Jurnal Algoritma, Logika dan Komputasi

This study aims to develop a prediction model for South Sulawesi's export volume based on destination countries using the Backpropagation Neural Network (BPNN) method. South Sulawesi has a significant contribution in the export of agricultural, marine, and mining commodities to various Asian and global countries. Common problems in the export process are unpreparedness of goods, limited commodity stocks, and a mismatch between production capacity and destination market demand. The export data used is from 2018 to 2026 with a total of 1,555 rows of data . The BPNN model with a 6-6-1 architecture is applied to study historical patterns and make accurate predictions. The test results show a Mean Squared Error (MSE) value of 0.0161440, with prediction results close to the actual trend. Exports peaked at almost 40 tons in 2019 and decreased significantly in 2023, then are predicted to recover steadily in 2025–2026. The destination countries with the highest export volumes are China, Japan, and East and Southeast Asian countries. The main commodities contributing significantly are octopus, processed wood, and marine products. These findings demonstrate that the BPNN method is effective in identifying export patterns and can be used as a basis for data-driven trade planning at the regional level. This study also underscores the importance of logistical readiness and market diversification in efforts to maintain export sustainability.

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