Keywords:
big data
data mining
decision tree algorithm
food security

  • Abstract

    This research investigates the key factors influencing the paddy rice harvest class in West Java, Indonesia, with a focus on understanding the interplay between traditional farming knowledge and modern technological solutions to support food security. It is recognize that rice is a staple for a large portion of the global population and critical to food security. This research explores how decision tree algorithms can help identify the most influential factors and support sustainable agricultural practices, especially in the context of small-scale rice farming in West Java, Indonesia. A dataset encompassing demographic variables (age, gender, household involvement), environmental conditions, and farming experience was analyzed using a decision tree model. The model’s performance was validated using cross-validation, achieving an average accuracy of 71.43%. In this study reveal that gender and household size of farmers showed moderate influence, reflecting the socio-economic dynamics of family-based farming. Meanwhile, other factors such as land ownership and paddy field type had a limited impact to the paddy productions. These findings highlight the need for structured knowledge transfer programs from experienced from experience farmers to younger farmers and suggest that combining traditional farming practices with technology farming tools might can improve rice yield outcomes. Furthermore, the research applies the CRISP-DM methodology to guide data analysis and ensure alignment with agricultural goals. The integration of data mining in agriculture not only improves understanding of paddy productivity but also supports adaptive strategies in response to climate variability and demographic shifts. This research provides practical implications for farmers, researchers, and policymakers seeking to implement targeted interventions that contribute to sustainable rice production and long-term food resilience in developing regions.

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Utama, I., Harsanto, B., Karmagatri, M., Iskandar, Y., Kasumaningrum, Y., & Rahmatillah, I. (2025). Data mining-based analysis of factors affecting paddy farming. Multidisciplinary Science Journal, 8(1), 2026048. https://doi.org/10.31893/multiscience.2026048
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