<![CDATA[Cultivation of rice plants, which is the staple food of Indonesian people, will experience a decline in yields in 2023, causing losses for farmers. Rice cultivation has obstacles such as disease which causes rice yields to decrease. This research aims to classify rice leaf disease using the KNN method by applying stratified sampling to RGB image extraction, so that farmers can easily maintain the stability of rice yields which decrease over time. The data used is rice leaf disease image data in .png format which consists of 3 classes, namely bacterial leaf blight, bacterial leaf streak, and brown leaf spot. The digital image processing used is an RGB image which is extracted and the average value of the red, green, and blue layers is taken. This research produces a superior KNN classificarion accuracy value on RGB images using stratified sampling of 73% compared to random sampling of 69%.]]>
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