Comparison of Naïve Bayes Classifier and Support Vector Machine for sentiment analysis on civil military relations conflict among Rohingya refugees as recommendation for defense policy making
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Aug 16, 2024
Keywords:
Civil military conflict, Naïve bayes classifier, Rohingya refugees, Sentiment analysis, Support vector machine
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Abstract
This research focuses on the evaluating the performance of various sentiment analysis techniques using the Naive Bayes Classifier and Support Vector Machine in identifying civil-military conflicts among Rohingya refugees. The goal is to assist leaders in formulating defense policies. This research uses text data from news sources on Twitter, with a total of 5018 data that have been processed to become clean data, then divided into 1004 test data and 4018 training data to be classified using the Support Vector Machine and Naive Bayes methods. This research analyzes the sentiment and polarity of public opinion related to the issues that occur in this situation. The results of the sentiment analysis from the two methods are then classified using the Support Vector Machine and Naive Bayes methods, and then compared to determine which method is more effective in capturing the complex dynamics of sentiment. The findings of this research indicate that the Support Vector Machine method has a higher accuracy in identifying sentiments related to the civil-military conflict among Rohingya refugees, with an accuracy of 87.95%, compared to the Naive Bayes Classifier with an accuracy of 85.16%. The analysis results in the form of frequently occurring words in the true positive word cloud, namely apology, human, angry, and solidarity, are handed over to experts to be formulated into recommendation sentences and can be used to assist in the formulation of policies for defense decision-makers in more effectively addressing the Rohingya refugee issue.
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Putri, N. S., Saragih, H., & Heikhmakhtiar, A. K. (2024). Comparison of Naïve Bayes Classifier and Support Vector Machine for sentiment analysis on civil military relations conflict among Rohingya refugees as recommendation for defense policy making. Journal of Intelligent Decision Support System (IDSS), 7(3), 227-235. https://doi.org/10.35335/idss.v7i3.255
References
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Harun, N. A., Huspi, S. H., & A. Iahad, N. (2023). Question Classification for Helpdesk Support Forum Using Support Vector Machine and Naïve Bayes Algorithm. International Journal of Innovative Computing, 13(1), 37–45. https://doi.org/10.11113/ijic.v13n1.388
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Nugraha, A. F. (2022). Naïve Bayes dan Support Vector Machine Berbasis PSO untuk Seleksi Fitur pada Sentiment Analysis. Innovation in Research of Informatics (INNOVATICS), 4(2), 56–61. https://doi.org/10.37058/innovatics.v4i2.5291
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Santoso, I., Oktora, S. I., Muchlisoh, S., & Pasaribu, E. (2023). Social Network Analysis untuk Identifikasi Pengguna Twitter Berpengaruh pada Topik Bencana Gempa dan Tsunami di Indonesia. Jurnal Edukasi dan Penelitian Informatika (JEPIN), 9(1), 115. https://doi.org/10.26418/jp.v9i1.62211
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Valero-Carreras, D., Alcaraz, J., & Landete, M. (2023). Comparing two SVM models through different metrics based on the confusion matrix. Computers and Operations Research, 152(April 2022), 106131. https://doi.org/10.1016/j.cor.2022.106131
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Yacouby, R., & Axman, D. (2020). Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models. 79–91. https://doi.org/10.18653/v1/2020.eval4nlp-1.9
Yam, J. H., & Taufik, R. (2021). Hipotesis Penelitian Kuantitatif. Perspektif : Jurnal Ilmu Administrasi, 3(2), 96–102. https://doi.org/10.33592/perspektif.v3i2.1540
Elsayed, F. E. (2020). Social Media Role in Relieving the Rohingya Humanitarian Crisis. New Media and Mass Communication, 87, 28–48. https://doi.org/10.7176/nmmc/87-04
Fikri, M. I., Sabrila, T. S., Azhar, Y., & Malang, U. M. (2020). Comparison of the Naïve Bayes Method and Support Vector Machine on Twitter Sentiment Analysis. SMATIKA Jurnal: STIKI Informatika Jurnal, 10(2), 71–76.
Haradhan, M. (2020). Quantitative Research: A Successful Investigation in Natural and Social Sciences. Journal of Economic Development, Environment and People, 9(4), 52–79. https://mpra.ub.uni-muenchen.de/105149/
Harun, N. A., Huspi, S. H., & A. Iahad, N. (2023). Question Classification for Helpdesk Support Forum Using Support Vector Machine and Naïve Bayes Algorithm. International Journal of Innovative Computing, 13(1), 37–45. https://doi.org/10.11113/ijic.v13n1.388
Kristiyanti, D. A., Umam, A. H., Wahyudi, M., Amin, R., & Marlinda, L. (2019). Comparison of SVM Naïve Bayes Algorithm for Sentiment Analysis Toward West Java Governor Candidate Period 2018-2023 Based on Public Opinion on Twitter. 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018, Citsm 2018, 1–6. https://doi.org/10.1109/CITSM.2018.8674352
Kusumawati, R., D’Arofah, A., & Pramana, P. A. (2019). Comparison Performance of Naive Bayes Classifier and Support Vector Machine Algorithm for Twitter’s Classification of Tokopedia Services. Journal of Physics: Conference Series, 1320(1), 1–11. https://doi.org/10.1088/1742-6596/1320/1/012016
Lestari, M. I., & Anggraeni, D. (2021). Analisis dampak sentimen masyarakat selama pandemi covid-19 terhadap kurs rupiah (Studi kasus pandemi covid-19 di Indonesia). Jurnal EMBA, 9(1), 1–14.
Lestari, U., Romadhani, T., Suraya, S., & Fatkhiyah, E. (2022). Sentiment Analysis for Extracting Student Opinion Data on Higher Education Services Using the Naive Bayes Classifier and Support Vector Machine Methods (Case Study Akprind Institute of Science and Technology Yogyakarta). Jurnal TAM (Technology Acceptance Model), 13(1), 51. https://doi.org/10.56327/jurnaltam.v13i1.1220
Malsi, E., & Jatikusumo, D. (2022). Analisis Sentimen Terhadap Ulasan Aplikasi FLIP.ID Menggunakan Klasifikasi Naïve Bayes. Jurnal Informatika dan Teknologi Informasi, 18(1), 1–11.
Mehta, P., & Pandya, S. (2020). A review on sentiment analysis methodologies, practices and applications. International Journal of Scientific and Technology Research, 9(2), 601–609.
Nada, D. D., Soehardjoepri, S., & Atok, R. M. (2023). Perbandingan Analisis Sentimen Mengenai BPJS pada Media Sosial Twitter Menggunakan Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM). Jurnal Sains dan Seni ITS, 11(6). https://doi.org/10.12962/j23373520.v11i6.96330
Narkhede, S. (2019). Understanding AUC - ROC Curve. 6–11.
Normawati, D., & Prayogi, S. A. (2021). Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter. Jurnal Sains Komputer & Informatika (J-SAKTI, 5(2), 697–711.
Nugraha, A. F. (2022). Naïve Bayes dan Support Vector Machine Berbasis PSO untuk Seleksi Fitur pada Sentiment Analysis. Innovation in Research of Informatics (INNOVATICS), 4(2), 56–61. https://doi.org/10.37058/innovatics.v4i2.5291
Patel, T. S., Patel, D. P., Sanyal, M., & Shrivastav, P. S. (2023). Prediction of Heart Disease and Survivability using Support Vector Machine and Naive Bayes Algorithm. M. http://dx.doi.org/10.1101/2023.06.09.543776%0Ahttps://syndication.highwire.org/content/doi/10.1101/2023.06.09.543776
Rahayu, A. S., Fauzi, A., & Rahmat, R. (2022). Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Pada Analisis Sentimen Spotify. Jurnal Sistem Komputer dan Informatika (JSON), 4(2), 349. https://doi.org/10.30865/json.v4i2.5398
Riyadi, S., Siregar, M. M., Margolang, K. fadhli F., & Andriani, K. (2022). Analysis of Svm and Naive Bayes Algorithm in Classification of Nad Loans in Save and Loan Cooperatives. JURTEKSI (Jurnal Teknologi dan Sistem Informasi), 8(3), 261–270. https://doi.org/10.33330/jurteksi.v8i3.1483
Santoso, I., Oktora, S. I., Muchlisoh, S., & Pasaribu, E. (2023). Social Network Analysis untuk Identifikasi Pengguna Twitter Berpengaruh pada Topik Bencana Gempa dan Tsunami di Indonesia. Jurnal Edukasi dan Penelitian Informatika (JEPIN), 9(1), 115. https://doi.org/10.26418/jp.v9i1.62211
Saputra, A., Subing, M., & Pratama, R. (2023). Perbandingan Metode Naïve Bayes Classifier Dan Support Vector Machine Untuk Analisis Sentimen Pengguna Twitter Mengenai Piala Dunia Fifa 2022. Teknomatika, 13(01), 22–31.
Sriyano, C. S., & Setiawan, E. B. (2021). Pendeteksian Berita Hoax Menggunakan Naive Bayes Multinomial Pada Twitter dengan Fitur Pembobotan TF-IDF. e-Proceeding of Engineering : Vol.8, No.2, 8(2), 3396–3405.
Valero-Carreras, D., Alcaraz, J., & Landete, M. (2023). Comparing two SVM models through different metrics based on the confusion matrix. Computers and Operations Research, 152(April 2022), 106131. https://doi.org/10.1016/j.cor.2022.106131
Validation, C., Galih, K. S. P., Galih, K. S. P., Validation, C., & Kunci, K. (2023). 1 1 , 2*. 5(2), 294–300.
Yacouby, R., & Axman, D. (2020). Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models. 79–91. https://doi.org/10.18653/v1/2020.eval4nlp-1.9
Yam, J. H., & Taufik, R. (2021). Hipotesis Penelitian Kuantitatif. Perspektif : Jurnal Ilmu Administrasi, 3(2), 96–102. https://doi.org/10.33592/perspektif.v3i2.1540
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