<![CDATA[Agricultural land transition in rapidly urbanizing coastal regions poses significant challenges for sustainable land use planning and long-term food security. This study examines the driving forces behind agricultural land conversion in Cilegon City, Banten Province, Indonesia as one of Southeast Asia's most industrialized coastal cities by integrating Remote Sensing (RS), Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) with structured local knowledge elicitation. Land cover classification was performed using Random Forest machine learning applied to multi-temporal Landsat imagery (2011 and 2023), revealing substantial encroachment of non-agricultural land uses. Through pairwise comparison interviews with six domain experts, AHP weighting assigned the highest influence to rainfall (18%), soil quality (15%), and road accessibility (14%) as transition drivers. The resulting transitional suitability map, validated against observed land cover change, achieved an overall accuracy of 88.70% and a Kappa coefficient of 0.86, demonstrating the model's strong predictive capacity. The findings underscore that environmental, infrastructural, and socio-economic factors collectively govern land conversion dynamics. This study contributes a replicable, participatory spatial framework that bridges objective geospatial data with community-embedded knowledge, supporting more inclusive, evidence-based urban planning and agricultural land management in fast-growing coastal cities. Keywords: analytical hierarchy process, coastal city, land use change, local knowledge, remote sensing.]]>
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