N. Azizah, K. A'Yun, T.W. Septiarini, D.U. Wutsqa, A.M. Abadi
The study aims to investigate the most optimum rules of fuzzy inference systems to forecast the white sugar price in the international market. The fuzzy rules are optimized by using a table look-up method. As a comparison, we also employ fuzzy time series methods that developed by Chen, Singh, and Heuristic. The main differences among the four methods are on the inputs determination for prediction and on the algorithm to calculate the prediction. The performance of the method is evaluated using MAPE (Mean Absolute Percentage Error). The MAPE values of white sugar price forecasting yielded by a fuzzy inference system with table lookup are 2.83 % on training data and 7.66 % on checking data. Furthermore, the MAPE values resulted by fuzzy time series model of Chen, Singh, and heuristic are 8.52%, 8.62% and 8.01% on training data, and 6.44%, 6.47%, and 6.44% on checking data, respectively. The table lookup delivers the highest performance on the training data, while it delivers the lowest performance on checking data. However, the fluctuation of its forecasts is more reasonable, since it follows the fluctuation of the actual data, while the other three methods deliver constant forecasts which are not reasonable. © 2018 Published under licence by IOP Publishing Ltd.
Graduate Programs of Mathematics Education, Yogyakarta State University, Kolombo Street 01, Sleman, Yogyakarta, Indonesia; Graduate Programs of Mathematics, Gadjah Mada University, Sleman, Yogyakarta, Indonesia; Departments of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songla University, Pattani, Thailand; Mathematics Department, Faculty of Mathematics and Science, Yogyakarta State University, Kolombo Street 01, Sleman, Yogyakarta, Indonesia
