Multiplatform Topic Modeling Analysis of Gender-Based Violence in Indonesia Using LDA

Annisa Putri Patricia; Nina Setiyawati; Dwi Hosanna Bangkalang

doi:10.34148/teknika.v15i2.1478

Authors

Annisa Putri Patricia Informatics Engineering, Faculty of Information Technology, Satya Wacana Christian University, Salatiga, Central Java, Indonesia
Nina Setiyawati Informatics Engineering, Faculty of Information Technology, Satya Wacana Christian University, Salatiga, Central Java, Indonesia
Dwi Hosanna Bangkalang Information System, Faculty of Information Technology, Satya Wacana Christian University, Salatiga, Central Java, Indonesia

DOI:

https://doi.org/10.34148/teknika.v15i2.1478

Keywords:

Gender-based Violence, LDA, Social Media, Web Scraping, Topic Modeling

Abstract

Gender-based violence remains a critical social issue in Indonesia, generating substantial public discourse across digital platforms. This study applies Latent Dirichlet Allocation (LDA)-based topic modeling to identify and compare the dominant themes emerging in online discussions of gender-based violence across three platforms: X (formerly Twitter), YouTube, and the news portal Detik.com. A total of 21,000 data points were collected through keyword-based web scraping covering the period January 2020 to October 2025. The two-phase modeling process yielded coherence scores of 0.38 and 0.53 for X, 0.77 and 0.73 for YouTube, and 0.8 and 0.52 for Detik.com across the first and second phases, respectively, reflecting differences in language register and content structure across platforms. A two-phase modeling approach was employed for each platform to progressively refine topic quality and eliminate irrelevant outputs. The results show that LDA successfully identified platform-specific thematic patterns: X captured emotional and psychological dimensions of gender-based violence, YouTube surfaced legislative and advocacy-oriented discourse centered on the PKS Law, and Detik.com produced case-specific topics grounded in legal proceedings and journalistic reporting. Across all three platforms, domestic violence emerged as the most consistently prominent theme. These findings demonstrate that a multi-platform approach yields a more comprehensive and nuanced picture of public discourse on gender-based violence than any single-source analysis, and that LDA is an effective tool for large-scale thematic extraction from heterogeneous online text data.

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References

[1] Komnas Perempuan, "CATAHU 2024: Menata Data, Menajamkan Arah," Jakarta, Mar. 2025.

[2] Komnas Perempuan, "CATAHU 2023: Momentum Perubahan," Jakarta, Mar. 2024.

[3] Komnas Perempuan, "CATAHU 2022: Kekerasan terhadap Perempuan di Ranah Publik dan Negara," Jakarta, Mar. 2023.

[4] N. Hayati, "Media Sosial dan Kekerasan Berbasis Gender Online Selama Pandemi Covid-19," Jurnal Hukum, Humaniora, Masyarakat, dan Budaya, vol. 1, no. 1, pp. 43–52, 2021.

[5] S. Sari, W. Dh, R. Furqan, and F. Hidayatullah, "The Role of Social Media Among Women Survivors of Sexual Violence," Jurnal Visi Komunikasi, vol. 24, no. 02, pp. 115–125, 2025.

[6] E. Ratnasari, S. Sumartias, and R. Romli, “Social Media, Digital Activism, and Online Gender-Based Violence in Indonesia,” Nyimak J. Commun., vol. 5, no. 1, pp. 97–116, 2021.

[7] Komnas Perempuan, "CATAHU 2025: Menguatkan Data, Mengatasi Kerentanan," Jakarta, Mar. 2026.

[8] I. A. Siregar, F. A. S. N. Haq, "Analisis Sentimen dan Topik Kekerasan Seksual Perempuan di X 2022-2025 Berbasis Naïve Bayes-LDA," JOISIE (Journal of Information Systems and Informatics Engineering), vol. 9, no. 2, pp. 380–391, 2025.

[9] L. Adhrianti, Yayantika, and N. C. Marlina, "Peran Media Terhadap Fenomena Victim-Blaming dalam Kekerasan Seksual Berbasis Gender," Jurnal Riset Jurnalistik dan Media Digital, vol. 4, no. 2, pp. 141–150, 2024.

[10] Musliadi KH, H. Zainuddin, and Y. Wabula, "Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation Algorithm," Journal of Applied Engineering and Technological Science, vol. 4, no. 1, pp. 390–399, 2022.

[11] Z. Munawarah, "Analisis Framing Berita Kekerasan Berbasis Gender Online di Media Liputan6.com dan Antaranews.com," Medkom: Jurnal Media dan Komunikasi, vol. 4, no. 2, pp. 139–153, 2024.

[12] R. Hammad, V. C. Hardita, and A. Z. Amrullah, “Topic modeling and sentiment analysis about Mandalika on social media using the latent Dirichlet allocation method,” MATRIX J. Manaj. Teknol. dan Inform., vol. 12, no. 3, pp. 109–116, Nov. 2022, doi: 10.31940/matrix.v12i3.109-116.

[13] N. Royani, C. E. Widodo, and B. Warsito, “Topic Modelling Latent Dirichlet Allocation untuk Klasifikasi Komentar pada Layanan Streaming Platform,” JST (Jurnal Sains dan Teknol., vol. 12, no. 3, Jan. 2024, doi: 10.23887/jstundiksha.v12i3.68492.

[14] A. D. Arko, M. Yusril, H. Setyawan, and R. Andarsyah, “Topic Modeling for Constructing Learning Profiles using Latent Dirichlet Allocation and Coherence Evaluation,” JUTI J. Ilm. Teknol. Inf., vol. 23, no. 2, pp. 126–143, 2025, doi: 10.12962/j24068535.v23i1.a1301.

[15] B. B. Wahyujati and F. A. Herawati, "Femicide Framing and Ethics Violation on Indonesian Instagram News Contents," Jurnal Ilmu Komunikasi, vol. 22, no. 2, pp. 237–258, 2025.

[16] D. Choirinnisa, F. Alzami, H. Indrayani, A. Rohmani, S. H. Nugraini, R. Zulfiningrumi, et al., "LDA Topic Modeling: Twitter-Based Public Opinion on Indonesian Ministry of Finance," Sinkron: Jurnal dan Penelitian Teknik Informatika, vol. 9, no. 2, pp. 849–863, 2025.

[17] B. D. Santosa, N. Fatimah, N. I. Kusumaningtyas, and U. S. Aesyi, "Analisis Kepercayaan Masyarakat Tentang Kepolisian Indonesia di Twitter Menggunakan Latent Dirichlet Allocation (LDA)," Indonesian Journal on Data Science, vol. 1, no. 2, pp. 66–76, 2023.

[18] Z. A. Rana, I. T. Haritza, M. A. Fadhillah, and D. A. Purnama, "Analysis of Public Opinion on Disability Services Using Sentiment Analysis and Latent Dirichlet Allocation Topic Modeling Methods," Jurnal Teknologi Sistem Informasi dan Aplikasi, vol. 8, no. 3, pp. 151–161, 2025.

[19] M. R. Fahlevvi and Azhari, "Topic Modeling on Online News Portal Using Latent Dirichlet Allocation (LDA)," IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 16, no. 4, pp. 335–344, 2022.

[20] E. S. Negara and D. Triadi, "Topic Modeling Using Latent Dirichlet Allocation (LDA) on Twitter Data with Indonesia Keyword," Bulletin of Social Informatics Theory and Application, vol. 5, no. 2, pp. 124–132, 2021.

[21] M. Wetzels, R. Wetzels, E. Schweiger, and D. Grewal, "How Topic Modeling Can Spur Innovation Management," Journal of Product Innovation Management, vol. 42, pp. 921–946, 2025.

[22] J. W. Fernanda, "Pemodelan Persepsi Pembelajaran Online Menggunakan Latent Dirichlet Allocation," Statistika, vol. 9, no. 2, 2021.

[23] T. Titiana and D. H. Bangkalang, “Analisis Dan Penerapan Topic Modeling Pada Judul Tugas Akhir Mahasiswa Menggunakan Metode Latent Dirichlet Allocation (LDA),” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 8, no. 4, pp. 1275–1287, 2023.

[24] D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent Dirichlet Allocation," Journal of Machine Learning Research, vol. 3, pp. 993–1022, 2003.

[25] D. Rifaldi, A. Fadlil, and Herman, "Teknik Preprocessing pada Text Mining Menggunakan Data Tweet 'Mental Health',"DECODE: Jurnal Pendidikan Teknologi Informasi, vol. 3, no. 2, pp. 161–171, 2023.

[26] E. Puspita, D. F. Shiddieq, and F. F. Roji, "Pemodelan Topik pada Media Berita Online Menggunakan Latent Dirichlet Allocation (Studi Kasus Merek Somethinc)," MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 2, pp. 481–489, 2024.

[27] H. T. Saputra, A. Damanik, M. M. Shaquille, M. A. Rusydi, M. H. Riziq, and N. S. Zulva, "Analisis Modelling pada Reviews Lazada Indonesia Menggunakan Latent Dirichlet Allocation (LDA) untuk Optimalisasi Strategi Bisnis," JEKIN (Jurnal Teknik Informatika), vol. 5, no. 1, 2025.

[28] Y. Matira, Junaidi, and I. Setiawan, "Pemodelan Topik pada Judul Berita Online Detikcom Menggunakan Latent Dirichlet Allocation," Estimasi: Journal of Statistics and Its Application, vol. 4, no. 1, pp. 53–63, 2023.

[29] M. A. Landers-Potts, K. A. S. Wickrama, L. G. Simons, C. Cutrona, F. X. Gibbons, R. L. Simons, et al., "An Extension and Moderational Analysis of the Family Stress Model Focusing on African American Adolescents," Family Relations: Interdisciplinary Journal of Applied Family Studies, vol. 64, no. pp. 233–248, 2015.

[30] J. Y. Modiano, "Pengaruh Budaya Patriarki dan Kaitannya dengan Kekerasan dalam Rumah Tangga," Jurnal Sapientia et Virtus, vol. 6, no. 2, pp. 129–140, 2021.

[31] M. Janah, P. Pusvitasari, N. Shafira, P. N. Dewi, and N. K. Zahra, "Studi Kasus: Dampak Psikologis terhadap Korban Kekerasan dalam Rumah Tangga (KDRT) dan Implikasinya dalam Proses Peradilan," Jurnal Ilmiah Penelitian Mandira Cendikia, vol. 3, no. 4, 2025.