Agricultural Commodity Sales Recommendation System For Farmers Based on Geographic Information Systems and Price Forecasting Using Probabilistic Neural Network Algorithm

Abstract Since the implementation of social distancing and physical distancing due to the outbreak/pandemic of the Coronavirus (Covid-19), direct sales in the market have experienced a shortage of buyers. Farmers also share this in Indonesia, where the price game offered by collectors does not match the market price. The second problem is the mismatch of prices in each market, forcing farmers to check locations to sell their agricultural products. This problem is also experienced by the O’reng Rembangan Community Information Group (KIM), one of the community groups engaged in production to cultivate vegetable and fruit gardens in Kemuning Lor Village, Arjasa District, Jember Regency. The purpose of this research is the creation of an information system that can help farmers, especially KIM O’reng Rembangan, to obtain current market price information, receive market recommendations for agricultural products, get the nearest market from the location of farmers, and can be used by sellers to make purchases, optimize stock merchandise. This research also focuses on the prediction of agricultural commodity prices. The method used is the Probabilistic Neural Network (PNN) method to estimate the price of agricultural commodities. The resulting system in this study consists of 2 parts. The first part is the input device, which officers can use to enter the price of each agricultural commodity directly from each market. The second part is a Geographic information system used to display the forecasting results of agricultural commodity prices in each market. The forecast of agricultural commodity prices in this study has an accuracy of 98.3%.