Sentiment Analysis of Indonesian Responses to the Conflict in Palestine Using KNN and SVM Methods
Abstract
The prolonged conflict between Palestine and Israel has attracted worldwide attention, including Indonesia, which has a history of strong support for the Palestinian cause. This study aims to analyze the sentiment of Indonesian people towards the Palestinian-Israeli conflict using the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) methods. The subject of this research is user data X (Twitter) which contains opinions about the conflict. After preprocessing, weighting, and labeling, 2960 tweets were collected and classified into three sentiment categories: positive, negative, and neutral. The KNN+SVM method is applied to classify the sentiment of the processed tweet data. The results showed that of the 2960 data analyzed, 33.8% were labeled positive, 38.9% were labeled negative, and 27.4% were labeled neutral with 82% accuracy, 83% precision, 82% recall, and 82% F1-Score. These results show that the majority of Indonesians tend to be negative in expressing their views on the Palestinian-Israeli conflict. This analysis provides greater insight into sentiment patterns in Indonesian responses to sensitive issues, and contributes to the study of public opinion and social dynamics on social media.
References
[2] F. Upa and Nurhalifah, “Analisis Sentimen Pengguna Twiter Menggunakan Metode SVM-KNN Analysis of Twiter User Sentiment Using SVM-KNN Method,” Nusantara Hasana Journal, vol. 3, no. 12, p. Page, 2024.
[3] A. Tiara Susilawati, N. Anjeni Lestari, and P. Alpria Nina, “Analisis Sentimen Publik Pada Twitter Terhadap Boikot Produk Israel Menggunakan Metode Naïve Bayes,” Jurnal Ilmiah Mahasiswa, vol. 2, no. 1, pp. 26–35, 2024, doi: 10.59603/niantanasikka.v2i1.240.
[4] B. A. Yuniarossy et al., “Analisis Sentimen Terhadap Isu Feminisme Di Twitter Menggunakan Model Convolutional Neural Network (CNN),” vol. 5, no. 1, 2024, doi: 10.46306/lb.v5i1.
[5] A. Hermawan, I. Jowensen, J. Junaedi, and Edy, “Implementasi Text-Mining untuk Analisis Sentimen pada Twitter dengan Algoritma Support Vector Machine,” JST (Jurnal Sains dan Teknologi), vol. 12, no. 1, pp. 129–137, Apr. 2023, doi: 10.23887/jstundiksha.v12i1.52358.
[6] P. Pasek, O. Mahawardana, G. A. Sasmita, P. Agus, and E. Pratama, “Analisis Sentimen Berdasarkan Opini dari Media Sosial Twitter terhadap ‘Figure Pemimpin’ Menggunakan Python,” 2022.
[7] T. Krisdiyanto, E. Maricha, and O. Nurharyanto, “Analisis Sentimen Opini Masyarakat Indonesia Terhadap Kebijakan PPKM pada Media Sosial Twitter Menggunakan Naïve Bayes Clasifiers,” Jurnal CoreIT, vol. 7, no. 1, 2021.
[8] J. W. Iskandar and Y. Nataliani, “Perbandingan Naïve Bayes, SVM, dan k-NN untuk Analisis Sentimen Gadget Berbasis Aspek,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 6, pp. 1120–1126, Dec. 2021, doi: 10.29207/resti.v5i6.3588.
[9] M. G. Andriawan and T. Ernawati, “Penggunaan Algoritma Naïve Bayes Dan Support Vector Machine Untuk Analisis Sentimen Konflik Palestina Dan Israel Pada Platform X,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3, Aug. 2024, doi: 10.23960/jitet.v12i3.4943.
[10] T. Safitri, Y. Umaidah, and I. Maulana, “Analisis Sentimen Pengguna Twitter Terhadap BTS Menggunakan Algoritma Support Vector Machine,” 2023. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[11] D. Musfiroh et al., “Analisis Sentimen terhadap Perkuliahan Daring di Indonesia dari Twitter Dataset Menggunakan InSet Lexicon,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 1, pp. 24–33, 2021.
[12] R. Siringoringo, “Klasifikasi Data Tidak Seimbang Menggunakan Algoritma Smote dan K-Nearest Neighbor,” 2018.
[13] I. Prayoga, M. D. Purbolaksono, and A. Adiwijaya, “Sentiment Analysis on Indonesian Movie Review Using KNN Method With the Implementation of Chi-Square Feature Selection,” Jurnal Media Informatika Budidarma, vol. 7, no. 1, p. 369, Jan. 2023, doi: 10.30865/mib.v7i1.5522.
[14] M. B. Hamzah, “Classification of Movie Review Sentiment Analysis Using Chi-Square and Multinomial Naïve Bayes with Adaptive Boosting,” Journal of Advances in Information Systems and Technology, vol. 3, no. 1, 2021, [Online]. Available: https://journal.unnes.ac.id/sju/index.php/jaist
[15] S. D. Prasetyo, S. S. Hilabi, and F. Nurapriani, “Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes dan KNN,” Jurnal KomtekInfo, pp. 1–7, Jan. 2023, doi: 10.35134/komtekinfo.v10i1.330.
[16] R. I. Putra Selian, A. V. Vitianingsih, S. Kacung, A. Lidya Maukar, and J. Febrian Rusdi, “Sentiment Analysis of Public Responses on Social Media to Satire Joke Using Naive Bayes and KNN,” sinkron, vol. 8, no. 3, pp. 1443–1451, Jul. 2024, doi: 10.33395/sinkron.v8i3.13721.
[17] F. M. Carina, Admi Salma, Dony Permana, and Zamahsary Martha, “Sentiment Analysis of X Application Users on the Conflict Between Israel and Palestine Using Support Vector Machine Algorithm,” UNP Journal of Statistics and Data Science, vol. 2, no. 2, pp. 204–212, May 2024, doi: 10.24036/ujsds/vol2-iss2/170.
[18] D. Vonega, A. Fadila, and D. Kurniawan, “Analisis Sentimen Twitter Terhadap Opini Publik Atas Isu Pencalonan Puan Maharani dalam PILPRES 2024”, JAIC, vol. 6, no. 2, pp. 129-135, Nov. 2022.
[19] J. Friadi, and D. E. Kurniawan, "Analisis Sentimen Ulasan Wisatawan Terhadap Alun-Alun Kota Batam: Perbandingan Kinerja Metode Naive Bayes dan Support Vector Machine," Jurnal Sistem Informasi Bisnis, vol. 14, no. 4, pp. 403-407, Oct. 2024. https://doi.org/10.21456/vol14iss4pp403-407
[20] S. D. Widiyaningsih and A. Pertiwi, “Analysis Of Ovo Application Sentiment Using Lexicon Based Method And K-Nearest Neighbor,” Jurnal Ilmiah Ekonomi Bisnis, vol. 25, no. 1, pp. 14–28, 2020, doi: 10.35760/eb.2020.v25i1.2416.
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