PEMODELAN TOPIC PERCAKAPAN PUBLIK MENGENAI KESEHATAN MENTAL REMAJA DI PLATFORM X MENGGUNAKAN METODE LATENT DIRICHLET ALLOCATION
Abstract
The high volume of unstructured conversations regarding adolescent mental health on platform X complicates manual topic identification. This study aims to map these public discourses using Latent Dirichlet Allocation (LDA) within the CRISP-DM framework. The corpus comprises 8,792 Indonesian tweets (combining Apify and Kaggle datasets) processed through noise removal, slang normalization, and bigram detection. Combined Coherence Score evaluation and qualitative interpretation identified an 11-topic model ( = 0.4880) as the optimal architecture. This validity was further corroborated by two independent raters against an alternative model (K=15), confirming the 11-topic model achieved comparable or superior coherence. The most dominant topics were general restlessness with self-soothing efforts (35.26%) and clinical anxiety with daily emotional pressure (18.16%). Furthermore, the study revealed that fandom-related terms are distributed across multiple topics, indicating that online community engagement functions as a cross-cutting element within adolescent mental health discourse.
Downloads
References
WHO, “Mental health of adolescents.” [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/adolescent-mental-health
S. Kemp, “Digital 2024: Indonesia,” 2024. [Online]. Available: https://datareportal.com/reports/digital-2024-indonesia
N. Nurwanda, N. Suarna, and W. Prihartono, “PENERAPAN NLP (NATURAL LANGUAGE PROCESSING) DALAM ANALISIS SENTIMEN PENGGUNA TELEGRAM DI PLAYSTORE,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 2, pp. 1841–1846, Apr. 2024, doi: 10.36040/jati.v8i2.8469.
R. Egger and J. Yu, “A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts,” Frontiers in Sociology, vol. Volume 7-, 2022, doi: 10.3389/fsoc.2022.886498.
S. Surorejo, M. Taufik, F. Maulana, W. Andriani, and G. Gunawan, “Application of the latent dirichlet allocation method to determine news text topics,” Jurnal Mandiri IT, vol. 13, no. 1, pp. 106–115, 2024, [Online]. Available: www.ejournal.isha.or.id/index.php/Mandiri
P. Syahputra and R. Kurniawan, “Analisis Sentimen Terhadap Kesehatan Mental Remaja Menggunakan Metode Naive Bayes,” Journal of Information System Research (JOSH), 2024, doi: 10.47065/josh.v5i4.5644.
R. Chandrasekaran, S. Kotaki, and A. H. Nagaraja, “Detecting and tracking depression through temporal topic modeling of tweets: insights from a 180-day study,” npj Mental Health Research, 2024, doi: 10.1038/s44184-024-00107-5.
F. Bierie, R. Pryss, and A. Aizawa, “Sentiments about Mental Health on Twitter—Before and during the COVID-19 Pandemic,” MDPI healthcare, 2023, doi: 10.3390/healthcare11212893.
T. Timakum, Q. Xie, and S. Lee, “Identifying mental health discussion topic in social media community: subreddit of bipolar disorder analysis,” Frontiers in Sociology, vol. 8, 2023, doi: 10.3390/healthcare11212893.
Cici Suhaeni, L. N. A. Mualifah, and H. Wijayanto, “LDA Topic Modeling Analysis of Public Discourse on Indonesia’s Free Nutritious Meals Program (MBG),” IJID (International Journal on Informatics for Development), vol. 14, no. 1, pp. 587–600, Jun. 2025, doi: 10.14421/ijid.2025.5211.
D. Duei Putri, G. F. Nama, and W. E. Sulistiono, “Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 10, no. 1, Jan. 2022, doi: 10.23960/jitet.v10i1.2262.
C. D. P. Laureate, W. Buntine, and H. Linger, “A systematic review of the use of topic models for short text social media analysis,” Artif. Intell. Rev., vol. 56, no. 12, pp. 14223–14255, Dec. 2023, doi: 10.1007/s10462-023-10471-x.
M. Rüdiger, D. Antons, A. M. Joshi, and T.-O. Salge, “Topic modeling revisited: New evidence on algorithm performance and quality metrics,” PLoS One, vol. 17, no. 4, p. e0266325, Apr. 2022, doi: 10.1371/journal.pone.0266325.
M. Li and T. Yu, “Methodological issues on evaluating agreement between two detection methods by Cohen’s kappa analysis,” Parasit. Vectors, vol. 15, no. 1, p. 270, Dec. 2022, doi: 10.1186/s13071-022-05402-8.
J. J. R. Arizabal and H. J. Yabut, “The Mediating Effect of Social Connectedness in the Relationship Between K-Pop Fandom Identity and Mental Health,” Sage Open, vol. 15, no. 3, Jan. 2025, doi: 10.1177/21582440251369989.
M. R. Amalsyah, D. Kurniawan, A. Rifai, and P. Sari, “Sentiment Analysis of Fintech Application User Reviews using the CRISP-DM Framework for Product Development Prioritization,” SISTEMASI, vol. 14, no. 2, p. 813, Mar. 2025, doi: 10.32520/stmsi.v14i2.5064.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
