Analisis Sentimen Masyarakat Terhadap Isu Migrasi Rohingya Ke Indonesia

  • Ulfa Kurniasih Ekonomi Syariah, Ekonomi dan Bisnis Islam, UIN K.H. Abdurrahman Wahid
  • Akrim Teguh Suseno Informatika, Sains dan Teknologi, ITSNU Pekalongan
Keywords: sentiment analysis, refugees, rohingya, naive bayes, decision tree

Abstract

The phenomenon of the Rohingya refugee exodus is something that attracts a lot of public interest on social media. This study aims to see how the public represents the phenomenon of the presence of Rohingya refugees in Indonesia on X social media with the Decision tree 5.0 algorithm and Naive bayes with the keyword "rohingya". The results of the study showed that negative sentiment is still dominant on X social media with an average of 49.5%, positive sentiment 18.5% and neutral sentiment 27%. For validity testing using the K-Fold Validation method, it shows that the naïve Bayes algorithm has a better level of accuracy with an accuracy level of 83% while the decision tree only has an accuracy level of 78%. The results of the study indicate that Indonesian people through X social media still tend to give a negative attitude towards the presence of Rohingya refugees in Indonesia.

Downloads

Download data is not yet available.

References

W. Wasalmi, “Sejarah Konflik Muslim Rohingya,” vol. 4, no. 2, pp. 2964–5476, 2023, doi: 10.59059/tarim.v4i2.217.

Setwapres, “Wapres Nilai Pengungsi Rohingya sebagai Masalah Kemanusiaan, Harus Diatasi,” https://www.wapresri.go.id/wapres-nilai-pengungsi-rohingya-sebagai-masalah-kemanusiaan-harus-diatasi-bersama/, Dec. 05, 2023.

A. W. Tambunan, “Kerja Sama UNHCR dan IOM dalam Menangani Pencari Suaka dan Pengungsi Etnis Rohingya di Indonesia,” Journal of International Relations, vol. 5, no. 2, pp. 341–350, 2019, [Online]. Available: http://ejournal-s1.undip.ac.id/index.php/jihiWebsite:http://www.fisip.undip.ac.id

E. Albert and L. Maizland, “The Rohingya Crisis,” Council on Foreign Relations., Jan. 2020.

I. Chaturvedi, Sentiment Analysis and Opinion Mining: Theoretical and Practical Perspectives. Springer, 2021.

W. , Medhat, A. Hassan, and H. Korashy, “Sentiment Analysis Algorithms and Applications: A Survey,” Journal of Computer Science., 2021.

C. C. Aggarwal, Machine Learning for Text. Springer, 2018.

T. Jo, Text mining, vol. 45. Cham, Switzerland: Springer International Publishing, 2019.

H. Alhuzali, T. Zhang, and S. Ananiadou, “Emotions and topics expressed on Twitter during the COVID-19 pandemic in the United Kingdom: Comparative geolocation and text mining analysis.,” J Med Internet Res, vol. 24, no. 10, 2022.

S. Demirel, E. Kahraman, and U. Gündüz, “A text mining analysis of the change in status of the Hagia Sophia on Twitter: the political discourse and its reflections on the public opinion,” Atl J Commun, pp. 1–28, 2022.

T. Siddiqui, “Sarcasm detection from twitter database using text mining algorithms. Turkish Journal of Computer and Mathematics Education,” TURCOMAT, vol. 12, no. 11, 2021.

M. Anandarajan, C. Hill, and T. Nolan, Text preprocessing. Practical text analytics:Maximizing the value of text data. Springer, 2019.

M. Lamba and M. Madhusudhan, Text Mining for Information Professionals: An Uncharted Territory. Springer, 2022.

A. Aziza and S. Rani, “Klasifikasi Sentimen Radikalisme dalam Konten Dakwah Radikal Indonesia melalui Media Sosial Twitter dengan Menggunakan Analisis Sentimen dan Text Mining,” 2023.

A. Lubis and M. Y. H. Setyawan, “Analisis Sentimen Terhadap Aplikasi Pospay Menggunakan Algoritma Support Vector Machine dan Naive Bayes,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 6, no. 3, Jul. 2024, doi: 10.47233/jteksis.v6i3.1310.

M. G. Wibowo and I. Pratama, “Sentiment Analysis of the Digital Population Identity Application Reviews Using the Support Vector Machine Method,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 6, no. 4, Oct. 2024, doi: 10.47233/jteksis.v6i4.1552.

H. A. Chowdhury, T. A. Nibir, and M. S. Islam, “Sentiment Analysis of Comments on Rohingya Movement with Support Vector Machine,” 2018. [Online]. Available: https://www.researchgate.net/publication/324007412

N. Rochmawati and S. C. Wibawa, “Opinion Analysis on Rohingya using Twitter Data,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Apr. 2018. doi: 10.1088/1757-899X/336/1/012013.

D. Toresa et al., “Perbandingan Algoritma C4.5 Dan Naïve Bayes Untuk Mengukur Tingkat Kepuasan Mahasiswa Dalam Penggunaan Edlink,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 5, no. 3, Jul. 2023, doi: 10.47233/jteksis.v5i3.855.

I. R. Hendrawan, E. Utami, and A. D. Hartanto, “Analisis Perbandingan Metode Tf-Idf dan Word2vec pada Klasifikasi Teks Sentimen Masyarakat Terhadap Produk Lokal di Indonesia,” Jurnal Smart Comp, vol. 11, no. 3, 2022.

Published
2025-02-03
How to Cite
Kurniasih, U., & Suseno, A. (2025). Analisis Sentimen Masyarakat Terhadap Isu Migrasi Rohingya Ke Indonesia. Jurnal Teknologi Dan Sistem Informasi Bisnis, 7(1), 199-207. https://doi.org/10.47233/jteksis.v7i1.1815
Section
Articles