Perbandingan Metode Single Exponential Smoothing dan Regresi Linier untuk Peramalan Persediaan Omeprazole Injeksi 40 mg di RS XYZ - Nur Irhamni Sabrina; Sri Rahmawati; Dwi Handayani (Jurnal Optimalisasi, 2026)
An important factor in ensuring the quality of health services is the availability of drugs in hospitals. Delays in planning drug supply can cause shortages or excess of goods that affect patient services and consequently become very high operational costs. Omeprazole 40 mg is a drug that is often used in hospitals. This study aims to analyze and compare the accuracy of the Single Exponential Smoothing (SES) method with Linear Regression in predicting the need for 40 mg injection drugs with Omeprazole, for the purpose of planning drug supplies in hospitals. This study used a descriptive quantitative approach using secondary data, namely historical data on the monthly requirement of Omeprazole injection 40 mg at Hospital XYZ. Forecasting was carried out for three months using the Single Exponential Smoothing and Linear Regression methods. The accuracy of both forecasting methods is based on the Mean Absolute Deviation (MAD), Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) indicators. The Single Exponential Smoothing method is the method with the highest error, with MAD being 104.76, MSE being 14.748 and MAPE being 68.89%. Therefore, the Linear Regression method is reported to be the best, with MAD being 42.07, MSE being 3.336 and MAPE being 46.77%, as it is able to capture drug demand trend patterns. Therefore, the time of use in planning the supply of 40 mg injectable Omeprazole to support the deployment in hospitals can be more effective and low-cost.