<![CDATA[Indonesia, located at the intersection of the Indo-Australian, Pacific, and Eurasian tectonic plates, is one of the countries most vulnerable to geological disasters, particularly earthquakes. Sumatra Island, as a densely populated region with high agricultural productivity, is highly affected by seismic activity. This study aims to analyze earthquake risk in Sumatra Island using an integrative approach that combines statistical and machine learning methods, namely the Neyman-Scott Cox Process spatial model and the Random forest algorithm. The Neyman-Scott Cox Process was used to identify spatial clustering patterns of earthquake events based on their relationship with geological features such as subduction zones, active faults, and volcanoes. In addition, Random forest was applied to develop a risk classification model based on geospatial variables and to map earthquake-prone areas into high- and medium-risk categories. The results showed that the Thomas Cluster model provided the best spatial representation with the lowest AIC value, while distance to the subduction zone had the greatest contribution to earthquake risk classification. Most western areas of Sumatra were identified as high-risk zones. The resulting zonation map can serve as a basis for decision-making in disaster mitigation policies, regional planning, and agricultural infrastructure protection. This study is expected to strengthen early warning systems, maintain food distribution and production, and contribute to food security and sustainable development in disaster-prone areas. Furthermore, this method can be replicated in other regions in Indonesia with similar geological characteristics.]]>
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