PERAMALAN MENGGUNAKAN METODE WEIGHTED FUZZY INTEGRATED TIME SERIES (Studi Kasus: Harga Beras di Indonesia Bulan Januari 2011 s/d Desember 2017) - Setya Adi Rahmawan; Diah Safitri; Tatik Widiharih (Jurnal Gaussian, 2019)
Fuzzy Time Series (FTS) is a time series data forecasting technique that uses fuzzy theory concepts. Forecasting systems using FTS are useful for capturing patterns of past data and then to using it to produce information in the future. Initially in the FTS each pattern of relations formed was considered to have the same weight besides using only the first order. In its development the Weighted Fuzzy Integrated Time Series (WFITS) which gave a difference in the weight of each relation and high order usage has been appeared. Measuring the accuracy of forecasting results is used the value of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). In this study both the first-order and high-order WFITS methods were applied to forecast rice prices in Indonesia based on data from January 2011 to December 2017. In this regard, the results of the analysis obtained data forecasting using Lee's high-order model WFITS algorithm (1,2,3) giving the value of RMSE and MAPE on the data testing in a row as many as 69,898 and 0.47% while for the RMSE and MAPE on the training data is as many as 70.4039 and 0.54%. Keywords: Fuzzy Time Series, Weighted Fuzzy Integrated Time Series, RMSE, MAPE, High-Order, Rice Prices