Demand forecasting is needed in a company to find out the estimated level of demand in the future. Forecasting cannot be considered as an absolute one because there is no accurate forecasting. The forecasting accuracy level continues to be sought through forecasting development. One of those is applied through artificial intelligence. This study discusses the application of forecasting product selling using average-based fuzzy time series in Ainaya Boutique as the case of the study. The

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... ing system with average-based fuzzy time series captures patterns from past data. In this study, the data used for forecasting is monthly selling data based on the last three years. This research uses five types of products that are sold the most in the boutique. The trial result of forecasting selling products during January 2017 -December 2019 that uses the average-based fuzzy time series model show the result of the next month following data: the average error for the robe is 4.81%, 11.21% for the tunic, 8.78% for pashmina, 18.35% for khimar, and 9.72% for jeans. These results show that forecasting selling products in an Ainaya Boutique using the average-based fuzzy time series model has a high accuracy level.