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All Journal The Journal of Enhanced Studies in Informatics and Computer Applications
Sentot Achmadi
Software Engineering Department, Malang National Institute of Technology, Indonesia
Computer Science & IT

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Stock Forecasting using Daily Sales Transaction at Hundred Smoke Outlets with the Trend Moment Method Aditya Prakasa; Sentot Achmadi; Joseph Dedy Irawan
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 3 No. 1 (2026): JESICA Vol. 3 No. 1 2026
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v3i1.36

Abstract

Hundred Smoke Outlet is a culinary business experiencing fluctuating customer demand that changes weekly, creating a risk of imbalance between raw material inventory and actual needs. This research aims to develop a web-based forecasting system using the Trend Moment method that processes daily sales data and converts it into a Bill of Materials (BOM) structure to more accurately predict raw material requirements. The system is designed with two user types: admin and staff, who can manage sales data, inventory, and run the forecasting process. Based on black-box testing results for 11 scenarios across various system features, all functions performed as expected with a 100% success rate. Forecast accuracy was evaluated using the Mean Absolute Percentage Error (MAPE), which showed a maximum error of 0.50, a minimum error of 0, and an average error of 22.62%. These results indicate that the system can provide a fairly good level of accuracy in supporting raw material requirement planning.
Co-Authors Aditya Prakasa Joseph Dedy Irawan