<![CDATA[Consumption of shallots in Indonesia is still relatively large. This affects price movements, production to market needs. Associated with changes in the amount of production with public consumption needs affect the price variations in each period. Shallot price conditions in the market that experience changes can affect losses or profits for shallot entrepreneurs. This creates problems in the sale of bawal because the price of onions is difficult to predict. To minimize losses, a system or technology is needed that can help predict shallot prices. As an illustration of the shallot entrepreneurs. Shallot price prediction system can be done using the calculation method "Algorithm Fuzzy Inference System (FIS) Sugeno method". The use of this algorithm does not require independent assumptions, homoscedasticity, and normally distributed residuals which are often not found in the data so that this method is considered suitable for predicting data that has extreme values. The price of shallots is influenced by two variables, namely the amount needed by the amount of market demand. The test results show a Mean Square Error (MSE) value of 137.671697. then the Root Mean Square Error (RMSE) value is the result of the square of the Mean Square Error (MSE) value, namely: 1.0541 The Mean Absolute Percentage Error (MAPE) value which has an error rate of 40%.]]>
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