Analisis Sentimen Crawling Data dari Sosial Media X tentang Gaza Menggunakan Metode SVM dan Decision Tree
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The Palestinian conflict with Israel has attracted a lot of attention from the world, especially in the Gaza Strip after the Hamas group took over the leadership, so that people often make tweets (posts) with various views, some have neutral, positive, negative views. Sentiment analysis is used to analyze data obtained from posts on Twitter. The purpose of this study is to determine the public's views on the Gaza Strip by applying the Decision Tree and Support Vector Machine algorithms to measure their level of accuracy. The results of the sentiment analysis show 805 neutral data, 415 positive, and 308 negative, then the Support Vector Machine algorithm produces the highest level of accuracy with a value of 73.86 which can be categorized as good.
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Decision Tree ; Support Vector Machine ; Sentiment Analyst ; Gaza ; Hamas ; Palestine ; Israel ; Twitter
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BBC News Indonesia. (2023). Sejarah konflik Palestina-Israel, pertikaian berkepanjangan yang berlangsung puluhan tahun. BBC.com. https://www.bbc.com/indonesia/articles/cjr0pz20z7po.
Dewi, C., & Chen, R. C. (2019). Integrating real-time weather forecasts data using OpenWeatherMap and Twitter. International Journal of Information Technology and Business, 1(2), 48-52.
Dewi, C., & Chen, R. C. (2020). Decision making based on IoT data collection for precision agriculture. Intelligent Information and Database Systems: Recent Developments 11, 31-42. https://doi.org/10.1007/978-3-030-14132-5_3.
Dewi, C., Zendrato, J., & Christanto, H. J. (2024). Original Research Article Improvement of support vector machine for predicting diabetes mellitus with machine learning approach. Journal of Autonomous Intelligence, 7(2). https://doi.org/10.32629/jai.v7i2.888.
Dharma, R. (2022). Data Preprocessing: Pengertian, Manfaat, dan Tahapan Kerjanya.
Irawan, Y. (2021). Penerapan Algoritma Decision Tree C4. 5 Untuk Memprediksi Kelayakan Calon Pendonor Melakukan Donor Darah Dengan Klasifikasi Data Mining. JTIM: Jurnal Teknologi Informasi dan Multimedia, 2(4), 181-189. https://doi.org/10.35746/jtim.v2i4.75.
Marpaung, V. P., Sihombing, G. A., Maulida, H., Ridho, A., & Ardianto, B. (2024). Serangan Militer Israel di Jalur Gaza: Pertanggung jawaban Pidana Berdasarkan Statuta Roma Mahkamah Pidana Internasional. Aliansi: Jurnal Hukum, Pendidikan dan Sosial Humaniora, 1(5), 18-28. https://doi.org/10.62383/aliansi.v1i5.371.
Ramadhan, M. A., & Wahyudin, M. I. (2022). Analisis Sentimen Mengenai Keberhasilan Indonesia di Ajang Thomas Cup 2020 (Studi Kasus Media Sosial Twitter) Menggunakan Metode Naïve Bayes dan Decision Tree. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 6(4), 505-511. https://doi.org/10.35870/jtik.v6i4.560.
Ramlan, R., Satyahadewi, N., & Andani, W. (2023). Analisis Sentimen Pengguna Twitter Menggunakan Support Vector Machine Pada Kasus Kenaikan Harga BBM. Jambura Journal of Mathematics, 5(2), 431-445. https://doi.org/10.34312/jjom.v5i2.20860.
Sejati, W., Bist, A. S., & Tambunan, A. (2023). Pengembangan analisis sentimen dalam rekayasa software engineering menggunakan tinjauan literatur sistematis. Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi, 2(1), 95-103. https://doi.org/10.33050/mentari.v2i1.377
Singgalen, Y. A. (2023). Analisis Sentimen Top 10 Traveler Ranked Hotel di Kota Makassar Menggunakan Algoritma Decision Tree dan Support Vector Machine. KLIK: Kajian Ilmiah Informatika dan Komputer, 4(1), 323-332. https://doi.org/10.30865/klik.v4i1.1153.
Tuhuteru, H. (2020). Analisis Sentimen Masyarakat Terhadap Pembatasan Sosial Berksala Besar Menggunakan Algoritma Support Vector Machine. Journal Information System Development (ISD), 5(2).
Zy, A., Sasongko, A. T., & Kamalia, A. Z. (2023). Penerapan Naïve Bayes Classifier, Support Vector Machine, dan Decision Tree untuk Meningkatkan Deteksi Ancaman Keamanan Jaringan. KLIK: Kajian Ilmiah Informatika Dan Komputer, 4(1), 610-617. https://doi.org/10.30865/klik.v4i1.1134.