Implementasi Algoritma BERTopic Berbasis IndoBERT untuk Pemodelan dan Analisis Trending Topik Berita Online pada Dashboard Website
Abstract
The growing volume of online news makes it difficult for the Sumatera Ekspres editorial team to manually track popular topics in a timely manner. This study implements the BERTopic algorithm based on IndoBERT to automatically model trending topics of online news, following the CRISP-DM stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Data were collected through web scraping from eight national news portals and YouTube, then processed through case folding, cleaning, tokenization, and stopword removal before being modeled using IndoBERT as the embedding, UMAP for dimensionality reduction, HDBSCAN for clustering, and c-TF-IDF for keyword extraction, complemented by a trend_score metric to identify topics that are genuinely gaining momentum. The main contribution of this study lies in integrating more heterogeneous multi-source data than prior work and introducing the trend_score metric as a more accurate indicator of momentum, realized as a ready-to-use dashboard for the editorial team. Testing across four data periods (1, 7, 14, and 30 days) shows that the model consistently produces topics of good coherence and diversity quality. The results were implemented into a website-based dashboard built with FastAPI and SQLite that presents trending topic visualizations, word cloud, and news headline recommendations, which has been tested and confirmed to function as required, making it suitable as a decision-support tool for the editorial team to monitor popular issues quickly, efficiently, and in a data-driven manner.
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References
A. Khaer, N. Khoir, and Y. Arini Hidayati, “Senjakala Media Cetak: Tantangan Jurnalisme Cetak di Era Digital,” TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora, vol. 2, no. 3, pp. 324–331, Nov. 2021, doi: https://doi.org/10.33650/trilogi.v2i3.3080.
N. A. Rakhmawati, A. Cisatra, D. D. M. Ansori, D. N. F. A. Akmal, and S. Ramadhani, “Identifikasi Topik Hangat di Media Berita Menggunakan Latent Dirichlet Allocation,” Journal of Information Engineering and Educational Technology, vol. 8, no. 1, pp. 14–17, Jun. 2024, doi: 10.26740/jieet.v8n1.p14-17.
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, Feb. 2024, doi: 10.57152/malcom.v4i2.1204.
A. Gaviota and F. Ardiani, “Pemodelan Topik Menggunakan BERTopic Pada Nama Produk Template Desain di Peterdraw Studio,” CESS (Journal of Computer Engineering, System and Science), vol. 11, no. 1, pp. 27–40, Jan. 2026, doi: 10.24114/cess.v11i1.70335.
W. Wahyuni, T. P. Lestari, M. Apriliana, and R. Gumelta, “Implementation of BERTopic for Topic Modeling Analysis of the Free Nutritious Meal Program Based on YouTube Comments,” Journal of Applied Informatics and Computing, vol. 9, no. 4, pp. 1964–1971, Aug. 2025, doi: 10.30871/jaic.v9i4.9754.
Kristine Angelina Simanjuntak, Muhamad Koyimatu, and Yolla Putri Ervanisari, “Analisis Perubahan Opini Publik Terhadap Kendaraan Listrik di Indonesia Melalui Komentar YouTube: Pendekatan Topic Modeling BERTopic,” Jurnal Inovasi Kewirausahaan, vol. 1, no. 3, pp. 1–9, Oct. 2024, doi: 10.37817/jurnalinovasikewirausahaan.v1i3.3789.
A. Ramadhan and E. Fernando, “Analisis Sentimen Berbasis Aspek pada Ulasan Pelanggan Menggunakan Pemodelan Latent Dirichlet Allocation dan BERTopic,” Musytari : Jurnal Manajemen, Akuntansi, dan Ekonomi, vol. 20, no. 5, Jul. 2025, doi: https://doi.org/10.2324/q2e09q23.
D. Aryani, I. Lucia Kharisma, A. Sujjada, and K. Kamdan, “Topic Modeling of the 2024 Election Using the BERTopic Method on Detik.com News Articles,” Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi, vol. 9, no. 2, pp. 171–180, Aug. 2024, doi: 10.25139/inform.v9i2.8429.
Muhammad Rayhan Nur, Yudi Wibisono, and Rani Megasari, “Analisis Sentimen dan Pemodelan Topik pada Post tentang Merek Teknologi di X Menggunakan Fine-tuning IndoBERT dan BERTopic,” Jurnal Komputer Teknologi Informasi Sistem Informasi (JUKTISI), vol. 4, no. 2, pp. 743–750, Jul. 2025, doi: 10.62712/juktisi.v4i2.508.
C. J. L. Tobing, IGN Lanang Wijayakusuma, and Luh Putu Ida Harini, “Perbandingan Kinerja IndoBERT dan MBERT Untuk Deteksi Berita Hoaks Politik dalam Bahasa Indonesia,” JST (Jurnal Sains dan Teknologi), vol. 14, no. 1, pp. 114–123, May 2025, doi: 10.23887/jstundiksha.v14i1.92126.
A. Pambudi, “Penerapan CRISP-DM Menggunakan MLR K-Fold pada Data Saham PT. Telkom Indonesia (Persero) Tbk (TLKM) (Studi Kasus: Bursa Efek Indonesia Tahun 2015–2022),” Jurnal Data Mining dan Sistem Informasi, vol. 4, no. 1, p. 1, Mar. 2023, doi: 10.33365/jdmsi.v4i1.2462.
F. Koto, A. Rahimi, J. H. Lau, and T. Baldwin, “IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP,” Proceedings of the 28th International Conference on Computational Linguistics, vol. 1, no. 1, pp. 757–770, Nov. 2020, doi: 10.18653/v1/2020.coling-main.66.
C. Ramadhan, V. Atina, and H. Permatasari, “Analisis Perbandingan Model CNN dan IndoBERT Dalam Sentimen Berita Politik Indonesia,” Prosiding Seminar Nasional Teknologi Informasi dan Bisnis, vol. 1, no. 1, pp. 110–118, Jul. 2025, doi: 10.47701/v1r9ka69.
L. McInnes, J. Healy, and J. Melville, “UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction,” J. Open Source Softw., vol. 3, no. 29, p. 861, Sep. 2020, [Online]. Available: http://arxiv.org/abs/1802.03426
G. G. Ghiffary, K. Alifviansyah, A. Fitrianto, E. Erfiani, and L. M. R. D. Jumansyah, “Perbandingan Algoritma HDBSCAN dan Agglomerative Hierarchical Clustering dalam Klasterisasi pada Data yang Mengandung Pencilan,” Jurnal Riset dan Aplikasi Matematika (JRAM), vol. 8, no. 2, pp. 122–135, Oct. 2024, doi: 10.26740/jram.v8n2.p122-135.
N. Perdana and H. Santoso, “Klasterisasi Judul Berita Online Isu Pemilu Prabowo Subianto dengan Kombinasi LLMS Embedding Dengan HDBSCAN,” Jurnal Locus Penelitian dan Pengabdian, vol. 4, no. 10, pp. 9284–9298, Oct. 2025, doi: 10.58344/locus.v4i10.4779.
F. D. Handayani and Isnaini Rosyida, “Clustering Review Pengguna Aplikasi Zenius pada Layanan Google Play Store Menggunakan Metode DBSCAN dan HDBSCAN,” Emerging Statistics and Data Science Journal, vol. 1, no. 2, pp. 178–191, May 2023, doi: 10.20885/esds.vol1.iss.2.art19.
M. Grootendorst, “BERTopic: Neural topic modeling with a class-based TF-IDF procedure,” Mar. 2022, doi: 10.48550/arXiv.2203.05794.
R. A. Kurniawan, P. Purwantoro, and I. Maulana, “Pemodelan Topik Ulasan Pengguna Honkai Star Rail Menggunakan Bertopic Berbasis Indobert,” Blend Sains Jurnal Teknik, vol. 4, no. 4, pp. 872–881, Apr. 2026, doi: 10.56211/blendsains.v4i4.1534.
N. C. Sastya and I. Nugraha, “Penerapan Metode CRISP-DM dalam Menganalisis Data untuk Menentukan Customer Behavior di MeatSolution,” UNISTEK, vol. 10, no. 2, pp. 103–115, Oct. 2023, doi: 10.33592/unistek.v10i2.3079.
Y. A. Hafiz and E. Sudarmilah, “Implementasi Web Scraping pada Portal Berita Online,” Inisiasi, vol. 12, no. 1, pp. 55–60, Nov. 2023, doi: 10.59344/inisiasi.v12i1.120.
M. R. Fikri, R. T. Handayanto, and D. Irwan, “Web Scraping Situs Berita Menggunakan Bahasa Pemograman Python,” Journal of Students‘ Research in Computer Science, vol. 3, no. 1, pp. 123–136, May 2022, doi: 10.31599/jsrcs.v3i1.1514.
A. Z. Rizquina and C. I. Ratnasari, “Implementasi Web Scraping untuk Pengambilan Data Pada Website E-Commerce,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 5, no. 4, pp. 377–383, Oct. 2023, doi: 10.47233/jteksis.v5i4.913.
Runimeirati, Abdul Muis, and Figur Muhammad, “Pelatihan Text Mining Menggunakan Bahasa Pemrograman Python,” Abdimas Langkanae, vol. 3, no. 1, pp. 36–46, Jan. 2023, doi: 10.53769/abdimas.3.1.2023.83.
S. Saikin, S. Fadli, J. Akbar, and H. Fahmi, “Topic Modeling Judul Penelitian Menggunakan Metode Latent Dirichlet Allocation (LDA) dan Faktorisasi Matriks Non-Negatif (NMF),” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 2, pp. 3439–3445, Apr. 2025, doi: 10.36040/jati.v9i2.12998.
C. Naury, D. H. Fudholi, and A. F. Hidayatullah, “Topic Modelling pada Sentimen Terhadap Headline Berita Online Berbahasa Indonesia Menggunakan LDA dan LSTM,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 5, no. 1, p. 24, Jan. 2021, doi: 10.30865/mib.v5i1.2556.
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