<![CDATA[The problem of forecasting domestic passenger arrivals has become increasingly important due to frequent fluctuations and seasonal patterns, as observed at APT Pranoto Airport in Samarinda. Such data requires an approach capable of capturing both long-term trends and rapid changes. This study employs the Maximal Overlap Discrete Wavelet Transform (MODWT), a modified version of the Discrete Wavelet Transform (DWT), which can be applied to data of any size. MODWT decomposes the data into wavelet coefficients and scaling coefficients, which are then used to construct a Multiresolution Autoregressive (MAR) model at each level of Daubechies wavelets. This method is used as a preprocessing step to improve forecasting accuracy. The best model is selected based on the smallest Mean Absolute Percentage Error (MAPE). The analysis results show that the best forecasting model is the one using Daubechies 6 wavelets, with an in-sample MAPE of 13.758% and an out-of-sample MAPE of 9.525%. The forecast of domestic passenger arrivals at APT Pranoto Airport for the period from October 2024 to December 2024 follows a trending pattern.]]>
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