<![CDATA[Accident data from Satlantas Polrestabes Semarang City is known that in 2016 there is an increase in the number of traffic accidents in the Semarang city. In the future the impact of accidents is predicted to be bigger so it is necessary to forecasting. Forecasting is one of the most important elements in decision making, because effective or not a decision generally depends on several factors that can not be seen at the time the decision was taken. In this time study the possible time series model is ARMA (2,2), ARMA (2,1), ARMA (1,2), ARMA (1,1), AR (2), AR (1), MA (2), MA (1). However, after testing, the model used is ARMA (1,1). This model is used because it meets all the assumption requirements that are parameter significant , residual indepedent test, residual normality test and the smallest Mean Square Error value. According to data forecasting results showed the highest number of crashes existed in January of 97 accidents and the lowest in December amounted to 93 accidents, So that the necessary to action from the relevant agencies to cope with the increasing number of traffic accidents in the city of Semarang. Keywords : Time Series Method, ARMA (1,1), Traffic Accident.]]>
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