Implementation Of Fuzzy Sugeno Method To Determine The Production Amount Of Android-Based Oyster Mushroom Baglogs - Budiarto, Sony Panca; Alfiyan, Faruk Alfiyan (Journal of Artificial Intelligence and Software Engineering, 2025)
The agricultural industry is one of the key sectors of the global economy. Along with the increasing demand for agricultural products, the challenge to improve production efficiency and productivity is also increasingly urgent. Oyster mushroom (Pleurotus ostreatus) is one of the agricultural commodities that has great potential to be developed, has high nutritional value, and growing demand in the global market. In facing the challenges of increasing oyster mushroom production, there are several problems that must be overcome. One of them is determining the right amount of oyster mushroom baglog needed to match the increasing demand and supply of oyster mushrooms. The decision-making process in determining the optimal amount of baglog production is often complex and requires in-depth analysis of various variables. In this study, researchers used the Fuzzy Inference System (FIS) Sugeno method to determine the optimal amount of oyster mushroom baglog production. The Sugeno method was chosen because it can produce clear rule-based solutions by better considering the correlation between input variables. The FIS will be developed based on previous baglog demand, inventory, and production data. The system is developed based on Android Mobile using React Native. The results of measuring the validation value and accuracy of Sugeno fuzzy applications in predicting the number of oyster mushroom baglog needs get a MAPE value of 1.88%; the prediction method used is in the very good category.