Sistem Forecasting Permintaan Tempe menggunakan Metode Weighted Moving Average

Abstract

Information systems are created in stores in order to easily process data and produce the information needed quickly, accurately, precisely, effectively, and efficiently, reducing spending costs. The purpose of this study is to produce a forecasting system for tempeh demand to suit consumer needs when marketed. This type of research is research and development using the waterfall model. This model consists of stages of analysis, design, implementation, and testing. The analysis was conducted to obtain the needed data regarding tempeh using the weighted moving average (WMA) method. While we make this design, such as flowcharts, use cases, and data flow diagrams, Furthermore, the implementation of the women's tempeh factory was carried out, and testing was carried out using black box testing. The data we use is request data from September 29, 2023, to December 23, 2023. Our findings show that the mean absolute percentage error (MAPE) to predict tempeh demand is 3.83%; this result is quite small, so the accuracy rate obtained is 96.17%. In addition, the results of the system we developed are also in accordance with the results of manual calculations. This is also evidenced by the absence of errors that occur after testing using black box testing. So that this system can be used to manage tempeh, it is ready to be marketed by the female tempeh factory.