Web-Based Car Sales Prediction System Using the ARIMA (Autoregressive Integrated Moving Average) Model for Optimizing Automotive Marketing Strategies
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This study aims to develop a web-based car sales prediction system using the ARIMA (Autoregressive Integrated Moving Average) model to support the optimization of marketing strategies in the automotive sector. With the rapid growth of the automotive industry in Indonesia, companies, particularly car showrooms, face the challenge of accurately forecasting vehicle demand. Therefore, an ARIMA-based prediction system can assist in estimating future sales based on historical data, thereby improving stock management, distribution, and marketing strategies. The system was developed using five years of historical sales data and implemented the ARIMA model to forecast car sales for upcoming periods. It was built with the Python programming language, employing Flask for the backend and HTML, CSS, and JavaScript for the frontend. The prediction results are presented in the form of interactive graphs, enabling users to make data-driven decisions more effectively. System evaluation was carried out by measuring prediction accuracy using MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) metrics. The testing results indicate that the ARIMA model can generate predictions with a high level of accuracy, assisting showrooms in planning stock and promotional activities more efficiently. Furthermore, the system is equipped with a responsive user interface, making it easily accessible via mobile devices. This research contributes to the utilization of technology in sales planning, particularly in the automotive sector, by enabling more precise, efficient, and data-driven decision-making.
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