Mushroom Production Prediction Model using Conjugate Gradient Algorithm - Yosua Chandra Simamora; Solikhun Solikhun; Lise Pujiastuti; Mochamad Wahyudi (International Journal of Mechanical Computational and Manufacturing Research, 2022)
Mushrooms are heterotrophic living things that act as saprophytes on dead plants. Mushrooms contain many important substances such as protein, amino acids, lysine, histidine, etc. Mushrooms tend to be better consumed than animal meat, even the content of lysine and histidine contained in mushrooms is greater than eggs. In recent years the volume of Mushroom Demand has increased, while production has decreased, especially on the island of Sumatra, namely in 2020 and 2021. Therefore, it is necessary to predict the estimated production of mushroom plants on the island of Sumatra so that the government on the island of Sumatra has clear data references to determine policies and make the right steps so that the production of mushroom plants on the island of Sumatra does not continue to decline. The method used in predicting is one of the ANN methods, namely the Conjugate Gradient Algorithm. The data used in this paper is Vegetable Crop Production data from 2014-2021 which was obtained from the website of the Central Statistics Agency. Based on this data, network architecture models such as 3-10-1, 3-15-1, 3-20-1, 3-25-1, 3-30-1, will be formed and defined. From the five models, training and testing values were obtained which showed that the most optimal architectural model was 3-10-1 with a Performance/MSE test value of 0.00055034. This value is the smallest of the 5 architectural models after the training and testing process. From this it can be concluded that this model can be applied to predict mushroom production on the island of Sumatra