Forecasting Passenger Volume at Sultan Hasanuddin Airport in Makassar Using the Support Vector Regression Method - Maya Sari Wahyuni; Irwan Irwan; Asria Asria (Journal of Mathematics, Computations and Statistics, 2026)
The number of passengers at Sultan Hasanuddin Airport in Makassar fluctuates from year to year, requiring an accurate forecasting method to support planning and decision-making. This study discusses the application of the Support Vector Regression(SVR) method in forecasting the number of passengers at Sultan Hasanuddin Airport, Makassar City. SVR is a forecasting model used to predict nonlinear time series data. The data used is secondary data on the number of monthly domestic flight departures from January 2006 to December 2024 obtained from the Central Statistics Agency. The research stages included data normalization using the min-max method, the formation of supervisory data using a sliding window with a window size of 12, the division of data into training (80%) and Testing (20%), and modeling using SVR with a Radial Basis Function (RBF) kernel. The selection of optimal SVR parameters was carried out through Grid Search Optimization, with the best parameter results being epsilon (ε)=0, Cost(C)= , Gamma (γ)= . Evaluation using Mean Absolute Percentage Error (MAPE) resulted in a value of 16.43%, which is classified as good accuracy. Forecasting for the period January–December 2025 produced a pattern of passenger numbers fluctuating between 232.534 and 280.842.