Sistem Informasi Prediksi Penjualan E-Commerce Menggunakan Analisis Data Historis dan Algoritma MLR

Abstract

Accurate sales to improve business planning and decision making. This study aims to design an information system that utilizes historical data and multiple linear regression algorithms to predict e-commerce sales. This study addresses the current challenges in forecasting uncertain sales by analyzing historical sales data and identifying relevant independent variables, such as marketing efforts, economic factors, and customer behavior. Through the implementation of a multiple linear regression algorithm, the system calculates the relationship between these variables and sales, enabling accurate predictions. The proposed information system provides valuable insights for businesses to optimize inventory management, marketing strategy and resource allocation. The experimental results show the effectiveness of the system in forecasting e-commerce sales, resulting in increased operational efficiency and revenue. This research contributes to the field of e-commerce analytics and assists businesses in making data-driven decisions for sustainable growth.

Keywords: e-commerce, sales prediction, historical data, multiple linear regression, forecasting