Peramalan Permintaan Produk Menggunakan ARIMA Berbasis Data Mining - Amiri, Yusril Izzi Arlisa; Wardati, Nanda Kurnia (Jurnal Informatika: Jurnal Pengembangan IT, 2025)
Demand forecasting is a crucial component of business strategy to anticipate customer demand fluctuations and optimize inventory management. Data mining serves as an important analytical approach to uncover hidden patterns in historical data, enabling the generation of accurate predictions. This study aims to forecast the demand for association-related products at Toko As-Sakinah ’Aisyiyah using the Auto Regressive Integrated Moving Average (ARIMA) method, with Moving Average employed as a baseline comparison model. The dataset consists of monthly sales data aggregated from daily records spanning the period of January 2020 to December 2024, resulting in a total of 60 observations. The research stages followed the CRISP-DM framework, encompassing business understanding, data preparation, modeling, evaluation, and deployment. The analysis results indicate that the ARIMA(1,1,1) model is the most suitable, as it meets residual assumptions and yields lower error values compared to Moving Average. The comparison further confirms that ARIMA is more adaptive to trend patterns and short-term fluctuations. The 2025 demand projection reveals a consistent upward trend from January to December. Based on these findings, it is recommended that the store management gradually increase inventory levels to prevent supply shortages in the future