Selamat Datang Di Repository Universitas Royal

Information systems are created in stores so that they can easily process data and produce information that is needed quickly, accurately, precisely, effectively and cost-effectively. Nando Ponsel is a business engaged in the sale of mobile phones that has been established since 2010, located at Jl. Lintas Sumatera, Kel. Kedai Landang, District. East Range, Kab. Asahan. Cellphones marketed by Nando Ponsel are various types of cellphones, such as Oppo, Vivo, Xiaomi, Samsung, and Realme brands. With the current information technology, Nando mobile phones can apply a forecasting science in the work program in the store for selling cellphones. The forecasting system that is made can predict the number of cellphone sales for the future period. A method is needed so that it can be used in estimating the number of cellphones that will be marketed according to the number of consumer demand by using certain methods so that there is no longer a shortage or accumulation of the number of cellphones that can harm Nando Ponsel. This also makes it difficult for employees to check and makes it difficult for store owners to estimate the supplies that must be ordered. There are often difficulties in determining the number of cellphones that must be available in the next month in order to still be able to meet the needs of each customer and often the accumulation of cellphone supplies for a long period of time which results in many losses. Therefore, in this research, a program is made that implements the single moving average method to help predict the number of cellphone sales for the future on the Nando Ponsel. The prediction accuracy level of the Single Moving Average method to predict cellphone sales on Nando Ponsel is calculated in several ways, namely Mean Absolute Error (MAE) which is 11.48, Mean Squared Error (MSE) which is 177.77, Root Squared Error (RMSE) 13.33, and Mean Absolute Percentance Error (MAPE) which is 7,74%. With the design of the forecasting system application to predict the amount of antibiotic stock, this is made to get more objective results.

Keywords: Forecasting, Single Moving Average Method, Cellphone Sales