Perbandingan Metode Exponential Smoothing dan Moving Average pada Arus Barang Bongkar

Abstract

In the logistics and distribution sector, an accurate understanding of the patterns of unloaded freight flows is essential for efficient operational planning. In this context, data analysis methods are key to understanding trends, seasonal patterns, and short-term fluctuations in the flow of unloaded goods. The purpose of this research is to understand the difference between Exponential Smoothing and Moving Average forecasting techniques as a more accurate forecasting technique in predicting the volume of unloaded goods flow. The methodology in the data analysis stage in this research consists of four stages, namely data preparation, creating functions, creating GUI, and displaying visualisation results. The conclusion obtained from this research is that the Exponential Smoothing method with an alpha value of 0.9 has results that are close to the actual value that has been determined. This can be seen from the results of the Mean Absolute Error which describes the average absolute prediction error, Mean Squared Error which describes the average of the squares of prediction errors, Mean Absolute Deviation which describes the average prediction error in the same unit as the data and Mean Absolute Percentage Error which describes the average percentage of prediction errors where the resulting values are 388501761.94 for MSE and 2.55 for MAPE values, then for MAE of 14681.39 and for MAD of 14681.39.