<![CDATA[The Indonesian agricultural sector faces challenges in early detection of land disturbances such as pests, diseases, floods, and droughts. Low technology adoption and digital literacy among farmers lead to slow responses to early symptoms. The research aims to develop a web-based decision support system that integrates the decision tree method, forward chaining inference, and sending automatic classification results via WhatsApp. The research used a Waterfall model, encompassing needs analysis, design, implementation, testing, and maintenance. The system is built based on 24 input symptoms that generate five classification categories. This application allows farmers to enter land condition data and receive classification results directly through the system and WhatsApp notifications. Testing demonstrated 80% accuracy compared to expert diagnoses, with a 95% message delivery success rate and an average response time of under five seconds. These results demonstrate that a rules-based approach combined with real-time communication can improve the speed and effectiveness of decision-making at the farmer level. This solution has the potential to be applied in other regions as part of accelerating the digital transformation of agriculture.]]>
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