<![CDATA[In the sophisticated realm of big data, analyzing energy efficiency in Indonesia has become crucial for identifying savings opportunities. This study utilizes large-scale raster data, including CO2 emissions from the OCO-2 GEOS satellite, nocturnal satellite images from VIIRS, and demographic and infrastructural data from WorldPOP and EsriWorld Cover. Through advanced regression techniques in machine learning—Support Vector Regression, Artificial Neural Network, and particularly Random Forest—the research analyzes and forecasts energy efficiency across various Indonesian provinces. The analysis highlights a notable increase in CO2 emissions from 2019 to 2023, with a significant reduction in night-time light emissions in 2020 due to the pandemic, which temporarily decreased human activities. Despite these fluctuations, the continuous increase in population density and built-up areas underscores the persistent influence of urbanization on emissions. The Random Forest model, which provided the most accurate predictions, indicates an expected rise in total CO2 emissions until 2030, driven by urbanization and economic growth, followed by a decline by 2045 due to targeted governmental policies. These insights contribute significantly to understanding the distribution of energy efficiency and support the development of sustainable energy policies in Indonesia. The study not only enriches scientific literature but also guides policy-making, offering a framework for tailored energy efficiency improvements. This research marks a pivotal advancement in utilizing big data and satellite technology to optimize energy use in a context that was previously underexplored.]]>
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