Putri, Veneria Ananda (2024) Prediksi Jumlah Pasien Di Rsu St. Yoseph Labuan Bajo Menggunakan Metode Single Exponential Smoothing Dan Double Exponential Smoothing. Undergraduate thesis, Fakultas Teknologi Informasi Universitas Merdeka Malang.

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Abstract

St. Yoseph Labuan Bajo Hospital is currently experiencing an erratic surge in patients, so the hospital lacks medicines, medical supplies and medical personnel such as specialists and employees to provide services to patients. This hospital is
still relatively new and really needs a prediction of the number of patients in the future. Therefore, it is necessary to anticipate this problem with forecast data in the form of forecasting results from the next few periods. This research uses two
algorithms Single Exponential Smoothing and Double Exponential Smoothing. The purpose of this study is to evaluate the performance of the two methods in providing
accurate predictions to assist hospital management in resource planning and decision making. Researchers used a quantitative forecasting approach method using past data for the period Jan-2022 to Jun-2024. To calculate the forecasting value, the parameters used for the Single Exponential Smoothing method are α = 0.5 and α = 0.7. While the values used for Double Exponential Smoothing are α = 0.4 with β = 0.7 and α = 0.6 with β = 0.8. From the results of research that has been carried out using the two methods Single Exponential Smoothing and Double Exponential Smoothing, getting the smallest MAPE value is with the Single Exponential Smoothing method with a constant α = 0.7 obtained for outpatients MAPE 2%, for inpatients obtained MAPE 3%, and for total patients obtained
MAPE 4%. Meanwhile, the Double Exponential Smoothing method obtained the largest MAPE for outpatients with a value of α=0.4 and β=0.7 obtained 20% MAPE, for inpatients the value of α=0.6 and β=0.8 obtained 20%, and for total patients the value of α=0.4 and β=0.7 obtained 16%. Using this approach, a
prediction of approximately 102.5980 was obtained using Single Exponential Smoothing with α = 0.7 to predict the number of patients in the future, this study shows that Single Exponential Smoothing with the appropriate constant is better than Double Exponential Smoothing because it produces more accurate
predictions and has a lower MAPE

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