Forecasting tourist visits using data decomposition technique and learning optimization of artificial neural network

Herawati, Sri; Latif, Muhammad

doi:10.1088/1742-6596/1811/1/012092

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…These are referred to as inflows and outflows, respectively. Other data presented in a study carried out by Herawati (2021) and Mensah (2021) revealed that the rise in the number of people travelling internationally has a substantial impact on the rise in the amount of money spent by consumers…”

Section: Discussionmentioning

confidence: 99%

Analysis of dynamic agility orchestration resources as a model for Sunda Strait Coastal tourism recovery

Suseno,

Sutisna,

Hidayat

2023

ajcd

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This study aims to do two things: 1) figure out why coastal tourism isn't coming back, and 2) find a way to connect the results of different studies on how tourism affects coastal tourism recovery. Part of the research plan was to do a causal study, which looks for a link between a cause and an effect. Purposive sampling was used to select samples from the study populations of tourism actors in the Sunda Strait and Banten Province. The data were analyzed quantitatively and qualitatively (mixed methods).The IBM SPSS AMOS 21 (Analysis of Moment Structure) and Structural Equation Modeling (SEM) AMOS 21 programs were used to do descriptive analysis and test models and hypotheses. There was a connection between the tsunami disaster and the COVID-19 pandemic. These two things are the main reasons why the Sunda Strait coastal tourism sector is in a crisis, and it is still hard to get out of a crisis like this. Disaster management was shown to have a big, positive effect on the crisis, the way resources are used, and the number of tourists who visit. Tourist visits had a significant impact on dynamic agility orchestration resources and coastal tourism recovery. Also, the tourism crisis helped the coastal tourism industry get back on its feet in a big way. The tourism crisis and coastal tourism recovery benefited significantly from dynamic agility orchestration resources.

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Section: Discussionmentioning

confidence: 99%

Analysis of dynamic agility orchestration resources as a model for Sunda Strait Coastal tourism recovery

Suseno,

Sutisna,

Hidayat

2023

ajcd

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“…However, EMD sometimes produces mixed modes that do not match the data pattern. To overcome its weakness, Ensemble Empirical Mode Decomposition (EEMD) with the addition of white noise is used in forecasting [8]. The research integrates EEMD with an optimized artificial neural network using a genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Complete ensemble empirical mode decomposition with adaptive noise integrating feedforward neural network for tourist arrival forecasting

Herawati

Negara

Latif

2022

J. Phys.: Conf. Ser.

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The tourism sector has an important role in helping the income of a region, especially for economic development and opportunities to expand employment. However, the trend tourist arrival to these tourist attractions has decreased since the COVID-19 pandemic. The government enforces a new normal policy to reopen tourist attractions by implementing health protocols. Local governments and tourism managers need forecasting of tourist arrivals to help plan the tourism sector in the future and anticipate an increase in tourist arrival. Most tourist arrivals are influenced by several factors, such as : seasonality, politics, disasters, crises, and other important events. One method to accommodate these factors is using Ensemble Empirical Mode Decomposition (EEMD). However, EEMD still produces a mixing mode during decomposition. Complete Ensemble Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed to overcome the weaknesses of EEMD. This research integrates CEEMDAN with Feedforward Neural Network (FNN) in generating forecasts. The experiment results show that the integration of CEEMDAN and FNN can produce good forecasting accuracy.

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Forecasting tourist visits using data decomposition technique and learning optimization of artificial neural network

Cited by 2 publications

References 11 publications

Analysis of dynamic agility orchestration resources as a model for Sunda Strait Coastal tourism recovery

Analysis of dynamic agility orchestration resources as a model for Sunda Strait Coastal tourism recovery

Complete ensemble empirical mode decomposition with adaptive noise integrating feedforward neural network for tourist arrival forecasting

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