Analisis Sentimen Pengguna Twitter terhadap Konflik Rusia-Ukraina Menggunakan Naïve Bayes dan Lexicon Based Features

Barnes J. Manurung; Bita Parga Zen; Yohani Setiya Rafika Nur; Roland Claudio Felle; Eryan Ahmad Firdaus

doi:10.57094/ji.v4i1.2574

Barnes J. Manurung Universitas Telkom
Bita Parga Zen Universitas Ma Chung
Yohani Setiya Rafika Nur Universitas Telkom
Roland Claudio Felle Universitas Pertahanan Republik Indonesia
Eryan Ahmad Firdaus Universitas Pertahanan Republik Indonesia

DOI: https://doi.org/10.57094/ji.v4i1.2574

Keywords: Russia-Ukraine Conflict, Sentiment Analysis, Naïve Bayes, Lexicon-Based Features, Twitter

Abstract

The conflict between Russia and Ukraine remains one of the major international issues drawing global attention. It began in 2014 with the ousting of President Yanukovych, which triggered a political divide between pro-European Union and pro-Russian factions within Ukraine. Tensions escalated significantly by late 2021, culminating in Russia’s military aggression against Ukraine on February 24, 2022, under President Vladimir Putin’s directive. Twitter, as a social media platform, became a major outlet for the public to express their opinions regarding the conflict. This study aims to analyze public sentiment on Twitter concerning the Russia-Ukraine conflict. The methods used in this research are a combination of the Naïve Bayes algorithm and Lexicon Based Features. Naïve Bayes is utilized to classify sentiment data, while Lexicon Based Features assign weights to positive and negative sentiment words in textual data. The results show that this combined method is effective in categorizing public opinions based on their sentiments towards the conflict. This sentiment analysis provides a broader understanding of global perceptions and may serve as a reference for assessing public opinion in digital media contexts.

References

A. Zaenudin, “Bagaimana Twitter Memengaruhi Opini Publik dan Preferensi Politik?” 21 Maret 2018. [Online]. Available: https://tirto.id/bagaimana-twitter-memengaruhi-opini-publik-dan-preferensi-politik-cGre.

Rizal, “Wow! Pengguna Aktif Harian Twitter Tembus 217 Juta di Dunia,” 15 February 2022. [Online]. Available: https://infokomputer.grid.id/read/123143849/wow-pengguna-aktif-harian-twitter-tembus-217-juta-di-dunia.

A. Mardatila, “Penyebab Konflik Rusia dan Ukraina, Pahami Perkembangannya dari Waktu ke Waktu,” merdeka.com, 24 Februari 2022. [Online]. Available: https://www.merdeka.com/sumut/penyebab-konflik-rusia-dan-ukraina-pahami-perkembangannya-dari-waktu-ke-waktu-kln.html.

A. Aflaha, “Rusia Vs Ukraina: Perang Akhirnya Beneran Terjadi!” cnbcindonesia, 24 February 2022. [Online]. Available: https://www.cnbcindonesia.com/market/20220224130324-17-318002/rusia-vs-ukraina-perang-akhirnya-beneran-terjadi.

R. B. S. H. M. A. D. P. D. S. P. Haiqal Ramanizar Al Fajri, “Analisis Sentimen Pengguna Twitter terhadap Konflik antara Palestina dan,” Seminar Nasional Mahasiswa Ilmu Komputer dan Aplikasinya (SENAMIKA), 2021.

H. N. P. M. W. d. I. Ali Sholihin, “nalisis Penyakit Difteri Berbasis Twitter Menggunakan,” Sains, Aplikasi, Komputasi dan Teknologi Informasi, 2019.

K. I. Rifiana Arief, “ANALISIS SENTIMEN TOPIK VIRAL DESA PENARI PADA MEDIA SOSIAL TWITTER DENGAN METODE LEXICON BASED,” Jurnal Ilmiah MATRIK, 2019.

F. V. Sari, “ANALISIS SENTIMEN PELANGGAN TOKO ONLINE JD.ID,” Jurnal SIMETRIS, vol. 2, no. 681, p. November, 2019.

U. D. Arni, “Apa Itu Text Mining?” garudacyber, 25 Oktober 2018. [Online]. Available: https://garudacyber.co.id/artikel/1254-apa-itu-text-mining.

A. S. Oktriwina, “NLP: Kecerdasan Buatan yang Bantu Komputer Pahami Bahasa Manusia,” glints, 2 Februari 2021. [Online]. Available: https://glints.com/id/lowongan/natural-language-processing-adalah/#.YjEcWnpBxHM.

D. Sierra, “Algoritma TF — IDF,” medium, 13 Februari 2019. [Online]. Available: https://dltsierra.medium.com/algoritma-tf-idf-633e17d10a80.

I. S. d. D. F. Bita Parga Zen, “TF-IDF Method and Vector Space Model Regarding the Covid-19 Vaccine on Online News,” Jurnal dan Penelitian Teknik Informatika, 2021.

N. S. Z. A. Dedi Darwis, “Penerapan Algoritma Naive Bayes untuk Analisis Sentimen Review Data Twitter BMKG Nasional,” Jurnal TEKNO KOMPAK, 2021.

F. V. Sari, “ANALISIS SENTIMEN PELANGGAN TOKO ONLINE JD.ID MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER BERBASIS KONVERSI IKON EMOSI,” Jurnal SIMETRIS, 2019.

G. A. B. M. B. S. Rachmad Mahendrajaya, “ANALISIS SENTIMEN PENGGUNA GOPAY MENGGUNAKAN METODE,” Fakultas Teknik, Universitas Muhammadiyah Ponorogo, 2019.

R. Andriani and J. Attata, “UPAYA RESOLUSI KONFLIK RUSIA-UKRAINA,” 28 March 2022.

R. N. Shobah, “Perang Rusia vs Ukraina: Sejarah Konflik 2 Negara Pecahan Uni Soviet Ini, Bermula dari Krisis 2013,” Tribunkaltim.co, 25 Februari 2022. [Online]. Available: https://kaltim.tribunnews.com/2022/02/25/perang-rusia-vs-ukraina-sejarah-konflik-2-negara-pecahan-uni-soviet-ini-bermula-dari-krisis-2013?page=2.

sef, CNBC Indonesia, “Kronologi dan Latar Belakang Perang Rusia vs Ukraina,” CNBC Indonesia, 6 Maret 2022. [Online]. Available: https://www.cnbcindonesia.com/news/20220304133929-4-320041/kronologi-dan-latar-belakang-perang-rusia-vs-ukraina.

D. H. Jayani, “10 Media Sosial yang Paling Sering Digunakan di Indonesia,” 26 2 2020. [Online]. Available: https://databoks.katadata.co.id/datapublish/2020/02/26/10-media-sosial-yang-paling-sering-digunakan-di-indonesia.

Y. Ratnasari, “Twitter Resmi Menambah Batasan Kicauan Jadi 280 Karakter,” Tirto.id, 8 November 2017. [Online]. Available: https://tirto.id/twitter-resmi-menambah-batasan-kicauan-jadi-280-karakter-czNm.

F. D. Adhinata, B. Parga Zen and A. D. Septiadi, “Lightweight and Efficient Deep Learning Model for Fire Detection,” 2023 10th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Palembang, Indonesia, 2023, pp. 583-588, doi: 10.1109/EECSI59885.2023.10295606.

F. Mukarromah and B. Parga Zen, “News Website Implementation Using The Fuzzy Analytical Hierarchy Process (FAHP) Method,” 2023 29th International Conference on Telecommunications (ICT), Toba, Indonesia, 2023, pp. 1-6, doi: 10.1109/ICT60153.2023.10374045.

A. Utami, D. F. Hebrasianto Permadi, B. P. Zen, F. D. Adhinata and W. A. Saputra, “Classification Analysis of Star Hotels in Banyumas Using the Random Forest Method,” 2024 International Conference on Information Technology and Computing (ICITCOM), Yogyakarta, Indonesia, 2024, pp. 139-144, doi: 10.1109/ICITCOM62788.2024.10762547.

Bita Parga Zen, Irwan Susanto, Khofifah Putriyani, Sintiya; Automatic document classification for tempo news articles about covid 19 based on term frequency, inverse document frequency (TF-IDF), and Vector Space Model (VSM). AIP Conf. Proc. 23 July 2024; 2952 (1): 060003. https://doi.org/10.1063/5.0212036

Zen, B.P., Abdurahman, Zafia, A., Utami, A., Putro, I.N.Y., Aditama, F.D. (2024). Multi Socket Transmission System Application with Advanced Encryption Standard Algorithm to Support Confidential Medical Data Security. In: Triwiyanto, T., Rizal, A., Caesarendra, W. (eds) Proceedings of the 4th International Conference on Electronics, Biomedical Engineering, and Health Informatics. ICEBEHI 2023. Lecture Notes in Electrical Engineering, vol 1182. Springer, Singapore. https://doi.org/10.1007/978-981-97-1463-6_1.