The conversion of agricultural land to non-agricultural uses poses a significant threat to food security, particularly in metropolitan buffer regions such as Bogor Regency. Rapid population growth and economic restructuring have led to a 24.5% reduction in rice field area, declining from 48,177 ha in 2003 to 36,355 ha in 2019, contributing to a regional rice deficit of 37%. This study employs a quantitative spatial approach using Geographic Information Systems (GIS), integrating Spatial Overlay, Cellular Automata (CA), and a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) to analyze land conversion dynamics and evaluate the effectiveness of spatial planning policies. The results reveal persistent conversion of rice fields, including within designated food crop zones under the Regional Spatial Plan (RTRW), indicating a misalignment between spatial planning and actual land-use dynamics. This study contributes to applied environmental science by demonstrating the value of integrated spatial modeling in assessing land-use policy effectiveness and supporting spatially informed strategies for sustainable agricultural land management and local food security.
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