<![CDATA[Oyster mushroom cultivation is an agricultural sector with high economic potential, but its productivity is often disrupted by pests and diseases. Inappropriate management due to farmers' limited knowledge can cause significant losses. This study aims to develop an expert system for oyster mushroom pest and disease diagnosis based on Artificial Neural Networks (ANN), to assist in early identification of emerging disorders. The dataset consists of 150 samples covering a combination of symptoms and disease labels, collected from two different cultivation locations. There are several stages in this study, namely the preprocessing process that includes label encoding, feature normalization using Z-score, and data division in a ratio of 80% for training and 20% for testing. The ANN model was designed using a Multi-Layer Perceptron (MLP) with two hidden layers containing 10 neurons each, a ReLU activation function, an Adam solver, and a maximum iteration of 1000. The test results showed the model has an accuracy rate of 97%, with perfect precision and recall values for most disease classes. This study shows that the ANN approach is able to effectively recognize oyster mushroom disease symptom patterns. This system can be an efficient and adaptive diagnostic tool, and has the potential to be further developed as a smart agricultural technology solution]]>
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