Application of the Naïve Bayes Algorithm and Simple Exponential Smoothing for Food Commodity Prices Forecasting

Inconstancy of the market prices can affect society's purchasing power. One effort to anticipate the price uncertainty is by conducting commodity price forecasting. In the concept of forecasting, the commodity prices can be predicted by studying sales data in the previous period. This study aims to implement a decision support system in predicting food commodity prices trend. In data collection, the authors used list of food commodities provided by Industry and Trade Service of Gowa Regency. For data analysis, we use Naive Bayes algorithm to predict the food commodity prices in the future and Simple Exponential Smoothing to find out the price trend in a certain period. As a result, both methods can predict commodity prices and market tendency in a given time completely.