Forecasting Data Penjualan Harian Dea Bakery dengan Metode Sarima
DOI:
https://doi.org/10.55606/juisik.v5i3.1611Keywords:
Dea Bakery, Forecasting, Sales, SARIMA, Time SeriesAbstract
Dea Bakery is a bakery business located in Payakumbuh that faces fluctuating product sales influenced by seasonal factors such as religious holidays, weekends, and consumer trends. These conditions require accurate analytical approaches to support business planning and strategic decision-making. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) method to forecast product sales at Dea Bakery Payakumbuh Branch in 2023. The dataset used consists of daily sales records from January to December 2023. The research stages include data collection, preprocessing, data splitting, stationarity testing, ACF and PACF analysis, model parameter selection, and model evaluation using Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that the best model is SARIMA(1,0,1)(0,0,1)[7], which successfully represents weekly seasonality and daily sales trends. Evaluation on the training data achieved MSE of 1147.91, RMSE of 33.88, and MAPE of 16.50%, while the testing data achieved MSE of 969.67, RMSE of 31.14, and MAPE of 15.42%. These values are categorized as good performance, indicating that although there is a slight decrease in accuracy on the testing data, the model is still able to capture the overall sales trends and provide reliable predictions. Therefore, the SARIMA method can be used as a data-driven solution to improve operational efficiency and strengthen the competitiveness of Dea Bakery in the market.
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