Forecasting Service Sales Turnover Using Double Exponential Smoothing Method (case Study: Kinclong Sub)

Abstract

As the lifestyle of modern society develops, service companies are businesses in the form of services that sell special
skills in order to make it easier for customers, one of which is shoe washing services. However, the problem that
usually occurs is that the number of service requests is not certain which results in raw materials that sometimes
accumulate or run out every month, errors in predicting the sales turnover of shoe washing services can result in
owners experiencing losses if the target raw materials are not appropriate.
This study aims to create a forecasting system for the amount of turnover from each service sale by Kinclong Sub using
the Double Exponential Smoothing method, so that the raw material needs needed every month can be optimal. The
best turnover forecasting results for each service on Kinclong Sub for the following month, May 2022, were Rp.
10.006,248 with a MAPE value of 17.86% for DeepClean services. And Unyellowing got Rp. 1493,374 with a MAPE
value of 17.18 %. So it can be concluded that the service that has the most turnover for the next month is DeepClean,
the forecasting results can make it easier for Kinchlong Sub shop owners to provide raw materials from services that
are most in demand by consumers.