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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
Budiana, Kelik
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Computer Science & IT Control & Systems Engineering

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Visualisasi Prediksi Prevalensi Balita Menggunakan Algoritma Random Forest Pada Lembaga Bidang Pangan Mubarak, Roy; Maesaroh, Siti; Yusuf, Mohamad; Budiana, Kelik; Fahreza, Muhammad Rizky
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 10 No 2 (2024): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v10i2.3176

Abstract

This study aims to apply the Random Forest algorithm to predict the prevalence of malnutrition in toddlers in DKI Jakarta. This algorithm was chosen because it has been proven to provide high prediction accuracy, with an average above 80% in previous research. The research method includes stages of problem formulation, research objectives, research design, sample selection, data collection, data processing, interpretation of results, report preparation, and result presentation. The data used is sourced from the National Food Agency (BPN) totaling 3000 datasets, which are then processed through stages of selection, preprocessing, transformation, data mining using the Random Forest algorithm, and evaluation using RMSE and MAE formulas. It is expected that the results of this study will provide valuable insights into efforts to address malnutrition issues in toddlers in the DKI Jakarta area. Keywords: Prediction ,Random Forest, Prevalence of Toddlers,Nutrition
Topic Modeling Analysis of Indonesia Food-Security News: Methods,Interpretations, and Trend Insights Afiyati, Afiyati; Rochmad, Imbuh; Budiyanto, Setiyo; Jokonowo, Bambang; Santoso, Hadi; Budiana, Kelik
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 Afiyati, Afiyati Bambang Jokonowo Fahreza, Muhammad Rizky Hadi Santoso Imbuh Rochmad Mohamad Yusuf Setiyo Budiyanto Siti Maesaroh