Single Exponential Smoothing: Metode Peramalan Kebutuhan Vaksin Campak

Abstract

The importance of the measles immunization vaccine for children up to the age of 9 months to prevent children from getting sick with measles or reduce the transmission rate in the surrounding environment, especially at the Gambir Health Center. This demand is still considered ineffective and there is often an oversupply of vaccines, which results in a buildup of vaccines in storage. The purpose of this study was to create a measles vaccine needs forecasting system using the Single Exponential Smoothing (SES) method. The model used to build this system is the Systems Development Life Cycle (SDLC) with stages of analysis, design, implementation, and trial. Data collection techniques use observation, interviews, or smart phones for shooting or sound recording. The analysis technique for system forecasting uses the SES method, while the system testing uses a Blackbox. Our findings show that the lowest MAPE value was obtained at 49.8%. The results of testing the system using a Blackbox that all components in this system are already functioning properly. With this system, it can make it easier for related parties to predict the number of measles vaccines in the new Gambir health center.

Effectiveness of Single and Double Exponential Smoothing: SES, ARRSES and Holt’s Linear for Time Series Data Prediction with Trend and Non-seasonal Characteristic (Covid-19 Vaccinate Case)

“…Selain hasil tersebut, beberapa penelitian dengan menggunakan metode exponential smoothing yang berhasil dan layak digunakan untuk dijadikan salah satu pilihan metode peramalan adalah salah satunya adalah penelitian Azzahra dkk [1]. Dalam penelitian Azzahra dkk dengan judul penelitian "single exponential smoothing: Metode Peramalan Kebutuhan Vaksin campak", hasil penelitian menunjukkan bahwa tingkat kesalahan dalam meramalkan atau memprediksi stok vaksin campak sebesar 49,8%.…”