Implementasi Metode Time Series dalam Forecasting Penggunaan SatuSehat

Meilinda, Kharomah Syifa (2023) Implementasi Metode Time Series dalam Forecasting Penggunaan SatuSehat. Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

Since the Covid-19 pandemic in Indonesia, the government has implemented new rules to deal with Covid-19. The existence of a pandemic has also given rise to new innovations in the field of technology to assist the government and society in monitoring the spread of Covid-19 in Indonesia. This innovation is the PeduliLindungi application, this application has a tracking feature and other features that are always used by the community. The influence of PeduliLindungi is like an obligation for the Indonesian people, but since the endemic, the PeduliLindungi application has changed to the SatuSehat application which functions to integrate Indonesian public health data. The change in the application from PeduliLindungi to SatuSehat has reduced the level of application usage, so this study aims to determine the enthusiasm of the public in using the SatuSehat application as a health platform provided by the government in Indonesia. The research was conducted by predicting the number of users of the SatuSehat application based on user data from October 2022 – February 2023. The forecasted data can be used to determine whether the number of users is likely to decrease or increase in the next seven months. The research was conducted using the Time Series method with Single Moving Average (SMA) and Single Exponential Smoothing (SES). The results of the research are in the form of forecasting calculations that can be used to determine the enthusiasm of the community to accept and use the health platform provided by the government. After doing the calculations, it is known that the research results show that SatuSehat application users tend to experience a decrease from March 2023 – July 2023. Error Forecasting shows that an accurate method for performing forecasting calculations for SatuSehat application users is the SES method with a constant α = 0.9 which obtains the results MAD calculation is 1,627,251 and MAPE is 9.34%. Keywords: Covid-19, SatuSehat, Time Series, Single Moving Average, Single Exponential Smoothing.

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