Abstract
Previous research has not discussed about the prediction method of carbon stock changes using open-source software. This research aims to fill the gap by using QGIS as open-source software in. The method used is Support Vector Machine and Cellular Automata, which is only found in QGIS software, including QGIS 3.8.0 and QGIS 2.18.24 with Pip-Python 3. The results show that SVM and Cellular Automata algorithms in QGIS software successfully predicted land cover in the context of carbon stock change. This study shows the prediction of carbon stock changes due to land cover conversion in Salatiga City for the period 2019-2029 decreased by 9,202.77 tons C, where carbon emission was 10,313.47 tons C and carbon sequestration was 1,110.70 tons C. The prediction of carbon stock changes in Salatiga City is needed to reference local governments to formulate mitigation and adaptation efforts to global climate change.
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