Prediction of the Number of Motorized Vehicles in Ternate City Using the Average Based Fuzzy Time Series Model Method - Friyanti Friyanti; Iis Hamsir Ayub Wahab; Arbain Tata (International Journal Of Electrical Engineering and Inteligent Computing, 2024)
Prediction or forecasting is one of the most important elements in decision-making. The impact caused is the number of motorized vehicles, residents, roads, and area area. By predicting the number of motor vehicles, the prediction data can be used from a program to reduce the impact of a high number of motor vehicles. This study aims to determine the Prediction of the Number of Motorized Vehicles in Ternate City using the Average Based Fuzzy Time Series Model Method in Ternate City from 2019 to 2024. Settlement using Average Based Method data and fuzzy time series interval numbers have been determined at the beginning of the calculation process, this process is very influential in the formation of fuzzyrelationship on each number to compare each other which will certainly have an impact on the difference in the results of the reduction calculation. The test results are known that the Fuzzy time series is one of the methods for prediction. One type of method is the average-based fuzzy time series with the average total value calculated using the Mean Absolute Percentage Error (MAPE) method obtained from the number of each indicator of 2.98% which shows that this study is included in the category of good used in the prediction of motor vehicles in Ternate City because it has an accuracy value of less than 20%. From the predictions carried out, the MAPE value of the test was 1.01%, the MSE value of forecasting was 1400.5, and the MAD value of forecasting was 27.93.