Keywords
discursive patterns
dominant themes
Makan Bergizi Gratis (MBG)
text analytics
How to Cite
Abstract
This study investigates public discourse on Indonesia's Free Nutritious Meals (Makan Bergizi Gratis/MBG) program through Latent Dirichlet Allocation (LDA) topic modeling of YouTube comments. Filling a research gap on online public opinion regarding the MBG policy, this study identifies dominant themes and discursive patterns in public perception. A three-topic model, validated through coherence score evaluation and pyLDAvis visualization, reveals key topics: concerns over food prices and distribution, perceived benefits for children and society, and emotionally and politically driven reactions. The findings provide valuable insights into public opinion, while also highlighting challenges in processing Indonesian-language text, such as informal language and noisy data. This study contributes to understanding public perceptions of social policies in digital environments and recommends future research directions, including improved text preprocessing and alternative topic modeling approaches. By shedding light on online public discourse, this research informs policymakers and stakeholders about the effectiveness and potential areas for improvement in the MBG program.
References
M. Thelwall, P. Sud, and F. Vis, “Commenting on YouTube videos: From guatemalan rock to El Big Bang,” J. Am. Soc. Inf. Sci., vol. 63, no. 3, pp. 616–629, Mar. 2012, doi: 10.1002/asi.21679.
D. M. Blei, “Probabilistic topic models,” Commun. ACM, vol. 55, no. 4, pp. 77–84, Apr. 2012, doi: 10.1145/2133806.2133826.
P. DiMaggio, M. Nag, and D. Blei, “Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding,” Poetics, vol. 41, no. 6, pp. 570–606, Dec. 2013, doi: 10.1016/j.poetic.2013.08.004.
A. M. G. A., S. Robledo, and M. Zuluaga, “Topic Modeling: Perspectives From a Literature Review,” IEEE Access, vol. 11, pp. 4066–4078, 2023, doi: 10.1109/ACCESS.2022.3232939.
I. Vayansky and S. A. P. Kumar, “A review of topic modeling methods,” Information Systems, vol. 94, p. 101582, Dec. 2020, doi: 10.1016/j.is.2020.101582.
H. Jelodar et al., “Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey,” Dec. 06, 2018, arXiv: arXiv:1711.04305. doi: 10.48550/arXiv.1711.04305.
D. M. Blei, “Latent Dirichlet Allocation,” J. Mach. Learn. Res., vol. 3, pp. 993–1022, 2003.
C. C. Silva, M. Galster, and F. Gilson, “Topic modeling in software engineering research,” Empir Software Eng, vol. 26, no. 6, p. 120, Nov. 2021, doi: 10.1007/s10664-021-10026-0.
U. Detthamrong et al., “Topic Modeling Analytics of Digital Economy Research: Trends and Insights,” J Scientometric Res., vol. 13, no. 2, pp. 448–458, Aug. 2024, doi: 10.5530/jscires.13.2.35.
D. Yu and B. Xiang, “Discovering topics and trends in the field of Artificial Intelligence: Using LDA topic modeling,” Expert Systems with Applications, vol. 225, p. 120114, Sep. 2023, doi: 10.1016/j.eswa.2023.120114.
R. Churchill and L. Singh, “The Evolution of Topic Modeling,” ACM Comput. Surv., vol. 54, no. 10s, pp. 1–35, Jan. 2022, doi: 10.1145/3507900.
A. Gandomi and M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” International Journal of Information Management, vol. 35, no. 2, pp. 137–144, Apr. 2015, doi: 10.1016/j.ijinfomgt.2014.10.007.
A. Krishnan, “Exploring the Power of Topic Modeling Techniques in Analyzing Customer Reviews: A Comparative Analysis,” Aug. 19, 2023, arXiv: arXiv:2308.11520. doi: 10.48550/arXiv.2308.11520.
A. Farea, S. Tripathi, G. Glazko, and F. Emmert-Streib, “Investigating the optimal number of topics by advanced text-mining techniques: Sustainable energy research,” Engineering Applications of Artificial Intelligence, vol. 136, p. 108877, Oct. 2024, doi: 10.1016/j.engappai.2024.108877.
B. Gencoglu, M. Helms-Lorenz, R. Maulana, E. P. W. A. Jansen, and O. Gencoglu, “Machine and expert judgments of student perceptions of teaching behavior in secondary education: Added value of topic modeling with big data,” Computers & Education, vol. 193, p. 104682, Feb. 2023, doi: 10.1016/j.compedu.2022.104682.
T. Irawan, L. Mutawalli, S. Fadli, and W. Bagye, “Topic Modelling Pola Komunikasi Pilpres 2024: Focus Web Scraping dan Latent Dirichlet Allocation,” J. Manaj. Inform. dan Sist. Inform., vol. 7, no. 2, pp. 186–194, 2024, https://doi.org/10.36595/misi.v7i2.1183.
E. Puspita, D. F. Shiddieq, and F. F. Roji, “Pemodelan Topik pada Media Berita Online Menggunakan Latent Dirichlet Allocation (Studi Kasus Merek Somethinc): Topic Modeling on Online News Media Using Latent Diriclet Allocation (Case Study Somethinc Brand),” MALCOM, vol. 4, no. 2, pp. 481–489, Feb. 2024, doi: 10.57152/malcom.v4i2.1204.
Uray Nur Khadijah and Nuri Cahyono, “Analisis Topic Modelling Pariwisata Yogyakarta Menggunakan Latent Dirichlet Allocation (LDA),” ijcs, vol. 13, no. 4, Jul. 2024, doi: 10.33022/ijcs.v13i4.3816.
R. K. Gupta, R. Agarwalla, B. H. Naik, J. R. Evuri, A. Thapa, and T. D. Singh, “Prediction of research trends using LDA based topic modeling,” Global Transitions Proceedings, vol. 3, no. 1, pp. 298–304, Jun. 2022, doi: 10.1016/j.gltp.2022.03.015.
O. Ishmael, E. Kiely, C. Quigley, and D. McGinty, “Topic Modelling using Latent Dirichlet Allocation (LDA) and Analysis of Students Sentiments,” in 2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE), Phitsanulok, Thailand: IEEE, Jun. 2023, pp. 1–6. doi: 10.1109/JCSSE58229.2023.10201965.
J. H. Lee, J. Wood, and J. Kim, “Tracing the Trends in Sustainability and Social Media Research Using Topic Modeling,” Sustainability, vol. 13, no. 3, p. 1269, Jan. 2021, doi: 10.3390/su13031269.
W.-R. Yang and H.-C. Yang, “Topic Modeling Analysis of Social Media Marketing using BERTopic and LDA,” The Journal of Industrial Distribution & Business, vol. 13, no. 9, pp. 37–50, Sep. 2022, doi: 10.13106/JIDB.2022.VOL13.NO9.37.
O. Ozyurt, “Empirical research of emerging trends and patterns across the flipped classroom studies using topic modeling,” Educ Inf Technol, vol. 28, no. 4, pp. 4335–4362, Apr. 2023, doi: 10.1007/s10639-022-11396-8.
H. Özköse, O. Ozyurt, and A. Ayaz, “Management Information Systems Research: A Topic Modeling Based Bibliometric Analysis,” Journal of Computer Information Systems, vol. 63, no. 5, pp. 1166–1182, Sep. 2023, doi: 10.1080/08874417.2022.2132429.
B. Ozyurt and M. A. Akcayol, “A new topic modeling based approach for aspect extraction in aspect based sentiment analysis: SS-LDA,” Expert Systems with Applications, vol. 168, p. 114231, Apr. 2021, doi: 10.1016/j.eswa.2020.114231.
M. E. D. Vanti, V. Octaviani, and M. Maryaningsih, “Analisis Framing Pemberitaan Program Makan Gratis Prabowo Subianto Di Media Online,” I. Kom. dan Adm. Pub., vol. 11, no. 1, Jun. 2024, doi: 10.37676/professional.v11i1.6396.
A. Andin et al., “Penerapan Nilai Pancasila Melalui Program Makan Bergizi Gratis,” Indonesian Journal of Education and Development Research, vol. 3, no. 1, pp. 370–383, Dec. 2024, doi: 10.57235/ijedr.v3i1.4684.
A. Kiftiyah, F. A. Palestina, F. U. Abshar, and K. Rofiah, “Program Makan Bergizi Gratis (MBG) dalam Perspektif Keadilan Sosial dan Dinamika Sosial – Politik,” PJK, vol. 5, no. 1, pp. 101–112, Apr. 2025, doi: 10.52738/pjk.v5i1.726.
R. Qomarrullah, S. Suratni, L. Wulandari S, and M. Sawir, “Dampak Jangka Panjang Program Makan Bergizi Gratis terhadap Kesehatan dan Keberlanjutan Pendidikan,” IJI_Publication, vol. 5, no. 2, pp. 130–137, Mar. 2025, doi: 10.51577/ijipublication.v5i2.660.
P. A. Maharani, A. R. Namira, and T. V. Chairunnisa, “Peran Makan Siang Gratis Dalam Janji Kampanye Prabowo Gibran Dan Realisasinya,” JOLASOS, vol. 1, no. 1, pp. 1–10, Jun. 2024, doi: 10.70656/jolasos.v1i1.79.
A. A. Merlinda and Yusmar Yusuf, “Analisis Program Makan Gratis Prabowo Subianto Terhadap Strategi Peningkatan Motivasi Belajar Siswa di Sekolah Tinjauan dari Perspektif Sosiologi Pendidikan,” RRJ, vol. 7, no. 2, pp. 1364–1373, Jan. 2025, doi: 10.38035/rrj.v7i2.1360.
B. Rahmatullah, S. A. Saputra, P. Budiono, and D. P. Wigandi, “Sentimen Analisis Makan Bergizi Gratis Menggunakan Algoritma Naive Bayes,” JIfoTech, vol. 5, no. 1, Mar. 2025, doi: 10.46229/jifotech.v5i1.978.
R. Egger and J. Yu, “A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts,” Front. Sociol., vol. 7, p. 886498, May 2022, doi: 10.3389/fsoc.2022.886498.
B. Yin and C.-H. Yuan, “Detecting latent topics and trends in blended learning using LDA topic modeling,” Educ Inf Technol, vol. 27, no. 9, pp. 12689–12712, Nov. 2022, doi: 10.1007/s10639-022-11118-0.
K. Stevens, P. Kegelmeyer, D. Andrzejewski, and D. Buttler, “Exploring Topic Coherence over Many Models and Many Topics”.
S. Mifrah, “Topic Modeling Coherence: A Comparative Study between LDA and NMF Models using COVID’19 Corpus,” IJATCSE, vol. 9, no. 4, pp. 5756–5761, Aug. 2020, doi: 10.30534/ijatcse/2020/231942020.
M. Röder, A. Both, and A. Hinneburg, “Exploring the Space of Topic Coherence Measures,” in Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, Shanghai China: ACM, Feb. 2015, pp. 399–408. doi: 10.1145/2684822.2685324.
C. Sievert and K. Shirley, “LDAvis: A method for visualizing and interpreting topics,” in Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, Baltimore, Maryland, USA: Association for Computational Linguistics, 2014, pp. 63–70. doi: 10.3115/v1/W14-3110.
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