<![CDATA[The increasing demand for food, climate variability, resource constraints, and fragmented agricultural information systems have created substantial challenges for achieving sustainable food security in Indonesia. Although Internet of Things (IoT) technologies have been increasingly adopted in smart agriculture, many existing approaches remain focused on real-time monitoring and lack an integrated data-driven framework that translates heterogeneous agricultural data into actionable decisions for improving production efficiency and food-system resilience. This study aims to develop a data-driven IoT-based smart agriculture model that integrates real-time environmental and agricultural data, data analytics, predictive modeling, and decision support to strengthen sustainable food security in Indonesia. The proposed model combines IoT sensors for collecting key parameters, including soil moisture, temperature, humidity, light intensity, and water-related variables, with data processing and analytical methods to identify agricultural conditions and support predictive decision-making. The model is evaluated through system performance, prediction accuracy, resource-use efficiency, and selected indicators of agricultural productivity and food-security resilience. The research adopts a design and development approach followed by empirical validation using field-based agricultural data. The expected contribution is an integrated conceptual and technological framework that connects data acquisition, intelligent analytics, and agricultural decision-making rather than treating IoT merely as a monitoring technology. The study is expected to provide practical support for precision agriculture and evidence-based resource management while contributing to the development of scalable smart agriculture strategies for sustainable food security in Indonesia.]]>
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