Aplikasi Auto Sales Forecasting Berbasis Computational Intelligence Website untuk Mengoptimalisasi Manajemen Strategi Pemasaran Produk - Bakri, Rizal; Data, Umar; Astuti, Niken Probondani (JSINBIS (Jurnal Sistem Informasi Bisnis), 2019)
Business analytics plays an important role in optimizing the management of product marketing strategies. One of the most popular analytical tools in business analytics is sales forecasting. Businesses need to conduct sales forecasting to optimize marketing management in the form of product availability predictions, predictions of capital adequacy, consumer interest, and product price governance. However, the problem that is often encountered in forecasting is the number of forecasting methods available so that it makes it difficult for business people to choose the best forecasting method. The aims of this research is to develop a forecasting software tha can be accessed online based on computational intelligence, which is a software that can make forececasting with various methods and then intelligently choose the best forecasting method. The software development method used in this study is the SDLC with waterfall model. The result of this research is the Auto sales forecasting software was developed using the R programming language by combining various package and can be accessed online through the page Http://bakrizal.com/AutoSalesForecasting. This software can be used to conduct forecast analysis with various methods such as Simple Moving Average, Robust Exponential Smoothing, Auto ARIMA, Artificial Neural Network, Holt-Winters, and Hybrid Forecast. This software contains intelligence computing to choose the best forecasting method based on the smallest RMSE value. After testing the sales transaction data at the Futry Bakery & Cake Shop in Makassar, the results show that the Robust Exponantial Smoothing method is the best forecasting method with an RMSE value of 0.829Â Â