DATA MINING DALAM PREDIKSI JUMLAH PASIEN DENGAN REGRESI LINEAR DAN EXPONENTIAL SMOOTHING - Daniel Iskandar (Universitas Trilogi, 2023)
The number of patient visits is one of the main factors that determine the strategies and policies decided by hospital management because the number of patients affects all major aspects of hospital services, such as drugs and medical supplies stocks, the number of doctors and nurses, the rooms capacity, and many other services. Hospital ‘XYZ’ needs a method to predict the number of patients that will visit the hospital in order to provide the best service efficiently. Previous studies have shown that the Linear Regression and Exponential Smoothing methods produced good predictive values, but no research has been found that comparing the two methods. To fill this gap, the purpose of this study is to compare the prediction results of the number of patients between Linear Regression method and Exponential Smoothing method. Number of daily patients data in 2021 was collected using Data Mining. The prediction comparison was carried out two times, first is the prediction of the daily number of patients and the second is the prediction of the number of weekly patients. The first comparison showed that Linear Regression predicted better by having 23.90% MAPE, while Exponential Smoothing had 27.62%. In the second prediction, Linear Regression again produced a better MAPE value with 4.66%, while Exponential Smoothing was 6.82%.