Analisis Sentimen Publik Terhadap Pengungsi Rohingya di Indonesia dengan Metode Support Vector Machine dan Naïve Bayes
DOI:
https://doi.org/10.30865/mib.v8i2.7517Keywords:
Sentiment Analysis, Twitter, SVM, Naïve Bayes, RohingyaAbstract
The arrival of Rohingya refugees in Indonesia has become a highly controversial topic, eliciting various responses from the public. In this context, public sentiment analysis regarding the arrival of Rohingya refugees is crucial for understanding the dynamics of feelings, opinions, and attitudes of the Indonesian society towards this issue. In conducting public sentiment analysis, the selection of methods is crucial to ensure accurate results. The aim of this research is to conduct sentiment analysis regarding the arrival of Rohingya refugees using the Support Vector Machine (SVM) and Naive Bayes methods. The main focus is to evaluate public sentiment and compare the performance of both methods. Two common methods used in sentiment analysis are Support Vector Machine (SVM) and Naïve Bayes. This research utilized a dataset of 3350 tweets to conduct public sentiment analysis on the arrival of Rohingya refugees in Indonesia. In this study, data was divided using the 70:30 split method, where 70% of the data was used for model training and 30% for model testing. The research findings indicate that the SVM model has an accuracy of 76%, while the Naïve Bayes model has an accuracy of 70%. This suggests that the SVM model is better at predicting sentiments and has lower error rates compared to the Naïve Bayes model.References
A. Inayah and G. Karisma, “Respon Asean Terhadap Permasalahan Etnis Rohingya Di Myanmar,†J. Hub. Int. Indones., vol. 4, no. 1, pp. 31–44, 2022.
B. Budaya, “Dampak Kewarganegaraan Etnis Rohingya Di Myanmar Terhadap Pelanggaran Hak Asasi Manusia Dan Negara Sekitar,†J. Ilm. Huk., vol. 11, no. 1, pp. 106–120, 2018, [Online]. Available: https://maksigama.wisnuwardhana.ac.id/index.php/maksigama/article/view/44
D. G. S. Mangku, “Pemenuhan Hak Asasi Manusia Kepada Etnis Rohingya Di Myanmar,†Perspekt. Huk., vol. 21, no. 1, pp. 1–15, 2021.
A. P. Nardilasari, A. L. Hananto, S. S. Hilabi, T. Tukino, and B. Priyatna, “Analisis Sentimen Calon Presiden 2024 Menggunakan Algoritma SVM Pada Media Sosial Twitter,†JOINTECS (Journal Inf. Technol. Comput. Sci., vol. 8, no. 1, p. 11, 2023, doi: 10.31328/jointecs.v8i1.4265.
R. Maria, R. U. Umayah, S. Mahardinny, D. N. Kalana, and D. D. Saputra, “Analisis Sentimen Persepsi Masyarakat Terhadap Penggunaan Aplikasi My Pertamina Pada Media Sosial Twitter Menggunakan Metode Naïve Bayes Classifier,†J. Komput. Antart., vol. 1, no. 1, pp. 1–10, 2023, [Online]. Available: https://ejournal.mediaantartika.id/index.php/jka/article/view/1%0Ahttps://ejournal.mediaantartika.id/index.php/jka/article/download/1/1
C. F. Hasri and D. Alita, “Penerapan Metode Naïve Bayes Classifier Dan Support Vector Machine Pada Analisis Sentimen Terhadap Dampak Virus Corona Di Twitter,†J. Inform. dan Rekayasa Perangkat Lunak, vol. 3, no. 2, pp. 145–160, 2022.
S. Styawati, A. R. Isnain, N. Hendrastuty, and L. Andraini, “Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis,†J. Inform. J. Pengemb. IT, vol. 6, no. 1, pp. 56–60, 2021, doi: 10.30591/jpit.v6i1.3245.
Rina Noviana and Isram Rasal, “Penerapan Algoritma Naive Bayes Dan Svm Untuk Analisis Sentimen Boy Band Bts Pada Media Sosial Twitter,†J. Tek. dan Sci., vol. 2, no. 2, pp. 51–60, 2023, doi: 10.56127/jts.v2i2.791.
D. Atmajaya, A. Febrianti, and H. Darwis, “Metode SVM dan Naive Bayes untuk Analisis Sentimen ChatGPT di Twitter,†Indones. J. Comput. Sci., vol. 12, Aug. 2023, doi: 10.33022/ijcs.v12i4.3341.
I. S. K. Idris, Y. A. Mustofa, and I. A. Salihi, “Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine (SVM),†Jambura J. Electr. Electron. Eng., vol. 5, no. 1, pp. 32–35, 2023, doi: 10.37905/jjeee.v5i1.16830.
T. T. Widowati and M. Sadikin, “Analisis Sentimen Twitter terhadap Tokoh Publik dengan Algoritma Naive Bayes dan Support Vector Machine,†Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 11, no. 2, pp. 626–636, 2021, doi: 10.24176/simet.v11i2.4568.
Y. Ansori and K. F. H. Holle, “Perbandingan Metode Machine Learning dalam Analisis Sentimen Twitter,†J. Sist. dan Teknol. Inf., vol. 10, no. 4, p. 429, 2022, doi: 10.26418/justin.v10i4.51784.
Z. Alhaq, A. Mustopa, S. Mulyatun, and J. D. Santoso, “Penerapan Metode Support Vector Machine Untuk Analisis Sentimen Pengguna Twitter,†J. Inf. Syst. Manag., vol. 3, no. 2, pp. 44–49, 2021, doi: 10.24076/joism.2021v3i2.558.
P. K. Sari and R. R. Suryono, “Komparasi Algoritma Support Vector Machine , Naïve,†J. Mnemon., vol. 2, no. 2, pp. 7–13, 2024.
K. S. Putri, I. R. Setiawan, and A. Pambudi, “Analisis Sentimen Terhadap Brand Skincare Lokal Menggunakan Naïve Bayes Classifier,†Technol. J. Ilm., vol. 14, no. 3, p. 227, 2023, doi: 10.31602/tji.v14i3.11259.
T. A. Dewi and E. Mailoa, “Perbandingan Implementasi Metode Smote Pada Algoritma Support Vector Machine (Svm) Dalam Analisis Sentimen Opini Masyarakat Tentang Mixue,†J. Indones. Manaj. Inform. dan Komun., vol. 4, no. 3, pp. 849–855, 2023, doi: 10.35870/jimik.v4i3.289.
S. Rabbani, D. Safitri, N. Rahmadhani, A. A. F. Sani, and M. K. Anam, “Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM,†MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 2, pp. 153–160, 2023, doi: 10.57152/malcom.v3i2.897.
N. S. Ramadhanti, W. A. Kusuma, and A. Annisa, “Optimasi Data Tidak Seimbang pada Interaksi Drug Target dengan Sampling dan Ensemble Support Vector Machine,†J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 6, p. 1221, 2020, doi: 10.25126/jtiik.2020762857.
M. I. Fikri, T. S. Sabrila, Y. Azhar, and U. M. Malang, “Comparison of the Naïve Bayes Method and Support Vector Machine on Twitter Sentiment Analysis,†SMATIKA J. STIKI Inform. J., vol. 10, no. 2, pp. 71–76, 2020.
M. H. Wicaksono, M. D. Purbolaksono, and S. Al Faraby, “Perbandingan Algoritma Machine Learning untuk Analisis Sentimen Berbasis Aspek pada Review Female Daily,†eProceedings Eng., vol. 10, no. 3, pp. 3591–3600, 2023, [Online]. Available: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/20631/19944%0Ahttps://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/20631
A. F. Setyaningsih, D. Septiyani, and S. R. Widiasari, “Implementasi Algoritma Naïve Bayes untuk Analisis Sentimen Masyarakat pada Twitter mengenai Kepopuleran Produk Skincare di Indonesia,†J. Teknol. Inform. dan Komput., vol. 9, no. 1, pp. 224–235, 2023, doi: 10.37012/jtik.v9i1.1409.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
