<![CDATA[Sweet potatoes are the world's third most important root crop and the fourth most popular staple food in developing countries, including Indonesia. Some diseases commonly found in sweet potato leaves are early blight (identified by leaves containing batataezim) and late blight (characterized by leaves that have chlorosis). These two diseases have different symptoms and require different treatments, but a slow identification process can lead to additional costs for plant care. This research will classify image data of sweet potato diseases using the Convolutional Neural Network (CNN) method. CNN is a derivative of the Multilayer Perceptron (MLP) designed to process image data with high network depth and is often used for classification tasks. The research uses a total of 750 images divided into 3 classes: images of healthy leaves, images of leaves with chlorosis, and images of leaves containing batataezim. Each leaf class will be labeled with 250 image data, and the labeled data will be further divided into training and testing sets. From these sets, prediction data will be obtained from the testing process during the CNN model training. The training accuracy resulted in a value of 98.17%, while the testing accuracy reached 98.67%. Additionally, the resulting loss values are remarkably low, at 0.04% for training and 0.03% for testing. The research findings will provide insights into the CNN method's ability to detect diseases in sweet potato plants, potentially impacting agricultural supervision, plant disease identification, and enabling more precise decisions regarding plant care actions.]]>
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