This research is to establish a Fuzzy Inference System forecasting model to help the decision
making process. In this research, the decision making is about decision making about stocks with
the used of the historical data. The data used in this research are secondary data that consist of
daily stock price fluctuation, daily closing price, daily trade volume, Price Earning Ratio and
Dividend per Share and the name of companies listed in the LQ45 in the period of January 1st
2000 to December 31st 2010. The total sample is one company. For testing the model is
performed by compare the real stock price fluctuation and forecasted stock price fluctuation.
Every model has its limitation, called as an error. In this research, error is a differences between
the real fluctuation compared with forecasting stock price fluctuation. In this research, to know
whether the model is applicable or not, errors indexes used. Many errors indexes such as Mean
Absolute Deviation, Mean Square Error, Mean Absolute Percentage Error, and Mean Percentage
Error.