Data mining-based analysis of factors affecting paddy farming

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Average accuracy of the decision tree model reached 71.43%, highlighting effective factor identification.
Factors such as gender and household size moderately influenced paddy production, reflecting socio-economic dynamics.
The CRISP-DM methodology guided the analysis, ensuring alignment with agricultural practices and goals.
Findings suggest integrating traditional farming with modern tools may enhance sustainable agriculture efforts.

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Analysis reveals key factors like gender and household size impact paddy farming, suggesting data mining supports food security.

Utama et al. (2025) studied this question.

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