<![CDATA[The mining activities and limestone mining industry have both positive and negative impacts on the community in the Cipatat District, West Bandung Regency. Technological advancements in satellite image analysis have increasingly incorporated Cloud computing and big data applications, such as Google Earth Engine (GEE), in land classification. Quick decision-making in mining activities requires fast and accurate data presentation. Through machine learning technology, this problem becomes one of the appropriate solutions. The objective of this research is to identify mining areas using machine learning in the Cipatat District, West Bandung Regency. The method employed is supervised classification analysis using the Random Forest and CHART algorithms with Google Earth Engine (GEE). The research results indicate that the accuracy level with the Random Forest algorithm is better with an accuracy rate of... compared to the CHART algorithm, which is...%. This is demonstrated by the alignment of field observation data with the classification that has been created. The utilization of the GEE platform in monitoring mining land developments can be applied and used as one of the considerations in policymaking for mining activities by the government of West Bandung Regency, West Java.]]>
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