FUZZY TIME SERIES IN FORECASTING EXPORT PERFORMANCE OF INDONESIAN SEAWEED PRODUCTS

Abstract

This study applies the Fuzzy Time Series method to forecast the export performance of Indonesian processed seaweed, one of the country's main export commodities, contributing significantly to foreign exchange earnings. The Fuzzy Time Series method is employed for its simplicity and effectiveness in handling time series data with high variability and uncertainty—characteristics often found in export data. Unlike traditional statistical methods, Fuzzy Time Series does not require strict assumptions such as stationarity or normality, making it suitable for real-world applications. Although more appropriate for short-term forecasting, the method still provides meaningful insights for planning and policy. The analysis uses monthly export data from January 2013 to December 2021 to generate forecasts for January to December 2022. The results indicate a positive trend in export performance, with projections showing an increase from 1,707,070 kg in December 2021 to approximately 1,759,763 kg in January 2022. Despite Indonesia's processed seaweed still lagging behind some competitors in terms of competitiveness, its steady growth and rising demand abroad highlight its strong development potential. The forecasting results can be a strategic reference to optimize the commodity's development, increase its added value, and ultimately enhance the country's foreign exchange income.