Risk Analysis of Oyster Mushroom Cultivation Success through Artificial Neural Network with Backpropagation Algorithm - Fazri Aslam; Riri Syafitri Lubis (KUBIK, 2025)
Consumption mushroom cultivation is still rare in most parts of Indonesia, although the demand for this agricultural product continues to increase. Mushroom business opportunities are actually quite promising. This research aims to analyze the prediction of the risk level of oyster mushroom cultivation success using the artificial neural network method with the Backpropagation algorithm. This research combines qualitative and quantitative approaches, with data analysis methods in the form of Backpropagation algorithm training implemented through MATLAB software. Based on the results of testing or training conducted using the 5-3-1 Artificial Neural Network (JST) architecture and Epoch 1, the minimum error is 0.6 or 4 kg of yield (IDR 80,000), while the maximum error is 0.7 or 5 kg of yield (IDR 100,000). with a training MSE of 0.0964 with This means that artificial neural networks can create patterns to predict the yield of oyster mushroom cultivation.