PREDIKSI PENJUALAN KEBAB BERDASARKAN POLA HARI DALAM SEMINGGU MENGGUNAKAN METODE SARIMA

Abstract

Uncertainty regarding daily sales volume poses a challenge for inventory management in the culinary business, as it can lead to a mismatch between stock levels and demand. This study aims to apply the Seasonal Autoregressive Integrated Moving Average (SARIMA) method to forecast daily kebab sales using historical data from January 4, 2025, to January 3, 2026. The research process involved data preprocessing, splitting the data into training and testing sets, testing for stationarity using the Augmented Dickey-Fuller (ADF) test, identifying parameters via Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots, and selecting the best model based on the Akaike Information Criterion (AIC) value. The ADF test results yielded a p-value of <0.05, indicating that the data was stationary. The optimal model identified was SARIMA (1,0,1)(1,0,1)₇, with an AIC value of 2509.27. Model evaluation resulted in an MAE of 15.09 portions, an RMSE of 17.09 portions, and a MAPE of 48.52%; these figures indicate that the average prediction error remains relatively high due to daily sales fluctuations. The model predicts sales of 26–28 portions per day, making it a useful reference for determining daily production volumes and raw material inventory requirements.