Klasifikasi Sentimen Pengguna Platform X Terhadap Isu Geopolitik Israel–Palestina Menggunakan BERT
Abstract
The geopolitical issue between Israel and Palestine has sparked a wide range of public reactions on social media, particularly on the platform X. Sentiment analysis is a widely discussed topic in Natural Language Processing. This study develops a sentiment classification model for that issue using the BERT Base Uncased architecture, with data labeled automatically through the VADER approach. Data were collected via scraping with Tweet-Harvest, yielding 3.172 tweets, later reduced to 2.362 after preprocessing stages including cleaning, case folding, tokenizing, stopword removal, and stemming. VADER labeling produced a distribution of 73.7% negative sentiment and 26.3% positive sentiment. The model achieved 86.92% accuracy, with precision, recall, and F1-Score for the negative class of 90%, 93%, and 91%, respectively, while the positive class obtained 77%, 71%, and 74%. The average F1-score reached 88%. These results indicate the model correctly predicts most of the data, although a slight indication of overfitting appeared, with no significant impact on validation accuracy. This confirms that combining VADER and BERT Base Uncased is effective for sentiment classification on international geopolitical issues.
Downloads
References
A. Delia, F. A. Khatimah, H. S. Andien, R. Arissaputra, and M. Parhan, “Peran Muslim Dalam Konflik Geopolitik: Analisis Perang Israel-Palestina,” Pendas: Jurnal Ilmiah Pendidikan Dasar, vol. 9, no. 2, pp. 152–166, Jun. 2024, doi: 10.23969/jp.v9i2.15182.
R. Hasna Rahmawan and I. Wayan Midhio, “Efek Konflik Israel-Hamas di Indonesia: Opini Publik dan Upaya Diplomatik,” Almikraj, vol. 5, no. 01, pp. 1752–1762, Dec. 2024, doi: 10.37680/almikraj.v5i01.643.
W. Widya Ariestya et al., “Reaksi Media Sosial Terhadap Konflik Israel-Palestina: Pendekatan Analisis Sentimen Dengan VADER Dan BERT,” Jurnal Ilmu Komputer dan Informatika, vol. 5, no. 2, pp. 150–157, Dec. 2024, doi: 10.24127/jiki.v5i2.7420.
M. H. Al-Areef and K. Saputra, “Analisis Sentimen Pengguna Twitter Mengenai Calon Presiden Indonesia Tahun 2024 Menggunakan Algoritma LSTM,” Jurnal Sains Manajemen Informatika dan Komputer, vol. 22, no. 2, pp. 270–279, Aug. 2023, doi: 10.53513/jis.v22i2.8680.
B. J. Manurung, B. Parga Zen, Y. Setiya, R. Nur, R. C. Felle, and E. A. Firdaus, “Analisis Sentimen Pengguna Twitter terhadap Konflik Rusia-Ukraina Menggunakan Naïve Bayes dan Lexicon Based Features,” Jurnal Informatika, vol. 4, no. 1, pp. 8–16, Mar. 2025, doi: 10.57094/ji.v4i1.2574.
M. G. Al-Kadzim, R. Rasim, and H. Herbert, “Analisis Perubahan Sentimen Publik di Media Sosial X terhadap Konflik Palestina-Israel Menggunakan Model IndoBERT,” Digital Transformation Technology, vol. 4, no. 2, pp. 1167–1174, Jan. 2025, doi: 10.47709/digitech.v4i2.5312.
J. R. Jim, M. A. R. Talukder, P. Malakar, M. M. Kabir, K. Nur, and M. F. Mridha, “Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review,” Natural Language Processing Journal, vol. 6, pp. 1–30, Mar. 2024, doi: 10.1016/j.nlp.2024.100059.
J. A. Widians, R. Wardoyo, and S. Hartati, “Feature selection based on chi-square and ant colony optimization for multi-label classification,” International Journal of Electrical and Computer Engineering, vol. 14, no. 3, pp. 3303–3312, Jun. 2024, doi: 10.11591/ijece.v14i3.pp3303-3312.
R. Azhar and M. F. Wijayanto, “Analisis Sentimen di Twitter: Mengungkap Persepsi dan Emosi Publik Seputar Konflik Palestina-Israel,” STAINS (Seminar Nasional Teknologi & Sains), vol. 3, no. 1, pp. 118–124, Jan. 2024, doi: 10.29407/stains.v3i1.4132.
A. Amelia and R. Yusuf, “Analisis Sentimen Masyarakat Indonesia Pada Platform X Terhadap Isu Fufufafa Menggunakan Bidirectional Encoder Representations From Transformers,” Jurnal Informatika Teknologi dan Sains (JINTEKS), vol. 7, no. 1, pp. 72–80, Feb. 2025, doi: 10.51401/jinteks.v7i1.5160.
N. Lubis, Mhd. Z. Siambaton, and R. Aulia, “Implementasi Algoritma Deep Learning pada Aplikasi Speech to Text Online dengan Metode Recurrent Neural Network (RNN),” Jurnal Teknik Informatika, vol. 3, no. 3, pp. 113–126, Sep. 2024, doi: 10.56211/sudo.v3i3.583.
D. Wang et al., “Unveiling the spatiotemporal propagation patterns of sentiments regarding the Israeli–Palestinian military conflict,” Humanities and Social Sciences Communications, vol. 12, no. 1, pp. 1–17, Dec. 2025, doi: 10.1057/s41599-025-05134-x.
M. Hasraddin Hasnan, R. Yusliana Bakti, M. Faisal, T. Khawa Abd Rahman, M. S. Syafaat Kuba, and T. Wahyuni, “Deteksi Berita Hoaks Berbahasa Indonesia yang Dapat Dijelaskan Menggunakan IndoBERT dan SHAP,” Buletin Sistem Informasi dan Teknologi Islam, vol. 7, no. 2, pp. 160–170, May 2026, doi: 10.33096/busiti.v7i2.3466.
L. Geni, E. Yulianti, and D. I. Sensuse, “Sentiment Analysis of Tweets Before the 2024 Elections in Indonesia Using BERT Language Models,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 9, no. 3, pp. 746–757, Aug. 2023, doi: 10.26555/jiteki.v9i3.26490.
A. Sehatman Saragih et al., “Analisis Sentimen Publik Indonesia Terhadap Konflik Israel-Iran Di Media Sosial Menggunakan IndoBERT Dan Explainable AI (LIME),” Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika, vol. 20, no. 1, pp. 78–87, Jan. 2026, doi: 10.47111/jti.v20i1.24465.
R. Dwiyansaputra, S. Ika Murpratiwi, and A. Aranta, “Analisis Sentimen Pengguna Platform Media Sosial X Pada Topik Pemilihan Presiden 2024 Menggunakan Perbandingan Model Monolingual Dan Multilingual BERT,” Jurnal Mahasiswa Teknik Informatika, vol. 9, no. 1, pp. 626–634, Feb. 2025, doi: 10.36040/jati.v9i1.12430.
K. Kusnawi and K. Anam, “Comparison ff Sentiment Labeling Using Textblob, Vader, and Flair in Public Opinion Analysis Post-2024 Presidential Inauguration with IndoBERT,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 2, pp. 803–818, Apr. 2025, doi: 10.52436/1.jutif.2025.6.2.4015.
J. L. Halim, “BERT-base, DistilBERT, and BERTweet: A Comparative Study On App Review,” JIKO (Jurnal Informatika dan Komputer), vol. 10, no. 1, p. 45, Feb. 2026, doi: 10.26798/jiko.v10i1.2217.
A. I. Fadri, N. Futri, A. Jelita, D. Dimas Bagaskara, and R. Zahra, “Sentiment Analysis of Public Opinion on the Gaza Conflict Using Machine Learning,” Public Research Journal of Engineering, Data Technology and Computer Science, vol. 3, no. 2, pp. 138–149, Jan. 2026, doi: 10.57152/predatecs.v3i2.2088.
R. Fauzi, E. Iman, and H. Ujianto, “Sentiment Analysis of Indonesian Responses to the Conflict in Palestine Using KNN and SVM Methods,” Journal of Applied Informatics and Computing (JAIC), vol. 8, no. 2, pp. 542–549, Dec. 2024, doi: 10.30871/jaic.v8i2.8725.
A. Areshey and H. Mathkour, “Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) Model,” Sensors, vol. 23, no. 11, pp. 1–18, Jun. 2023, doi: 10.3390/s23115232.
H. Tang and R. Zhang, “The Research on Aspect-Level Sentiment Analysis for Online Reviews Based on BERT-BiLSTM Model,” Frontiers in Artificial Intelligence and Applications, vol. 373, pp. 928–936, Nov. 2023, doi: 10.3233/FAIA230903.
S. D. Wahyuni and H. Februariyanti, “Analisis Sentimen dan Rating Pengguna Aplikasi Traveloka Menggunakan Vader,” semanTIK Teknik Informasi, vol. 12, no. 1, pp. 79–88, Apr. 2026, doi: 10.55679/semantik.v12i1.285.
K. Nugroho and F. N. Hasan, “Analisis Sentimen Masyarakat Mengenai RUU Perampasan Aset Di Twitter Menggunakan Metode Naïve Bayes,” SMATIKA JURNAL, vol. 13, no. 02, pp. 273–283, Dec. 2023, doi: 10.32664/smatika.v13i02.899.
M. M. Dakwah, A. A. Firdaus, F. Furizal, and R. Faresta, “Sentiment Analysis on Marketplace in Indonesia using Support Vector Machine and Naïve Bayes Method,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 10, no. 1, p. 39, Feb. 2024, doi: 10.26555/jiteki.v10i1.28070.
D. A. Kristiyanti and S. Hardani, “Sentiment Analysis of Public Acceptance of Covid-19 Vaccines Types in Indonesia using Naïve Bayes, Support Vector Machine, and Long Short-Term Memory (LSTM),” Jurnal RESTI, vol. 7, no. 3, pp. 722–732, Jun. 2023, doi: 10.29207/resti.v7i3.4737.
R. Rinandyaswara, Y. A. Sari, and M. T. Furqon, “Pembentukan Daftar Stopword Menggunakan Term Based Random Sampling Pada Analisis Sentimen Dengan Metode Naive Bayes (Studi Kasus: Kuliah Daring di Masa Pandemi),” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 9, no. 4, pp. 717–724, Aug. 2022, doi: 10.25126/jtiik.202294707.
D. Ariatmanto and A. M. Rifai, “The Impact of Feature Extraction in Random Forest Classifier for Fake News Detection,” Jurnal RESTI, vol. 8, no. 6, pp. 730–736, Dec. 2024, doi: 10.29207/resti.v8i6.6017.
S. Ernawati and R. Wati, “Evaluasi Performa Kernel SVM dalam Analisis Sentimen Review Aplikasi ChatGPT Menggunakan Hyperparameter dan VADER Lexicon,” Jurnal Buana Informatika, vol. 15, no. 1, pp. 40–49, Apr. 2024, doi: 10.24002/jbi.v15i1.7925.
M. Jundi, R. Rahmaddeni, P. Utami, S. Perdana Arifin, and L. Sinaga, “Klasifikasi Pengaduan Kekerasan Berbasis Bidirectional Encoder Representations from Transformers (BERT) pada Kementrian Pemberdayaan Perempuan dan Perlindungan Anak Kementrian (PPPA),” Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence), vol. 5, no. 2, pp. 258–264, Aug. 2025, doi: 10.55382/jurnalpustakaai.v5i2.1109.
M. Ilham and B. Priambodo, “Analisis Sentimen Publik Terhadap Program Makan Siang Gratis Menggunakan BERT Neural Network Pada Platform X,” JEMSI: Jurnal Ekonomi Manajemen Sistem Informasi, vol. 6, no. 2, pp. 1039–1047, Nov. 2024, doi: 10.38035/jemsi.v6i2.
F. M. Apriansyah, T. I. Ramadhan, C. R. Hidayat, and A. K. Wijaya, “Perbandingan IndoBERT dan IndoRoBERTa Untuk Analisis Sentimen Pada Film Dokumenter Dirty Vote,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 10, no. 3, pp. 593–605, Jul. 2025, doi: 10.30591/jpit.v10i3.8607.
F. Rafiandi Andhika, W. Witanti, and P. N. Sabrina, “Analisis Sentimen Menggunakan Metode IndoBERT pada Ulasan Aplikasi Zoom Menggunakan Fitur Ekstrasi GloVe,” Metik Jurnal, vol. 9, no. 2, pp. 439–448, Aug. 2025, doi: 10.47002/g38fxb78.
Copyright (c) 2026 Joan Angelina Widians, Cellia Auizia Nugraha, Nataniel Dengen, Akhmad Irsyad
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
