Forecasting the Number of Domestic Flight Passengers at Minangkabau International Airport Using Sliding Window-Based Backpropagation - Zahra, Leofany; Mukhti, Tessy Octavia (Jurnal Matematika, Statistika dan Komputasi, 2025)
This study discusses the forecasting of domestic passenger numbers at Minangkabau International Airport using an Artificial Neural Network (ANN) with a Backpropagation algorithm based on the Sliding Window technique. The data used comes from BPS West Sumatra Province for the 2018-2023 period. The Sliding Window technique transforms time series data into cross-sectional data, which is then modeled using ANN with variations in the number of neurons and hidden layers. The results show that the best model uses 1 hidden layer, 5 hidden neurons, a learning rate of 0.01, and a window size of 5, with an MSE of 0.0027 and a MAPE of 0.0860%. This model has proven to be highly accurate and can be used as a decision-making tool for airport capacity management and operations.