<![CDATA[The rapid population growth and urban activity have caused a continuous increase in household waste generation. Bekasi City is one of the major contributors, with a significant amount of organic household waste requiring a sustainable management strategy. This research proposes a Machine Learning approach based on the Random Forest algorithm to predict household waste generation for compost production planning. The dataset includes demographic variables and annual waste records from 2022 to 2024. The method consists of preprocessing, data splitting, and model evaluation stages. Results show that the model achieved an MAE of 1111.70, RMSE of 1549.57, and an R² value of 0.95, indicating strong predictive capability. The model was then used to calculate household waste prediction for 2025 to 2030, showing an increasing trend. Additionally, the projection enabled the estimation of compost production potential based on an assumption that 70% of total waste is organic and 50% of it can be processed into compost. This research confirms that Machine Learning and Artificial Intelligence approaches can support local waste management policy and long-term sustainability planning.]]>
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