ANALISIS SENTIMEN DALAM APLIKASI X TERHADAP PENGUNGSI ROHINGYA DENGAN LSTM
DOI:
https://doi.org/10.36080/skanika.v8i1.3329Keywords:
sentiment analysis, deep learning, long-short term memory, natural language processing, rohingyaAbstract
Rohingya refugees in Indonesia face discrimination and hate speech due to disinformation in Indonesian society The polemic over the influx of Rohingya refugees to Indonesia has created a variety of responses among the public. Understanding the perspectives and reactions of the Indonesian public regarding this issue is crucial, especially in analyzing the growing sentiment. This research was conducted to evaluate the views and sentiments of Indonesians towards the Rohingya through X social media platforms. Data was collected through web scraping using twikit during December 2023, resulting in 17,613 tweets. Labeling was done using the trained IndoBERTweet model with the IndoNLU smsa_doc-sentiment-prosa dataset. The Long Short-Term Memory (LSTM) model is applied with two class balancing methods, Random Oversampling and SMOTE. The results show that with Random Oversampling, the model achieves precision 0.9139, recall 0.9632, F1 score 0.9379, and accuracy 0.9069. Meanwhile, the use of SMOTE resulted in precision 0.9092, recall 0.9445, F1 score 0.9265, and accuracy 0.8906.
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References
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[2] C. A. Sopamena, “Pengungsi Rohingya Dan Potensi Konflik & Kemajemukan Horizontal Di Aceh,” J. Caraka Prabu, vol. 7, no. 2, pp. 85–115, 2023.
[3] 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.
[4] A. Firdaus, W. I. Firdaus, P. Studi, T. Informatika, M. Digital, and P. N. Sriwijaya, “Text Mining Dan Pola Algoritma Dalam Penyelesaian Masalah Informasi (Sebuah Ulasan),” J. JUPITER (Jurnal Penelit. dan Ilmu Komputer), vol. 13, no. 1, pp. 66–78, 2021.
[5] S. M. Fani, R. Santoso, and S. Suparti, “Penerapan Text Mining untuk Melakukan Clustering Data Tweet Akun Blibli pada Media Sosial Twitter Menggunakan K-means Clustering,” J. Gaussian, vol. 10, no. 4, pp. 583–593, 2021.
[6] S. Raschka and V. Mirjalili, Python Machine Learning, 3rd ed. Packt Publishing Ltd., 2019.
[7] S. Mutmatimah, Khairunnas, and Khairunnisa, “Metode Deep Learning LSTM dalam Analisis Sentimen Aplikasi PeduliLindungi,” J. Comput. Sci. Informatics, vol. 1, no. 1, pp. 9–19, 2024.
[8] M. Z. Rahman, Y. A. Sari, and N. Yudistira, “Analisis Sentimen Tweet COVID-19 menggunakan Word Embedding dan Metode Long Short-Term Memory (LSTM),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 11, pp. 5120–5127, 2021.
[9] P. L. Parameswari and Prihandoko, “Penggunaan Convolutional Neural Network Untuk Analisis Sentimen Opini Lingkungan Hidup Kota Depok Di Twitter,” J. Ilm. Teknol. dan Rekayasa, vol. 27, no. 1, pp. 29–42, 2022.
[10] A. R. Maulana, S. H. Wijoyo, and Y. T. Mursityo, “Analisis Sentimen Kebijakan Penerapan Kurikulum Merdeka Sekolah Dasar dan Sekolah Menengah pada Media Sosial Twitter dengan Menggunakan Metode Word Embedding Dan Long Short-term Memory Networks (LSTM),” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 3, pp. 523–530, 2023.
[11] Y. A. Pradana, I. Cholissodin, and D. Kurnianingtyas, “Analisis Sentimen Pemindahan Ibu Kota Indonesia pada Media Sosial Twitter menggunakan Metode LSTM dan Word2Vec,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 5, pp. 2389–2397, 2023.
[12] D. Ananda and R. R. Suryono, “Analisis Sentimen Publik Terhadap Pengungsi Rohingya di Indonesia dengan Metode Support Vector Machine dan Naïve Bayes,” J. Media Inform. Budidarma, vol. 8, no. 2, p. 748, 2024.
[13] A. F. Pratama, T. B. Kurniawan, Misinem, and D. A. Dewi, “Implementasi Analisis Sentimen dan Model Deep Learning Untuk Prediksi Harga Bitcoin,” JUPITER J. Penelit. Ilmu Dan Teknol. Komput., vol. 15, no. 1b, pp. 403–412, 2023.
[14] R. Merdiansah, S. Siska, and A. Ali Ridha, “Analisis Sentimen Pengguna X Indonesia Terkait Kendaraan Listrik Menggunakan IndoBERT,” J. Ilmu Komput. dan Sist. Inf., vol. 7, no. 1, pp. 221–228, Mar. 2024.
[15] J. F. Kusuma and A. Chowanda, “Indonesian Hate Speech Detection Using IndoBERTweet and BiLSTM on Twitter,” JOIV Int. J. Informatics Vis., vol. 7, no. 3, pp. 773–780, 2023.
[16] F. Koto, J. H. Lau, and T. Baldwin, “INDOBERTWEET: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization,” EMNLP 2021-2021 Conference on Empirical Methods in Natural Language Processing, Proceedings, 2021, pp. 10660–10668.
[17] Z. Tan et al., “Large Language Models for Data Annotation: A Survey,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024, pp. 930–957.
[18] U. Hasanah, A. Mohamad Soleh, and K. Sadik, “Effect of Random Under sampling , Oversampling, and SMOTE on the Performance of Cardiovascular Disease Prediction Models,” J. Mat. Stat. dan Komputasi, vol. 21, no. 1, pp. 88–102, 2024.
[19] M. V. Shyahrin, Y. Sibaroni, and D. Puspandari, “Penerapan Metode Long Short-Term Memory dan Word2Vec dalam Analisis Sentimen Ulasan pada Aplikasi Ferizy,” Techno.Com, vol. 22, no. 4, pp. 833–842, 2023.
[20] D. I. Af’idah, D. Dairoh, S. F. Handayani, and R. W. Pratiwi, “Pengaruh Parameter Word2Vec terhadap Performa Deep Learning pada Klasifikasi Sentimen,” J. Inform. J. Pengemb. IT, vol. 6, no. 3, pp. 156–161, 2021.
[21] G. Y. Christiawan, R. A. Putra, A. Sulaiman, E. Poerbaningtyas, and S. W. P. Listio, “Penerapan Metode Convolutional Neural Network Dalam Mengklasifikasikan Penyakit Daun Tanaman Padi ( CNN ),” J-INTECH (Journal Inf. Technol., vol. 11, no. 2, pp. 294–306, 2023.
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Published
2025-01-30
How to Cite
[1]
A. Pratama and M. Rosyda, “ANALISIS SENTIMEN DALAM APLIKASI X TERHADAP PENGUNGSI ROHINGYA DENGAN LSTM”, SKANIKA, vol. 8, no. 1, pp. 95–105, Jan. 2025.
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