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All Journal UNP Journal of Statistics and Data Science
Silvia Triana
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Comparison Performance of SARIMA and Exponential Smoothing Holt-Winter’s models for Forecasting turnover PT. Indah Logistik Cargo Padang Silvia Triana; Dina Fitria; Yenni Kurniawati; Admi Salma
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss4/432

Abstract

Forecasting is an important part of corporate decision making. With forecasting, companies can predict future conditions and demand so that they can make appropriate and strategic decisions. PT. Indah Logistik Cargo Padang's turnover data contains trend and seasonal elements that are forecasted using a time series model. This study was conducted to determine the best model for forecasting PT. Indah Logistik Cargo Padang's revenue in the coming period. The methods used in this study are the SARIMA method and Holt-Winter's Exponential Smoothing. The best model was obtained from the results of a comparative analysis of the two methods, as seen in the forecasting error rate determined by the mean absolute percentage error value. For forecasting the revenue of PT. Indah Logistik Cargo Padang, the best model used was SARIMA with a MAPE value of 3.9%.
Co-Authors Admi Salma Dina Fitria Yenni Kurniawati