Peramalan Jumlah Kunjungan Wisatawan Kota Batu Menggunakan Metode Time Invariant Fuzzy Time Series - Aria Bayu Elfajar; Budi Darma Setiawan; Candra Dewi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2017)
Forecasting systems with fuzzy time series capturing the pattern of past data and then use it to project future data. The process also does not require a complex learning system as it exists on genetic algorithms and neural networks, so that make the system is easy to develop. In the prediction using fuzzy time series, the length of the interval has been determined at the beginning of the calculation process. While determining the interval length is very influential in the formation of fuzzy relationships also will have an impact on the prediction of the outcome differences. Therefore, the formation of the fuzzy relationship must be precise and it requires the determination of an appropriate interval length. One method that can be used to determine the effective length of the interval is an average based method. In this paper, the authors implement the fuzzy time series to forecast the monthly visitor data, as for the data used for testing is derived from Dinas Pariwisata Kota Batu and from the results of tests conducted that data forecasting using Average based earned value error AFER best of 0.0056% by using 60 training data