Analisis Sentimen terhadap Pemerintahan Prabowo–Gibran menggunakan IndoBERT dan LDA

Sahrial Ihsani Ishak, Okma Arnilia, Tri Widodo, I Gusti Nyoman Agung Bisma Tatwa

Abstract


This study analyzes public perception of the Prabowo–Gibran administration through online news coverage using a Natural Language Processing (NLP) approach. Data were collected from credible news portals such as Indonesia News and Detik, totaling 195 articles. The analysis was conducted in two stages: first, IndoBERT was used to classify the sentiment into positive, negative, and neutral; second, Latent Dirichlet Allocation (LDA) was applied to identify the main topics driving rage. Sentiment results showed that most topics, particularly those related to the economy, public policy, and governance, were dominated by negative sentiment (80%), while positive sentiment accounted for 15.9% and neutral sentiment for 4.1%. These findings indicate public criticism and concern regarding the effectiveness of policies and economic stability. The combined IndoBERT and LDA approach proved effective in providing a comprehensive understanding of public opinion dynamics in the digital era. It can serve as a consideration for the government in formulating more responsive and transparent communication strategies.

Penelitian ini menganalisis persepsi publik terhadap kepemimpinan Prabowo–Gibran melalui pemberitaan media online menggunakan pendekatan Natural Language Processing (NLP). Data dikumpulkan dari portal berita kredibel seperti Antara News dan Detik dengan total 195 artikel. Analisis dilakukan dalam dua tahap: pertama, IndoBERT digunakan untuk mengklasifikasikan sentimen berita menjadi positif, negatif, dan netral; kedua, Latent Dirichlet Allocation (LDA) diterapkan untuk mengidentifikasi topik utama yang mendominasi pemberitaan. Hasil sentimen menunjukkan bahwa sebagian besar topik, terutama terkait ekonomi, kebijakan publik, dan pemerintahan, didominasi oleh sentimen negatif (80%), sedangkan sentimen positif tercatat 15,9% dan netral 4,1%. Temuan ini mengindikasikan adanya kritik dan keprihatinan publik terhadap efektivitas kebijakan dan stabilitas ekonomi. Hasil menunjukkan bahwa sebagian besar topik, terutama terkait ekonomi, kebijakan publik, dan pemerintahan, didominasi oleh sentimen negatif. Temuan ini mengindikasikan adanya kritik dan keprihatinan publik terhadap efektivitas kebijakan dan stabilitas ekonomi. Pendekatan kombinatif IndoBERT dan LDA terbukti efektif dalam memberikan pemahaman komprehensif mengenai dinamika opini publik di era digital, serta dapat menjadi bahan pertimbangan bagi pemerintah dalam merumuskan strategi komunikasi yang lebih responsif dan transparan.


Keywords


Analisis sentimen; IndoBERT; LDA; Media online; Prabowo-Gibran

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References


Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation: Extracting Topics from Software Engineering Data. Journal of Machine Learning Research, 3, 993–1022. https://doi.org/10.1016/B978-0-12-411519-4.00006-9

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 1(Mlm), 4171–4186.

Dewan Pers. (2025). Daftar Perusahaan Pers Terverifikasi Administrasi dan Faktual. Diakses pada 27 Oktober 2025.

Feldman, R. (2013). Techniques and applications for sentiment analysis. Commun. ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274

Foerderer, J. (2023). Should we trust web-scraped data? https://doi.org/10.48550/arXiv.2308.02231. Diakses pada 27 Oktober 2025.

Jurafsky, D., & Martin, J. H. (2025). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models (3rd ed.). https://web.stanford.edu/~jurafsky/slp3. Diakses pada 27 Oktober 2025.

Koto, F., Rahimi, A., Lau, J. H., & Baldwin, T. (2020). IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP. COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference, 757–770. https://doi.org/10.18653/v1/2020.coling-main.66

Liu, B. (2015). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press. https://doi.org/DOI: 10.1017/CBO9781139084789

Mandhasiya, D. G., Murfi, H., Bustamam, A., & Anki, P. (2022). Evaluation of Machine Learning Performance Based on BERT Data Representation with LSTM Model to Conduct Sentiment Analysis in Indonesian for Predicting Voices of Social Media Users in the 2024 Indonesia Presidential Election. 2022 5th International Conference on Information and Communications Technology (ICOIACT), 441–446. https://doi.org/10.1109/ICOIACT55506.2022.9972206

Manoppo, M. R., Kolang, I. C., Fiat, D. N., Michelly, R., & Mawara, C. (2025). Analisis Sentimen Publik Di Media Sosial Terhadap Kenaikan Ppn 12 % Di Indonesia Menggunakan Indobert Analysis of Public Sentiment on Social Media Towards the 12 % Ppn Increase in Indonesia Using Indobert. 4(2), 152–163. https://repository.uinjkt.ac.id/dspace/handle/123456789/76873. Diakses pada 27 Oktober 2025.

Naury, C., Fudholi, D. H., & Hidayatullah, A. F. (2021). Topic Modelling pada Sentimen Terhadap Headline Berita Online Berbahasa Indonesia Menggunakan LDA dan LSTM. Jurnal Media Informatika Budidarma, 5(1), 24. https://doi.org/10.30865/mib.v5i1.2556

Puspita, E., Shiddieq, D. F., & Roji, F. F. (2024). Topic Modeling on Online News Media Using Latent Diriclet Allocation (Case Study Somethinc Brand). MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(2), 481–489. https://doi.org/10.30865/mib.v5i1.2556

Rakhmawati, N. A., Cisatra, A., Ansori, D. D. M., Akmal, D. N. F. A., & Ramadhani, S. (2024). Identifikasi Topik Hangat di Media Berita Menggunakan Latent Dirichlet Allocation. Journal of Information Engineering and Educational Technology, 8(1), 14–17. https://doi.org/10.26740/jieet.v8n1.p14-17

Ramadhan, C., Atina, V., & Permatasari, H. (2025). Analisis Perbandingan Model CNN dan IndoBERT Dalam Sentimen Berita Politik Indonesia. 110–118. https://doi.org/10.47701/v1r9ka69

Syafutra, A. D., & Kusrini, K. (2025). Analisis Sentimen Omnibus Law di Twitter dengan Machine Learning dan Teknik Resampling. Jambura Journal of Informatics, 1(1), 22–35. https://doi.org/10.37905/jji.v1i1.30935

Ulloa, R., Mangold, F., Schmidt, F., Gilsbach, J., & Stier, S. (2025). Beyond time delays: how web scraping distorts measures of online news consumption. Communication Methods and Measures, 19(3), 179–200. https://doi.org/10.1080/19312458.2025.2482538




DOI: https://doi.org/10.37905/jji.v1i2.34895

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