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All Journal Zero : Jurnal Sains, Matematika, dan Terapan
Yunisa, Fahira Audri
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Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics

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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
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v7i1.17341

Abstract

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).
Co-Authors Siregar, Machrani Adi Putri