<![CDATA[Abstract. Waste is a growing environmental problem, especially if it is not managed properly starting from the sorting process. One effort to improve the effectiveness of waste management is through automatic identification of waste types. This study aims to implement the Random Forest algorithm in the process of classifying waste into two categories: organic and non-organic waste. The data used are waste images that have gone through the preprocessing stage and the extraction of color and texture features. The Random Forest model was chosen because it has advantages in handling diverse data and providing stable classification results. Test results show that this model is capable of classifying with a fairly good level of accuracy, with the highest accuracy of 87% on the test data. In addition, this model is also integrated into a mobile application to facilitate users in identifying waste types in real-time. This implementation is expected to help the community sort waste more efficiently and contribute to sustainable environmental management.]]>
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