ANALISIS KESESUAIAN AREA POTENSI MANIFESTASI ENERGI PANAS BUMI ASIA TENGGARA MENGGUNAKAN INTEGRASI ANALISIS MULTI-CRITERIA DAN MACHINE LEARNING

• Main Author: Guswanda, Riski
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/65208
• Institution: Institut Teknologi Bandung

The International Energy Agency (IEA) has estimated that global energy demand will increase by 1.6% with developing countries accounting for about 65% of the increase by 2030. The demand is estimated at more than 80% of users between 2015 and 2035. Meanwhile, subsidies for fossil fuels in Southeast Asia reached US$ 51 billion in 2012 which hurt the energy market, especially in Malaysia and Indonesia. This requires a shift in the use of fossil-fueled energy into clean and renewable energy such as geothermal energy. This is because currently, the total global energy consumption is equivalent to about 100 million barrels of oil per day, so theoretically geothermal energy can supply energy needs for 6 million years. The purpose of this study is to model the level of the potential for geothermal energy manifestasi areas in Southeast Asia. The methodology used is literature study and data collection in the form of remote sensing satellite imagery in product form on Google Earth Engine. Machine learning analysis uses random forest methods, classification and regression trees, as well as support vector machines that are used as base maps for multi-criteria analysis models. Meanwhile, the multi-criteria analysis used to model geothermal energy is the characteristics of geothermal manifestations on the surface. The expected result of this research is a thematic map of the level according to geothermal energy in Southeast Asia which contains potential locations for geothermal energy development in the Southeast Asian region.