Peramalan Produksi Kelapa Sawit Menggunakan Metode Pegel’s Exponential Smoothing - Sinaga, Yetty Veronica Lestari; Wahyuningsih, Sri; Siringoringo, Meiliyani (EKSPONENSIAL, 2021)
Time series data analysis using Pegel's exponential smoothing method are an analysis of time series that is influenced by trend and seasonal data patterns. The data used in this study was oil palm production in East Kalimantan Province from January 2014 until December 2018. This study aims to predict oil palm production for January, February, March in 2019. Forecasting results were verified based on the MAPE value and monitoring signal tracking method. The results showed that in the Pegel method, the exponential smoothing model without a multiplicative seasonal trend with a MAPE value of 7.84% had better forecasting accuracy than the other methods. The forecast results of the Pegel's exponential smoothing method without a multiplicative seasonal trend can be used to predict the next 3 periods, namely January, February and March 2019. The forecast results for the next 3 periods have increased in succession.