Analysis of the Environmental Impact of Food Production in Indonesia using Machine Learning Models Based on FAO Data

Food production in Indonesia is a critical pillar of national food security but has significant environmental impacts, including greenhouse gas emissions, land use, and water consumption. This study analyzes the environmental impact of major food production in Indonesia using a Machine Learning (ML) approach based on data from the Food and Agriculture Organization (FAO). Data from FAO.csv, Food_Production.csv, and total_population_reform.csv were processed to identify relationships between food commodities and their ecological footprints. ML techniques, such as clustering and the XGBoost model, were employed to group commodities based on environmental impact and predict total emissions with an RMSE of 3.07 MtCO₂e. Results indicate that commodities like rice, palm oil, and root crops have significant environmental impacts, with rice contributing the highest emissions at 374.684 MtCO₂e (1960–2013). This study provides data-driven strategies to support the sustainability of Indonesia’s food sector, aligned with green technology principles, through visualizations of supply and emissions for the top 10 commodities.