Forecasting Export Values in West Sumatra Using Backpropagation Neural Network
DOI:
https://doi.org/10.30871/jaic.v10i1.12199Keywords:
Backpropagation Neural Network (BPNN), Export Forecasting, West Sumatra, Time SeriesAbstract
Export value is an important indicator in supporting regional economic growth. However, its movement tends to be volatile and non-linear, making it difficult to forecast using conventional statistical methods such as ARIMA. This study aims to forecast the export value of West Sumatra Province using an Artificial Neural Network (ANN) with the Backpropagation algorithm. The data used consist of monthly export values from January 2006 to October 2025 obtained from Badan Pusat Statistik (BPS) of West Sumatra Province. The data were normalized and modified using the rolling window method, then divided into training and testing datasets. Several network architectures were evaluated through a trial-and-error process with variations in the number of neurons in the hidden layer. The best model was achieved with the BPNN(12,12,1) architecture, yielding a Mean Square Error (MSE) of 0.0236 and a Mean Absolute Percentage Error (MAPE) of 25.31%. The results indicate that the model is capable of capturing non-linear patterns and reasonably following the trend of the actual data. The selected model was then used to perform short-term forecasting of export values for the period from November 2025 to March 2026. The findings demonstrate that the Backpropagation Neural Network algorithm is effective for forecasting export values in West Sumatra Province. This study contributes theoretically by enriching the application of artificial intelligence in regional economic forecasting and practically by supporting data-driven policy formulation for export strategies in West Sumatra.
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