<![CDATA[Indonesia is an agrarian country where the agricultural sector plays a vital role in the economy. Diseases in rice plants pose a serious threat to farmers as they can significantly reduce the quality and yield of the harvest. Random Forest, one of the machine learning methods, has been implemented in research to effectively classify types of diseases in rice plants. This study reviews various literatures related to the application of the Random Forest method and several other algorithms such as CNN, Decision Tree, and SVM in detecting and identifying rice plant diseases. The review shows that the Random Forest method has high accuracy performance, making it a recommended method for early detection of rice plant diseases. This study is expected to serve as a guide for further research to improve the accuracy and efficiency of rice disease classification methods.]]>
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