Modeling the number of unemployed in South Sumatra Province using the exponential smoothing methods

Gustriansyah, Rendra; Alie, Juhaini; Suhandi, Nazori

doi:10.1007/s11135-022-01445-2

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…The output of this stage is the result of forecasting SMBII Museum visits based on the input dataset. DESM is a model developed to overcome differences that arise between actual data and predicted values when there is a trend in the data plot [20] trend shows the tendency to increase or decrease in data. The trend is a smoothed estimate of the average growth at the end of each period [21].…”

Section: B Data Processingmentioning

confidence: 99%

Forecasting a museum visit post pandemic using exponential smoothing model

Puspasari,

Gustriansyah,

Sanmorino

2023

J.INFOTEL

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This paper aims to evaluate the performance of a machine learning model for predicting the number of visitors to a museum after the COVID-19 pandemic. The easing of policies that began to be implemented by the Palembang city government after the end of the pandemic at the end of 2022 became a momentum in predicting the number of visits to the SMBII museum. During the pandemic the museum experienced a very drastic decline due to closures and restrictions on activities at the museum and had an impact on achieving the museum's targets in the fields of tourism and education. Museum managers need to establish a strategy as an effort to achieve the targets set during the post-pandemic period. This study predicts the number of visits to the SMBII museum in post-pandemic years by applying the double exponential smoothing (ESM) model. The dataset used is SMBII museum visit data which is divided into three categories of visitors, namely students, local and foreign. The evaluation results show that the double ESM model has the best performance with MSE = 3.8 and a = 0.9. The phenomena that occurred in the student visitor category affected ESM's performance in predicting visits where MSE in the post-pandemic period had a 200% higher value than before the pandemic which was influenced by the implementation of post-pandemic policies in museums. With the forecasting results in this study, it is hoped that it can become information in developing strategies and improving the performance of post-pandemic museums

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Section: B Data Processingmentioning

confidence: 99%