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All Journal Jurnal Sains & Teknologi Modifikasi Cuaca INTECOMS: Journal of Information Technology and Computer Science Jurnal Teknologi Sistem Informasi dan Aplikasi Social, Humanities, and Educational Studies (SHEs): Conference Series Building of Informatics, Technology and Science EAJ (ECONOMICS AND ACCOUNTING JOURNAL) Journal Sensi: Strategic of Education in Information System Jurnal Sistem Komputer & Kecerdasan Buatan Infotech: Journal of Technology Information Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) International Journal of Engineering, Science and Information Technology Jurnal Pengabdian kepada Masyarakat JOURNAL OF HUMANITIES, SOCIAL SCIENCES AND BUSINESS (JHSSB) International Journal of Multidisciplinary Research and Literature (IJOMRAL) Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Journal of Informatics and Electronics Engineering International Journal of Disaster Management Ranah Research : Journal of Multidisciplinary Research and Development Jurnal Ilmu Komputer
Tukiyat Tukiyat, Tukiyat
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Journal : Jurnal Teknik Informatika (JUTIF)

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Enhancing BERTopic with Neural Network Clustering for Thematic Analysis of U.S. Presidential Speeches Anggai, Sajarwo; Zain, Rafi Mahmud; Tukiyat, Tukiyat; Waskita, Arya Adhyaksa
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5090

Abstract

Understanding the underlying themes in presidential speeches is critical for analyzing political discourse and determining public policy direction.  However, topic modeling in this context presents difficulties, particularly when clustering semantically rich topics from high-dimensional embeddings.  This study seeks to improve topic modeling performance by incorporating a Neural Network Clustering (NNC) approach into the BERTopic pipeline.  We analyze 2,747 speeches delivered by U.S President Joe Biden (2021-2025) and compare three clustering techniques: HDBSCAN, KMeans, and the proposed Autoencoder-based NNC.  The evaluation metrics (UMass, NPMI, Topic Diversity) show that NNC produces the most coherent and diverse topic clusters (UMass = -0.4548, NPMI = 0.0234, Diversity = 0.3950, ).  These findings show that NNC can overcome the limitations of density and centroid-based clustering in high-dimensional semantic spaces. The study contributes to the field of Natural Language Processing by demonstrating how neural-based clustering can improve topic modeling, particularly for complex, real-world political corpora.
Co-Authors A.A. Ketut Agung Cahyawan W Abu Khalid Rivai Achmad Hindasyah, Achmad Agung Budi Susanto Agung Sundoro, Agung Alfan Muttaqin, Alfan Andrean, Kelvin Anggai, Sajarwo Ani Nuraini, Ani Arya Adhyaksa Waskita Brando, Charlo Budi Harsoyo Djaoharman, Sulaiman F Heru Widodo, F Heru F. Heru Widodo, F. Heru Fadhillah, Chandra Gumay, Rizki Izandi Halimatussadiah, Siti iman, Fajar nur Indrajaya, Titus Ismaya, Sakti Brata Kurnia, Muhammad Dahlan Makhsun, Makhsun Maya Sova Murni Handayani Nugraha Wahyu, Fajar Purwadi Purwadi R Djoko Goenawan, R Djoko Rafi Mahmud Zain Raharja, Yudi Rahayuningsih, Hesti Rozali, Cristien Sakya, Andi Eka Satyo Nuryanto, Satyo Sembodo, Eko Sugiyanto - Sunu Tikno, Sunu Supriatna, Dahlan Supriyani, Mei Sutrisno, Sutrisno Taswanda Taryo Tri Handoko Seto, Tri Handoko Uliyatunisa, Uliyatunisa Untung Haryanto, Untung Veithzal Rivai Zainal Waskita, Arya Adhyaksa Widyasari, Lala Wily, Wily Arisandi Zain, Rafi Mahmud