<![CDATA[The development and preservation of Green Open Space (GOS) is an important part of maintaining environmental balance, especially in the Sumatra Ecoregion. This study aims to predict the number of GOS points using a linear regression approach and the Random Forest algorithm. The data used include variables such as area and forest area from several provinces in Sumatra. Model performance evaluation was carried out using MAE, RMSE, and coefficient of determination (R²) metrics. The analysis results show that the Random Forest model has superior performance compared to linear regression, with an MAE value of 5.52, RMSE of 5.88, and R² of 0.74. Meanwhile, linear regression was only able to achieve an R² of 0.45. These findings indicate that Random Forest is more effective in capturing non-linear data patterns and more accurate in predicting the number of GOS points. This study contributes to the use of data science technology to support sustainable environmental planning, as well as becoming a basis for data-based spatial planning policy making]]>
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