Enhancing Air Traffic Forecasting Accuracy at Hang Nadim Airport Using ARIMA-Neural Network - Masparudin Masparudin; Abdullah Abdullah; Raymond Erz Saragih; Yonky Pernando; Ilwan Syafrinal (Universitas Islam Indragiri, 2026)
Passenger traffic fluctuations at Hang Nadim International Airport exhibit extreme volatility influenced by the unique characteristics of the Free Trade Zone (FTZ). Single statistical methods often fail to capture non-linear patterns in this high-variability data. Therefore, this study proposes a Hybrid ARIMA-Neural Network model to enhance forecasting accuracy. The primary variable used is the total monthly passenger volume (arrivals and departures). The research stages began with data preprocessing (80:20 train-test ratio), linear component modeling using ARIMA, residual extraction, and non-linear component modeling using Multi-Layer Perceptron (MLP) to correct residual errors on a one-step-ahead basis. Evaluation results show that the standalone ARIMA model is slow to anticipate extreme surges, resulting in a Mean Absolute Percentage Error (MAPE) of 23.75%. The hybrid model integration proved successful in compensating for these weaknesses, reducing the MAPE value to 12.51%. This achievement represents a 47.33% error reduction from the baseline. In terms of novelty, this hybrid approach provides a highly reliable computational solution for airport management with dual characteristics (tourism and industry) in mitigating uncertainty in capacity planning.