Media's Role in Reporting 2024 Indonesian Election Fraud

Hafiz Gustika Pratama, Atwar Bajari, Efi Fadilah

Abstract


The 2024 Indonesian Presidential Election sparked significant public debate regarding allegations of election fraud. This study investigated public sentiment toward media reporting in response to these fraud allegations on social media platform X. This study employed a quantitative approach with sentiment analysis methods, utilizing three sentiment analysis algorithms: Support Vector Machine (SVM), VADER Sentiment, and Naive Bayes. The research involved collecting tweets related to election fraud, which were then processed using the TF-IDF method to assess the importance of words within the text. Subsequently, the data was classified to identify the sentiment expressed in the tweets. VADER achieved the highest accuracy of 100%, followed by SVM at 92.29%, and Naive Bayes at 90.05%. While most tweets were neutral, negative sentiment was more prevalent in all models. These findings suggested that social media sentiment reflected public opinion on sensitive political issues, providing valuable insights into the discourse on election fraud. The study underscored the need for improving sentiment analysis methods, particularly in addressing data imbalance and the complexities of political sentiment in Indonesia.


Keywords


Election Fraud; Political Discourse; Public Opinion; Sentiment Analysis; Social Media

Full Text:

PDF (ENGLISH)

References


Aletti, G., Crimaldi, I., & Saracco, F. (2021). A model for the X sentiment curve. PLoS ONE, 14(4), 1–28.

Alfonso, M., & Rarasati, D. B. (2023). Sentiment analysis of 2024 presidential candidates election using SVM algorithm. JISA (Jurnal Informatika Dan Sains), 6(2).

Anudeepthi, D., Vutla, G., Reddy, V., & Santhi, S. (2023). Opinion mining on Ukraine–Russian war using VADER. Advances in IoT and Security with Computational Intelligence: Proceedings of ICAISA, Volume 7, 183–193.

April, L., Hananto, S., Hilabi, S., Tukino, T., & Priyatna, B. S. (2023). ). Analisis sentimen calon presiden 2024 menggunakan algoritma SVM pada media sosial Twitter. JOINTECS (Journal of Information Technology and Computer Science), 8(1).

Ardiansyah, R. (2019). Analisis sentimen calon presiden dan wakil presiden periode 2019-2024 pasca debat pilpres di X. Scientific Computing Science Informatics Journal, 2(1), 21–28.

Awwalin, I. N., Syaipudin, L., & Luthfi, A. (2024). Analisis Respon Publik Melalui Sosial Media Facebook terhadap Wacana Kenaikan Pajak Pasca Pilpres 2024 pada Media Pemberitaan. AKSAYA (Jurnal Rumpun Akuntansi Publik), 1(1), 01–08.

Bhoir, H., & Jayamalini, K. (2021). Implementation of Visual Sentiment Analysis on Flickr Images. SSRN. Https://Doi.Org/ .

Buntoro, G. A. (2018). Analisis Sentimen Calon Gubernur Jawa Timur 2018 di X. Scientific Computing Science Informatics Journal, 1(2), 37–45.

Diyasa, I. G. S. M., Marini Mandenni, N. M. I., Fachrurrozi, M. I., Pradika, S. I., Nur Manab, K. R., & Sasmita, N. R. (2021). X Sentiment Analysis as an Evaluation and Service Base on Python Textblob. IOP Conference Series: Materials Science and Engineering.

Elislah, N. (2023). Discourse network analysis on delaying elections in President Joko Widodo’s era. Jurnal ASPIKOM, 8(2), 225–240.

Garg, S., Chaturvedi, D., Jain, T., Mishra, A., & Kapoor, A. (2023). Navigating the Challenges of Sentiment Analysis: Accuracy and Bias. Research Square.

Hasan, K. M. A., Shovon, S. D., Joy, N. H., & Islam, M. S. (2021). Automatic Labeling of X Data for Developing COVID-19 Sentiment Dataset. In 2021 5th International Conference on Electrical Information and Communication Technology (EICT).

Hendriyanto, A., Purwasito, A., Rais, W. A., & Hastjarjo, S. (2023). The local online media professionalism over candidate framing inside Pacitan election 2020. Jurnal ASPIKOM, 8(1), 137–152.

Hidayat, E. Y., Hardiansyah, R. W., & Affandy, A. (2021). Analisis Sentimen X untuk Menilai Opini Terhadap Perusahaan Publik Menggunakan Algoritma Deep Neural Network. Jurnal Nasional Teknologi Dan Sistem Informasi, 7(2), 108–118.

Hutto, C. J., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the 8th International Conference on Weblogs and Social Media (ICWSM 2014, 1–10.

Iyengar, S., Sood, G., & Lelkes, Y. (2012). Affect, not ideology: A social identity perspective on polarization. Public Opinion Quarterly, 76(3), 405–431.

Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning , 137–142.

Joseph, T. (2024). Natural language processing (NLP) for sentiment analysis in social media. International Journal of Computer Engineering (IJCE).

Khatami, M. I. , & others. (2021). Eksistensi “Baliho 2024” dalam Pertarungan Elektabilitas: Analisis Pencitraan Tokoh Politik. Jurnal Ilmu Sosial Dan Ilmu Politik Universitas Jambi, 5(2), 14–24.

Kottala, S. Y., Gowda, D., Sindhu, D., Soni, H., Mukherjee, S., & Madhu, G. (2024). Enhancing accuracy in social media sentiment analysis through comparative studies using machine learning techniques. In Proceedings of the International Conference on Knowledge Engineering and Computer Science (ICKECS).

Kurniawan, D. E., & Fatulloh, A. (2017). Clustering of Social Conditions in Batam, Indonesia Using K-Means Algorithm and Geographic Information System. International Journal of Earth Sciences and Engineering, 10(05), 1076–1080.

Majbur, R. F., Yanuar, F., & Devianto, D. (2024). PENERAPAN METODE NAÏVE BAYES CLASSIFIER DALAM MENGANALISIS SENTIMEN PADA MEDIA SOSIAL X TERHADAP PILPRES 2024 DI INDONESIA. Lebesgue: Jurnal Ilmiah Pendidikan Matematika, Matematika Dan Statistika, 5(3), 2013–2025.

Markham, C. M., & Buchanan, C. M. (2012). Becoming a parent: The social construction of parental identity. Handbook of Identity Theory and Research.

McCombs, M. E., & Shaw, D. L. (1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36(2), 176–187.

Noelle-Neumann, E. (1974). The spiral of silence: A theory of public opinion. Journal of Communication, 24(2), 43–51.

Nurfaizah Prawitasari, I. D., & Fathuzaen. (2018). Analisis Sentimen X terhadap Opini Publik Pemilihan Kepala Daerah. In Proceedings of CITISEE, 299–303.

Pokharel, B. P. (2020). X sentiment analysis during COVID-19 outbreak in Nepal. SSRN. Https://Doi.Org/10.2139/Ssrn.3624719.

Putri, T. A. M., Enri, U., & Sari, B. N. (2020). Analisis Algoritma Naïve Bayes Classifier untuk Klasifikasi Tweet Pelecehan Seksual dengan #MeToo. Indonesian Journal of Computing and Information Technology, 5(2).

Ramesh, S. (2021). Sentimental Analysis for Political Polarization Using VADER Sentiment Lexicon. Journal of Xidian University, 15(5).

Ramos, J. , & others. (2003). Using TF-IDF to determine word relevance in document classification. In Proceedings of the 1st International Conference on Machine Learning and Data Mining in Pattern Recognition, 133–142.

Suhawni, S., & et al. (2024). VADER’s lexicon for social media slang. Computational Linguistics, 50(1), 64–87.

Sunstein, C. R. (2001). Republic. com. Princeton University Press.

Tsfati, Y., & Cappella, J. N. (2003). Do people watch what they do not trust? Exploring the association between news media skepticism and exposure. Communication Research, 30(5), 504–529.

Vonega, D. A., Fadila, A., & Kurniawan, D. E. (2022). Analisis Sentimen Twitter terhadap Opini Publik atas Isu Pencalonan Puan Maharani dalam PILPRES 2024. Journal of Applied Informatics and Computing (JAIC), 6(2), 129–135.

Yunanto, F., Rudiyanto, M., Kasanova, R., & Alfia. (2024). Analisis Kritis Konstruksi Narasi Media dan Persepsi Mahasiswa Terhadap Pilpres 2024: Studi Kasus di Universitas Madura. J-CEKI : Jurnal Cendekia Ilmiah, 4(1), 1854–1861.




DOI: https://doi.org/10.32509/wacana.v%25vi%25i.4937

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Indexed by:

      

 Recommended Tools :

 

Wacana: Jurnal Ilmiah Ilmu Komunikasi

Fakultas Ilmu KomunikasiUniversitas Prof. Dr. Moestopo (Beragama)
Kampus I, Jl. Hang Lekir I/8 Jakarta Pusat, Indonesia 10270
WA: 085714422271 (Chat Only)

email: wacana@dsn.moestopo.ac.id

Copyright (c) 2025 Wacana: Jurnal Ilmiah Ilmu Komunikasi

Licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.