Application of singular spectrum analysis (SSA) method on forecasting train passengers data in sumatera

A time series is a series of observations of a variable that is collected, recorded, or observed over a period of time in sequence. Singular Spectrum Analysis is a powerful method to analyze time series data by decomposing the original time series data into several small components that can be identified, such as trend, periodic, and noise components. One of the datasets that can be used is data on the number of train passengers in Sumatera in 2013–2022. In this study, the Singular Spectrum Analysis method is used to forecast the number of train passengers in Sumatera in 2013–2022. The best Singular Spectrum Analysis model in this study was obtained at a window length of 22 and a number of groups of 8, with a MAPE value of 19.55%

A Penerapan Metode Double Moving Average dan Double Exponential Smoothing pada Peramalan Nilai Impor Barang Konsumsi Tahun 2017-2022

The smoothing method is classified into two, namely the average smoothing method and the exponential smoothing method. This study examines the application of the double moving average (DMA) and double exponential smoothing (DES) methods in forecasting a data. This study uses 72 data, namely consumer goods import value data for the period January 2017 to December 2022. The method with the lowest MSE and MAPE values is used to predict the import value of consumer goods. The results obtained show that the brown double exponential smoothing method with parameter α, which is 0.1, is the best method for predicting the import value of consumer goods in 2017-2022 with an MSE value of 60374.46 and a MAPE value of 13.66%