Prediksi Jumlah Kendaraan Bermotor Di Indonesia Menggunakan Metode Average-Based Fuzzy Time Series Models - Fajar Pangestu; Agus Wahyu Widodo; Bayu Rahayudi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Motor vehicles in Indonesia are growing in number each year. The high number of motor vehicles will affect various sectors. Impacts such as traffic congestion, pollution, accidents, and traffic violations. By predicting the number of motor vehicles, predicted data can be used by the government or related parties to create a program to reduce the impact of high number of motor vehicles. Fuzzy time series is one method for prediction. One type of fuzzy time series method is the average-based fuzzy time series. This method is an average-based fuzzy time series method that is able to determine the effective interval length, so as to provide predictive results with a good degree of accuracy. The data used in the study amounted to 45 data. The result of this research test, the average value of error calculated using Mean Absolute Percentage Error (MAPE) method is 12.67% error value indicating that this research is included in good category used in motor vehicle prediction in Indonesia because it has accuracy value below 20 %.