Estimating the number of foreign tourists using artificial intelligence algorithm and analyzing the socio-economic impact on the community

Abstract

The high interest of foreign tourists to a region will affect the socio-economic indicators of the community. This study aims to forecast the number of foreign tourists to West Nusa Tenggara (NTB) using the backpropagation-relevance vector machine (BP-RVM) algorithm with the last 10 years of data. Forecasting using architecture is 12-25-7-3-1, activation function is radbas-logsig-logsig-purelin, training function is trainrp, learning rate is 0.1, momentum is 0.9, and RBF gamma is 0.01. The forecasting results show that in 2024 it is predicted that there will be 986,109 foreign tourists with a monthly average of 82,176, a decrease of 3.67%. The impact of foreign tourist arrivals in NTB (1) over the past three years has been significant and multidimensional, including quality of life, living standards, education, migration, public health, contribution of the tourism sector, regional income, and investment, (2) infrastructure development such as roads, bridges, and airports increased, (3) tourist demand encouraged the growth of MSMEs, (4) vocational training and education was organized, (5) the education curriculum in several schools was adjusted to the needs of the tourism industry, (6) the tourism sector contributed 10.62% to NTB's GRDP and generated IDR 10 trillion in foreign exchange, (7) regional income from tourism reached IDR 2.5 trillion which was used for infrastructure development and public services, and (8) investment in the tourism sector created new jobs, reduced unemployment, and increased local community income. The implementation of these research findings suggests that to sustain and enhance the tourism sector's contribution to the economy of West Nusa Tenggara, local government efforts should focus on infrastructure improvements, vocational training development, and curriculum adjustments to meet the tourism industry's needs.