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Athallah, Muhamad Reza
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Implementasi Data Mining Untuk Prediksi Peramalan Penjualan Produk Hj Karpet Menggunakan Metode Linear Regression Athallah, Muhamad Reza; Rozi, Anief Fauzan
Jurnal Sains dan Teknologi (JSIT) Vol. 2 No. 3 (2022): September - Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v2i3.550

Abstract

HJ Karpet is a business engaged in commerce that sells a variety of carpet types and where inventory is crucial. The problem that HJ Karpet has always faced is an abundance of carpet inventory or overstock. Forecasting with the linear regression method is the solution for assisting with stock planning. Forecasting is the process of calculating future values using historical information. This study used linear regression to calculate and design the system, which was then implemented as a system. This study's objective is to forecast sales of HJ Karpet products using historical sales data provided by HJ Karpet for the purpose of generating a sales forecast. This study resulted in the development of a sales forecasting system that will be used to provide stock planning recommendations for the subsequent months. The results of the manual calculation using the linear regression equation predict a value of 52,093 for January 2023 with an error MAPE (mean absolute percentage error) calculation value of 5,667205%, indicating that the results of the regression-based forecasting model have a high degree of accuracy.
Co-Authors Anief Fauzan Rozi, Anief Fauzan