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Topic Modeling Analysis of Indonesia Food-Security News: Methods,Interpretations, and Trend Insights Afiyati Afiyati; Imbuh Rochmad; Setiyo Budiyanto; Bambang Jokonowo; Hadi Santoso; Kelik Budiana
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5784

Abstract

The critical problem for food-security stakeholders in Indonesia is the lack of scalable, quantitative methods to systematically distill dominant themes and evolving trends from vast volumes of news media, which severely hinders timely policy monitoring and responsive intervention. This study aimed to develop and validate a reproducible topic modeling pipeline specifically designed to uncover the latent thematic structure and quantify the temporal dynamics within Indonesian food-security news discourse. The research method is a comprehensive natural language processing pipeline applied to a curated corpus of 770 news documents spanning 2012 to 2025. The process involved languageadaptive preprocessing of Indonesian text, n-gram (1-2) vectorization to capture nuanced phrases, and training multiple Latent Dirichlet Allocation (LDA) models. The optimal model, with K=10 topics,was rigorously selected through a perplexity-based grid search across a range of potential topic numbers. The resulting topics were then qualitatively interpreted and manually labeled into policy-relevant themes by domain experts. Subsequently, we computed monthly topic intensity series to conduct a longitudinal analysis. The results of this research are that the pipeline successfully generated semantically coherent topics that aligned perfectly with core policy pillars, including availability, access, and utilization. Furthermore, the analysis revealed significant temporal shifts, sustained intensification of price and inflation-related discussions throughout the 2022-2024 period. This study conclusively demonstrates that unsupervised topic modeling can effectively transform unstructured news streams into actionable, quantifiable intelligence, thereby significantly enhancing situational awareness and supporting evidence-based decision-making for food security stakeholders.
Co-Authors Abdul Hamid Abdurohman Abdurohman Adi Kurnia Afiyati, Afiyati Agus Dendi Rochendi Agus Dendi Rochendi Agus Dendi Rochendi Agus Dendi Rochendi Ahmad Firdausi Alvin Sepbrian Andi Adriansyah Apipi Saputra Aprilia Dian Oftari Arga Gilang Rolanda Arif Rahman Hakim Arissetyanto Nugroho Badaruddin Badaruddin Bambang Jokonowo Beny Nugraha Dadang Gunawan David Martin Antoyo Dian Widi Astuti Dimas Jatikusumo Erman Al Hakim Erry Yulian Triblas Adesta Fahraini Bacharuddin Fajar Banardi, Deswandi Fajar R Fauzi Nur Iman Febryyanti Nawang Wulan Fina Supegina Freddy A Silaban Freddy Artadima Silaban Galang Persada Nurani Hakim Galih Bangun Santosa Gao Hongmin Gunawan Osman Hadi Santoso Hadi Santoso Hadi Wuryanto Hanifah Diana Harry Candra Sihombing Imbuh Rochmad Imbuh Rochmad Imbuh Rochmad Imelda Uli Vistalina Simanjuntak Irmulansati Tomohardjo Julpri Andika Kelik Budiana Kristiani N Nahampun Lukman M Silalahi Lukman Medriavin Silalahi Lukman Medriavin Silalahi Lusianna EP Siagian M. Hafiz Ibnu Hajar Mochamad Furqon Ismail Mudrik Alaydrus Muhammad Asvial Muhammad Budi Haryono Muhammad Hafizd Ibnu Hajar Muhammad Ikhsan Muhammad Jamil Muhammad JAMIL Putri Wulandari Qotrunada, Farah Qotrunnada, Farah Mufida Rachmat Muwardi Raden Sutiadi RAHAYU, FAJAR Rahmad, Khozaeni Bin Resi Sujiwo Bijokangko Rini Kusumawardani, Rini Rio Mubarak Rochendi, Agus Dendi Said Attamimi Saragih, Carolan Ignatius Selamet Kurniawan Septi Andryana Silviana Windasari Triyanto Pangaribowo Ucuk Darusalam Wadjdi, Achmad Farid Wahyu Kusuma Raharja Wajdi , Achmad Farid Wijaksana, Wibi Wijaksana Yoga Putra Setiawan Yudhi Gunardi Yudistiro Yudistiro Yudistiro Yudistiro, Yudistiro Yuliarman Saragih