Narasi Presiden Indonesia: Analisis Wacana Politik Menggunakan BERTopic dalam Mengungkap Pola Tematik Pidato Presiden
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Uliyatunisa Uliyatunisa
*
Universitas Pamulang, Tangerang Selatan,
Indonesia
- Tukiyat Tukiyat Universitas Pamulang, Tangerang Selatan, Indonesia
- Arya Adhyaksa Waskita Universitas Pamulang, Tangerang Selatan, Indonesia
- Murni Handayani Universitas Pamulang, Tangerang Selatan, Indonesia
- Rafi Mahmud Zain Universitas Pamulang, Tangerang Selatan, Indonesia (*) Corresponding Author
Abstract
The speeches of the President of Indonesia play an important role as a means of political communication, policy delivery, and leadership image building in front of the public. However, the increasing volume of speeches presents new challenges in the manual analysis process, as it is time-consuming and prone to researcher subjectivity. This study offers a solution by using BERTopic, a transformer-based topic modelling method that utilises semantic representations from modern embedding models. The research data consists of transcripts of President Joko Widodo's official speeches obtained from the Cabinet Secretariat portal. To improve the quality of semantic representations, this study compares several Indonesian language embedding models, namely DistilBERT, NusaBERT, IndoE5, and SBERT. The analysis process was carried out through the stages of data preprocessing, embedding formation, dimension reduction, clustering, and model evaluation using topic coherence metrics. The objectives of this study were to reveal the themes contained in the President's speeches and to evaluate the effectiveness of embedding models in producing more coherent topics. The results show twenty main themes that consistently appear, including infrastructure development, economic policy, health and the pandemic, digital transformation, international diplomacy, sports, nationalism issues, and regional development. In terms of performance, SBERT provides the best results with a coherence value of UMass = -2.036 and NPMI = 0.082, indicating a positive semantic relationship. A UMass value close to zero indicates greater coherence of words within a topic, while an NPMI value above zero indicates that the connections between words are more easily understood by humans. This research contributes to the development of NLP-based political discourse studies in Indonesia, providing an empirical overview of the selection of appropriate embedding models in topic modelling and opening up opportunities for the integration of similar methods in public policy analysis.
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References
I. L. Alamsyah, N. Aulya, and S. H. Satriya, “Transformasi media dan dinamika komunikasi dalam era digital: Tantangan dan peluang ilmu komunikasi,” J. Ilm. Res. Student, vol. 1, no. 3, pp. 168–181, 2024, doi: https://doi.org/10.61722/jirs.v1i3.554.
A. K. N. Oktavianaa, N. A. S. ERa, I. B. M. Mahendraa, I. G. S. Astawaa, I. G. A. Wibawaa, and I. K. A. Mogia, “Pemodelan Topik Artikel Berita Menggunakan Structural Topic Model dan Latent Dirichlet Allocation,” J. Elektron. Ilmu Komput. Udayana p-ISSN, vol. 2301, p. 5373, 2022, doi: 10.24843/JLK.2023.v11.i03.p02.
D. Grootendorst, “BERTopic: Neural Topic Modeling with a Class-Based TF-IDF Procedure,” arXiv Prepr. arXiv2203.05794, 2022, doi: https://doi.org/10.48550/arXiv.2203.05794.
D. D. Harmoko and P. W. Purwaningrum, “Simbolisme Politik dan Narasi Keberlanjutan: Studi Analisis Wacana Kritis Pidato Presiden RI Ke 7 Joko Widodo,” JURNALISTRENDi J. Linguist. SASTRA, DAN Pendidik., vol. 10, no. 1, pp. 54–66, 2025, doi: https://doi.org/10.51673/jurnalistrendi.v10i1.2415.
D. Y. M. Putri and A. S. A. Sabardila, “Kohesi Gramatikal dan Leksikal Pidato Presiden Jokowi Tentang Penyematan Pangkat Jendral Kehormatan Kepada Prabowo Subianto,” J. Pendidik. Rokania, vol. 9, no. 3, pp. 350–361, 2024, doi: https://doi.org/10.37728/jpr.v9i3.1098 putri2024.
A. M. Alam, “Analisis Wacana Kritis pada Pidato Presiden Tahun 2022: Model Norman Fairclough,” J. Onoma Pendidik., vol. 10, no. 1, 2024, doi: https://doi.org/10.30605/onoma.v10i1.3163.
S. Anggai, R. M. Zain, T. Tukiyat, and A. A. Waskita, “Enhancing BERTopic with Neural Network Clustering for Thematic Analysis of US Presidential Speeches,” J. Tek. Inform., vol. 6, no. 4, pp. 1957–1970, 2025, doi: https://doi.org/10.52436/1.jutif.2025.6.4.5090.
R. M. Zain, S. Anggai, Tukiyat, A. Musyafa, and A. A. Waskita, “Revealing a Country ’ s Government Discourse Through BERT-based Topic Modeling in the US Presidential Speeches,” 2024 Int. Conf. Comput. Control. Informatics its Appl., vol. 11, pp. 191–196, 2024, doi: 10.1109/IC3INA64086.2024.10732578.
N. C. Hellwig, J. Fehle, M. Bink, T. Schmidt, and C. Wolff, “Exploring Twitter discourse with BERTopic: topic modeling of tweets related to the major German parties during the 2021 German federal election,” Int. J. Speech Technol., vol. 27, no. 4, pp. 901–921, 2024, doi: https://doi.org/10.1007/s10772-024-10142-4.
K. Sakiyama, L. de Souza Rodrigues, B. M. Nogueira, E. T. Matsubara, and R. A. F. Romero, “A Framework for Controversial Political Topics Identification Using Twitter Data,” in Brazilian Conference on Intelligent Systems, 2023, pp. 283–298. doi: https://doi.org/10.1007/978-3-031-45392-2_19.
J. G. Gutiérrez, M. Fernandez-de-Retana, and A. Bilbao-Jayo, “A Transformer-Based Approach to Analyzing Public Opinion and Political Trends,” in 2025 10th International Conference on Smart and Sustainable Technologies (SpliTech), 2025, pp. 1–6. doi: 10.23919/SpliTech65624.2025.11091652.
E. Księżniak and M. Sawiński, “Political Narratives and Misinformation During the COVID-19 Pandemic: A Comparative Analysis of Polish Political Parties on Twitter,” in International Conference on Business Information Systems, 2025, pp. 125–133. doi: https://doi.org/10.1007/978-3-031-94193-1_10.
P. S. Suryadjaja and R. Mandala, “Improving the performance of the extractive text summarization by a novel topic modeling and sentence embedding technique using sbert,” in 2021 8th International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA), 2021, pp. 1–6. doi: https://doi.org/10.1109/ICAICTA53211.2021.9640295.
H. U. Khan, A. Naz, F. K. Alarfaj, and N. Almusallam, “Identifying artificial intelligence-generated content using the DistilBERT transformer and NLP techniques,” Sci. Rep., vol. 15, no. 1, p. 20366, 2025, doi: https://doi.org/10.1038/s41598-025-08208-7.
T. D. Purnomo and J. Sutopo, “Comparison of Pre-Trained Bert-Based Transformer Models for Regional Language Text Sentiment Analysis in Indonesia,” Int. J. Sci. Technol., vol. 3, no. 3, pp. 11–21, 2024, doi: https://doi.org/10.56127/ijst.v3i3.1739.
A. S. Kazmali and A. Sayar, “Web Scraping: Legal and Ethical Considerations in General and Local Context - A Review,” Procedia Comput. Sci., vol. 259, no. C, pp. 1563–1572, Jun. 2025, doi: 10.1016/j.procs.2025.04.111.
M. A. Abdurrazzaq, “Analisis Ulasan Aplikasi MyPertamina Menggunakan Topic Modeling dengan Latent Dirichlet Allocation,” J. Sains dan Teknol., vol. 10, no. 1, doi: https://doi.org/10.53008/kalbiscientia.v10i1.694.
D. Maulidiya, “Topic Modelling using Latent Dirichlet Allocation (LDA) to Investigate the Latent Topics of Mathematical Creative Thinking Research in Indonesia,” J. Intell. Comput. Heal. Informatics, vol. 3, pp. 35–46, 2022, doi: 10.26714/jichi.v3i2.11428.
B. A. Tondang, Muhammad Rizqan Fadhil, Muhammad Nugraha Perdana, Akhmad Fauzi, and Ugra Syahda Janitra, “Analisis pemodelan topik ulasan aplikasi BNI, BCA, dan BRI menggunakan latent dirichlet allocation,” INFOTECH J. Inform. Teknol., vol. 4, no. 1, pp. 114–127, Jun. 2023, doi: 10.37373/infotech.v4i1.601.
Z. Kastrati, A. L. I. S. Imran, S. M. Daudpota, M. A. Memon, and M. Kastrati, “Soaring Energy Prices : Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With Transformers,” IEEE Access, vol. 11, no. February, pp. 26541–26553, 2023, doi: 10.1109/ACCESS.2023.3257283.
H. Son and Y. E. Park, “Agenda-setting effects for covid-19 vaccination: Insights from 10 million textual data from social media and news articles using BERTopic,” Int. J. Inf. Manage., vol. 83, no. February, p. 102907, 2025, doi: 10.1016/j.ijinfomgt.2025.102907.
H. S. Jung, H. Lee, and J. H. Kim, “Unveiling Cryptocurrency Conversations: Insights From Data Mining and Unsupervised Learning Across Multiple Platforms,” IEEE Access, vol. 11, no. November, pp. 130573–130583, 2023, doi: 10.1109/ACCESS.2023.3334617.
H. Lim, Q. Li, S. Yang, and J. Kim, “A BERT-Based Multi-Embedding Fusion Method Using Review Text for Recommendation,” Expert Syst., vol. 42, no. 5, p. e70041, 2025, doi: https://doi.org/10.1111/exsy.70041.
Y. An, D. Kim, J. Lee, H. Oh, J. S. Lee, and D. Jeong, “Topic Modeling-Based Framework for Extracting Marketing Information From E-Commerce Reviews,” IEEE Access, vol. 11, no. December, pp. 135049–135060, 2023, doi: 10.1109/ACCESS.2023.3337808.
A. Mulia and A. R. Dzikrillah, “Analisis Perbedaan Pendapat Netizen Indonesia tentang Presiden Jokowi sebelum dan sesudah Kenaikan Harga BBM Analysis of Indonesian Netizens’ Dissent on President Jokowi before and after Fuel Price Increase,” J. Comput. Eng. Syst. Sci., vol. 8, no. 2, pp. 318–328, 2023, doi: https://doi.org/10.24114/cess.v8i2.45319.
D. Aryani, I. L. Kharisma, and A. Sujjada, “Topic Modeling of the 2024 Election Using the BERTopic Method on Detik . com News Articles,” Inf. J. Ilm. Bid. Teknol. Inf. dan Komun., vol. 9, no. 2, pp. 171–180, 2024, doi: https://doi.org/10.25139/inform.v9i2.8429.
S. Umamaheswaran, V. Dar, E. Sharma, and J. S. Kurian, “Mapping Climate Themes from 2008-2021 - An Analysis of Business News Using Topic Models,” IEEE Access, vol. 11, no. February, pp. 26554–26565, 2023, doi: 10.1109/ACCESS.2023.3256530.
J. Song, Y. Yuan, K. Chang, B. Xu, J. Xuan, and W. Pang, “Navigating Public Sentiment in the Circular Economy through Topic Modelling and Hyperparameter Optimisation,” Energy AI, vol. 18, no. May, p. 100433, 2024, doi: 10.1016/j.egyai.2024.100433.
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Copyright (c) 2025 Uliyatunisa Uliyatunisa, Tukiyat Tukiyat, Arya Adhyaksa Waskita, Murni Handayani, Rafi Mahmud Zain
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