FORECASTING OF YAMAHA MOTORCYCLE SALES USING THE WEIGHTED MOVING AVERAGE (WMA) WEB-BASED

Juli Mayani Syahputri Hasibuan; Raja Tama Andri Agus; Rohminatin

doi:10.20884/1.jutif.2022.3.2.216

Juli Mayani Syahputri Hasibuan Program Studi Sistem Informasi, STMIK Royal Kisaran, Indonesia
Raja Tama Andri Agus Program Studi Sistem Informasi, STMIK Royal Kisaran, Indonesia
Rohminatin Program Studi Sistem Informasi, STMIK Royal Kisaran, Indonesia

DOI: https://doi.org/10.20884/1.jutif.2022.3.2.216

Keywords: Forecasting, Yamaha Motorcycle Sales, WMA

Abstract

UD Dunia Sakti Kisaran is a business engaged in the sale of Yamaha motorcycles. Existing activities in the company include stock purchase transactions and sales transactions. The problem that occurs in the company is that stock purchase transactions often have difficulty in determining how much stock to buy for the next period. This results in frequent shortages of stock or even a lot of stock remaining so that it cannot meet customer needs and the accumulation of goods in the warehouse for a long period of time. Sales predictions made by the company are only based on estimates, resulting in increased storage in the warehouse, and increased maintenance costs. To meet consumer needs, the company has not used a mathematical model in estimating the amount of demand for Yamaha motorcycles in the future. Problems like this make activities at UD Dunia Sakti Kisaran less effective in the process of selling and buying stock. To overcome this, a forecasting system is made using the web-based Weighted Moving Average (WMA) method using the PHP programming language and MySQL database. The forecasting system that is designed can provide convenience in forecasting sales of Yamaha motorcycles at UD Dunia Sakti Kisaran in the coming period using the Weighted Moving Average method. By using the WMA method, the average error results obtained for the types of Yamaha NMAX, VIXION, LEXI, XSR, FREEGO and GEAR motorcycles are with an error rate or MAPE value ranging from 19.22%-31.11%, meaning that the model's capability the resulting forecast is feasible/adequate.

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