THE APPLICATION OF SEASONAL TREND DECOMPOSITION USING LOESS FOR EXPORT FORECASTING BY ECONOMIC COMMODITY GROUP IN NORTH SUMATRA

Yunisa, Fahira Audri
Siregar, Machrani Adi Putri

Zero : Jurnal Sains, Matematika, dan Terapan
Vol 7, No 1 (2023): Zero: Jurnal Sains Matematika dan Terapan

In export data, there are often seasonal fluctuations caused by various factors, and STL (Seasonal Trend decomposition using Loess) can help effectively separate these seasonal components. STL is an algorithm developed to decompose a time series into three components: trend, seasonal, and remainder, aiding in a better understanding of the underlying patterns and variations in the data. The data taken in this study are data on the number of exports (tonnes) in the period January 2018 to December 2022 sourced from bps. From the forecasting results it can be concluded that the largest BM export value is 6357.6131 (tons), the largest BP export value is 859804.0 (tons) and the largest BP export value is 113157.64 (tons).