Evaluation of generated bootstrap weight in layer perceptron for Southeast Asia visitors during COVID19 outbreak

Abstract

The pandemic COVID-19 effected the global business sector include the tourism industry. Forecasting the visitor arrival from Southeast Asia is a vital for organized the economy impact at Malaysia state, particularly during this outbreak. Neural network family has been substantial approaches in tourism and the economy. The layer perceptron is a part of the neural network model which is used to produce accurate forecasting. However, the inherent biasness in the perceptron algorithm could lead to an underfitting problem which eventually leads to poor performance of forecast accuracy. The motivation of this study is to improve the accuracy of single-layer perceptron in forecasting the Southeast Asia visitors in Malaysia during COVID19. In this study, the bootstrap weights are generated at the hidden layer to reduce the biasness in output layer. The forecasting result of generated bootstrap weight model is compared with conventional perceptron model in terms of small bias estimation. The statistical results revealed that the generated bootstrap weight in perceptron provides accurate forecasting for Southeast Asia visitors during COVID-19.