Implementation of Web Based Motorcycle Sales Prediction System Using the Least Squares Method - Hidayatullah, Syamsudin; Mauladi, Kemal Farouq; Wahyudi, M Hasan (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2026)
The development of information technology has brought significant changes in business data management, including in the automotive industry. Dony Jaya Motor, as one of the motorcycle dealers, faces challenges in predicting sales, particularly in balancing stock availability with market demand. This study aims to develop a web-based motorcycle sales prediction system using the Least Squares method due to its ability to identify linear trend patterns from historical data, producing accurate and measurable sales projections. The data used cover motorcycle sales from May 2024 to April 2025. The implementation results show that the Least Squares method provides good predictive accuracy, with the average Mean Absolute Percentage Error (MAPE) value below 10%, indicating a very low prediction error rate. For example, for the Honda Beat 2015 type, the predicted sales for May 2025 were 5.67 units compared to the actual 6 units, resulting in a MAPE value of 4.67%. The developed system includes features for data input, graphical visualization, and real-time prediction reporting. The application of the Least Squares method in this web-based system has proven to assist management in stock planning, improve decision-making processes, and enhance overall operational efficiency and effectiveness within the company.