Application of the Naive Bayes Algorithm in Twitter Sentiment Analysis of 2024 Vice Presidential Candidate Gibran Rakabuming Raka using Rapidminer
DOI:
https://doi.org/10.35870/ijsecs.v4i1.2236Keywords:
Sentiment, Twitter, Vice President, Naïve Bayes, RapidminerAbstract
In the current era of digital democracy, social media sentiment analysis has become a relevant method for understanding public views of political figures. As one of the leading social media platforms, Twitter provides a public space for sharing opinions and expressions regarding political issues. This research aims to classify and measure the accuracy of people's responses to the positive and negative sides. Sentiment analysis was carried out using the Naïve Bayes method using a dataset of 3223 tweets. The final results of this research show that implementing the Naïve Bayes Method in sentiment analysis regarding political dynasty polemics, especially regarding the 2024 Cawapres Gibran Rakabuming Raka, provides an accuracy value of 82.19%. Of the 1696 negative and 112 positive sentiments predicted, there were 462 harmful and 953 positive predicted data. These results indicate that most public responses tend to be detrimental to the Constitutional Court's (MK) decision, which grants political legitimacy to Gibran Rakabuming Raka as the 2024 vice-presidential candidate.
Downloads
References
Nada, D. D., Soehardjoepri, S., & Atok, R. M. (2023). Perbandingan analisis sentimen mengenai BPJS pada media sosial Twitter menggunakan Naïve Bayes classifier (NBC) dan Support Vector Machine (SVM). J. Sains dan Seni ITS, 11(6). https://doi.org/10.12962/j23373520.v11i6.96330
Buana, R. S., Gata, W., Widodo, A. Z. P., Setiawan, H., & Hilyati, K. (2023). Analisis sentimen pada komen Twitter Pawang Hujan Mandalika dengan Support Vector Machine (SVM) dan Naïve Bayes. J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), 7(2), 194–200. https://doi.org/10.35870/jtik.v7i2.705
Ramanizar, H., Fajri, A., Sinaga, R. B., Mubarok, H., Pangestu, A. D., & Prasvita, D. S. (2021). Analisis sentimen pengguna Twitter terhadap konflik antara Palestina dan Israel menggunakan metode Naïve Bayesian classification dan Support Vector Machine. Semin. Nas. Mhs. Ilmu Komput. dan Apl. Jakarta-Indonesia(September), 166–175.
Nugroho, G., Murdiansyah, D., & Lhaksmana, K. (2021). Analisis sentimen pemilihan Presiden Amerika 2020 di Twitter menggunakan Naïve Bayes dan Support Vector Machine. e-Proceeding Eng., 8(5), 10106–10115. [Online]. https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/15727/15440
Dwianto, E., & Sadikin, M. (2021). Analisis sentimen transportasi online pada Twitter menggunakan metode klasifikasi Naïve Bayes dan Support Vector Machine. Format J. Ilm. Tek. Inform., 10(1), 94. https://doi.org/10.22441/format.2021.v10.i1.009
Ananda, F. D., & Pristyanto, Y. (2021). Analisis sentimen pengguna Twitter terhadap layanan internet provider menggunakan algoritma Support Vector Machine. MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., 20(2), 407–416. https://doi.org/10.30812/matrik.v20i2.1130
Husada, H. C., & Paramita, A. S. (2021). Analisis sentimen pada maskapai penerbangan di platform Twitter menggunakan algoritma Support Vector Machine (SVM). Teknika, 10(1), 18–26. https://doi.org/10.34148/teknika.v10i1.311
Fikri, M. I., Sabrila, T. S., & Azhar, Y. (2020). Perbandingan metode Naïve Bayes dan Support Vector Machine pada analisis sentimen Twitter. Smatika J., 10(02), 71–76. https://doi.org/10.32664/smatika.v10i02.455
Tuhuteru, H., & Iriani, A. (2018). Analisis sentimen Perusahaan Listrik Negara cabang Ambon menggunakan metode Support Vector Machine dan Naive Bayes Classifier. J. Inform. J. Pengemb. IT, 3(3), 394–401. https://doi.org/10.30591/jpit.v3i3.977
Giovani, A. P., Ardiansyah, A., Haryanti, T., Kurniawati, L., & Gata, W. (2020). Analisis sentimen aplikasi Ruang Guru di Twitter menggunakan algoritma klasifikasi. J. Teknoinfo, 14(2), 115. https://doi.org/10.33365/jti.v14i2.679
Fitriana, F., Utami, E., & Al Fatta, H. (2021). Analisis sentimen opini terhadap vaksin Covid-19 pada media sosial Twitter menggunakan Support Vector Machine dan Naive Bayes. J. Komtika (Komputasi dan Inform.), 5(1), 19–25. https://doi.org/10.31603/komtika.v5i1.5185
Arsi, P., & Waluyo, R. (2021). Analisis sentimen wacana pemindahan ibu kota Indonesia menggunakan algoritma Support Vector Machine (SVM). J. Teknol. Inf. dan Ilmu Komput., 8(1), 147. https://doi.org/10.25126/jtiik.0813944
Normah, Rifai, B., Vambudi, S., & Maulana, R. (2022). Analisa sentimen perkembangan Vtuber dengan metode Support Vector Machine berbasis SMOTE. J. Tek. Komput. AMIK BSI, 8(2), 174–180. https://doi.org/10.31294/jtk.v4i2
Rahayu, I. P., Fauzi, A., & Indra, J. (2022). Analisis sentimen terhadap program Kampus Merdeka menggunakan Naive Bayes dan Support Vector Machine. J. Sist. Komput. dan Inform., 4(2), 296. https://doi.org/10.30865/json.v4i2.5381
Sari, B. W., & Haranto, F. F. (2019). Implementasi Support Vector Machine untuk analisis sentimen pengguna Twitter terhadap pelayanan Telkom dan Biznet. J. Pilar Nusa Mandiri, 15(2), 171–176. https://doi.org/10.33480/pilar.v15i2.699
Petiwi, M. I., Triayudi, A., & Sholihati, I. D. (2022). Analisis sentimen Gofood berdasarkan Twitter menggunakan metode Naïve Bayes dan Support Vector Machine. J. Media Inform. Budidarma, 6(1), 542. https://doi.org/10.30865/mib.v6i1.3530
Setiawan, H., Utami, E., & Sudarmawan, S. (2021). Analisis sentimen Twitter kuliah online pasca Covid-19 menggunakan algoritma Support Vector Machine dan Naive Bayes. J. Komtika (Komputasi dan Inform.), 5(1), 43–51. https://doi.org/10.31603/komtika.v5i1.5189
Darwis, D., Siskawati, N., & Abidin, Z. (2021). Penerapan algoritma Naive Bayes untuk analisis sentimen review data Twitter BMKG nasional. J. Tekno Kompak, 15(1), 131. https://doi.org/10.33365/jtk.v15i1.744
Sari, D. N., & Basit, A. (2020). Media sosial Instagram sebagai media informasi edukasi. Persepsi Commun. J., 3(1), 23–36. https://doi.org/10.30596/persepsi.v3i1.4428
Sya’bani, M. R. F., Enri, U., & Padilah, T. N. (2022). Analisis sentimen terhadap bakal calon presiden 2024 dengan algoritme Naïve Bayes. JURIKOM (Jurnal Ris. Komputer), 9(2), 265. https://doi.org/10.30865/jurikom.v9i2.3989
Kwok, S. W. H., Vadde, S. K., & Wang, G. (2021). Tweet topics and sentiments relating to covid-19 vaccination among australian twitter users: machine learning analysis. Journal of Medical Internet Research, 23(5), e26953. https://doi.org/10.2196/26953
Djamaludin, M. A., Triayudi, A., & Mardiani, E. (2022). Analisis sentimen tweet kri nanggala 402 di twitter menggunakan metode naïve bayes classifier. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 6(2), 161-166. https://doi.org/10.35870/jtik.v6i2.398
Villavicencio, C. N., Macrohon, J. J. E., Inbaraj, X. A., Jeng, J., & Hsieh, J. (2021). Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information, 12(5), 204. https://doi.org/10.3390/info12050204
Asmara, R., Ardiansyah, M. F., & Anshori, M. (2020). Analisa sentiment masyarakat terhadap pemilu 2019 berdasarkan opini di twitter menggunakan metode naive bayes classifier. INOVTEK Polbeng - Seri Informatika, 5(2), 193. https://doi.org/10.35314/isi.v5i2.1095
Astari, N. M. A. J., Divayana, D. G. H., & Indrawan, G. (2020). Analisis sentimen dokumen twitter mengenai dampak virus corona menggunakan metode naive bayes classifier. Jurnal Sistem Dan Informatika (JSI), 15(1), 27-29. https://doi.org/10.30864/jsi.v15i1.332
Amelia, R., Darmansah, D., Prastiwi, N. S., & Purbaya, M. E. (2022). Impementasi algoritma naive bayes terhadap analisis sentimen opini masyarakat indonesia mengenai drama korea pada twitter. JURIKOM (Jurnal Riset Komputer), 9(2), 338. https://doi.org/10.30865/jurikom.v9i2.3895
Mustofa, R. L., & Prasetiyo, B. (2021). Sentiment analysis using lexicon-based method with naive bayes classifier algorithm on #newnormal hashtag in twitter. Journal of Physics: Conference Series, 1918(4), 042155. https://doi.org/10.1088/1742-6596/1918/4/042155
Yunanto, I., & Yulianto, S. (2022). Twitter sentiment analysis pedulilindungi application using naïve bayes and support vector machine. Jurnal Teknik Informatika (Jutif), 3(4), 807-814. https://doi.org/10.20884/1.jutif.2022.3.4.292
Isnain, A. R., Marga, N. S., & Alita, D. (2021). Sentiment analysis of government policy on corona case using naive bayes algorithm. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(1), 55. https://doi.org/10.22146/ijccs.60718
Chong, K., & Shah, N. (2022). Comparison of naive bayes and svm classification in grid-search hyperparameter tuned and non-hyperparameter tuned healthcare stock market sentiment analysis. International Journal of Advanced Computer Science and Applications, 13(12). https://doi.org/10.14569/ijacsa.2022.0131213
Downloads
Published
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
