PERBANDINGAN METODE DOUBLE MOVING AVERAGE DAN METODE DOUBLE EXPONENTIAL SMOOTHING (Studi Kasus : Peramalan Pasien COVID-19 di Australia)

Farikhatun Nabila, NIM.: 18106010043 (2022) PERBANDINGAN METODE DOUBLE MOVING AVERAGE DAN METODE DOUBLE EXPONENTIAL SMOOTHING (Studi Kasus : Peramalan Pasien COVID-19 di Australia). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

The raising of unpredictable COVID-19 cases makes it difficult to predict the amount of medical personnel, medical equipment, and health facilities that are needed. Forecasting helps decision-makers make better or more focused estimates by estimating future uncertainty. The double moving average (DMA) method and the double exponential smoothing (DES) method are two techniques that can be used to forecast time series data. This study aims to compare between the DMA and DES methods. This study used daily data of confirmed cases of COVID-19 in Australia from September 1st 2021 to February 28th 2022. Based on the results and discussion, the best parameters for each method were obtained. The DMA method with 7 period parameters obtained the smallest Mean Absolute Error (MSE) and Mean Absolute Percentage Error (MAPE) values of 81,042,735 and 0.1467% respectively. Meanwhile, the DES method with parameter of and obtained MSE and MAPE values of 77490510 and 0.1424% respectively. Based on the average value of the least squared error and the average value of the smallest percentage error, the DES method is more appropriate for predicting confirmed cases of COVID-19 in Australia.

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