Prediction of the Number of Airport Passengers Using Fuzzy C-Means and Adaptive Neuro Fuzzy Inference System
Sunarsan Sitohang, Abba Suganda Girsang, Suharjito Suharjito · International Review of Automatic Control (IREACO) · 2017
Airport requires a system to predict the number of passengers as a reference for airport development planning. In this study, the data used are time series of the number of passengers for eleven years. These data will form patterns which indicate the number of passengers each month in a year as the input data and the number of passengers next year as a target prediction. After the input data are clustered into three types using fuzzy C-means (FCM), the data are processed using adaptive neuro fuzzy inference system (ANFIS) to get the prediction data. The result shows that the “Mean Absolute Percentage Errors (MAPE ) which represent the errors for 4 years are 4.20%, 5.70%, 5.36% and 4.47% with an average of 4.93% . Based on this result, FCM and ANFIS can be combined to predict the data time series.