| dc.description.abstract | The price of chili is one of the food commodities
that can affect the inflation rate. Its uncertain price and even
increasing at certain times will negatively impact society and
state. The price offer for chili is still highly dependent on the
amount of chili produced. At the same time, the amount of chili
productivity depends on the harvested area and land
productivity. When the supply in the market is lacking, the price
will increase far from its average price. Otherwise, when the
supply is excessive, the price will fall far below the regular price.
Therefore, it requires a method to estimate this chili's price to
support decision making related to price issues. Many
forecasting methods have been used to predict data, such as
Backpropagation Neural Networks (BPNN) and Single Moving
Average (SMA), proven in some cases to provide good
forecasting results. These two methods will be compared with
the lowest error rate and the best method in predicting chili's
price. The results of this research will help various parties as a
consideration in making decisions and planning. | en_US |
