. 15, no. 2, pp. 110–118, doi: https://doi.org/10.15408/jti.v15i2.24996.

[23] M. Mahrus Zain, R. Nathamael Simbolon, H. Sulung, and Z. Anwar, “Analisis Sentimen Pendapat Masyarakat Mengenai Vaksin Covid-19 Pada Media Sosial Twitter dengan Robustly Optimized BERT Pretraining Approach,” Jurnal Komputer Terapan, vol. 7, no. Vol. 7 No. 2 (2021), pp. 280–289, 2021, doi: 10.35143/jkt.v7i2.4782.

[24] Y. Chen and Z. Zhang, “Research on text sentiment analysis based on CNNs and SVM,” Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018. pp. 2731–2734, 2018. doi: 10.1109/ICIEA.2018.8398173.

[25] S. Chandra Ayunda Apta, N. Trivetisia, N. A. Winanti, D. P. Martiyaningsih, T. W. Utami, and S. Sudianto, “Analisis Komparasi Algoritma Machine Learning untuk Sentiment Analysis (Studi Kasus: Komentar YouTube ‘Kekerasan Seksual’),” Jurnal Pengembangan IT, vol. 7, no. 2, pp. 80–84, 2022.

[26] S. Sudianto, “Analisis Kinerja Algoritma Machine Learning Untuk Klasifikasi Emosi,” vol. 4, no. 2, pp. 1027–1034, 2022, doi: 10.47065/bits.v4i2.2261.

[27] P. H. Prastyo, I. Ardiyanto, and R. Hidayat, “Indonesian Sentiment Analysis: An Experimental Study of Four Kernel Functions on SVM Algorithm with TF-IDF,” in 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020. doi: 10.1109/ICDABI51230.2020.9325685.

[28] K. R. SPORNE, “the Phylogenetic Classification of the Angiosperms,” Biological Reviews, vol. 31, no. 1, pp. 1–29, 1956, doi: 10.1111/j.1469-185X.1956.tb01550.x.


Full Text: PDF

DOI: 10.30595/juita.v11i2.17405

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2579-8901