Application of the Machine Learning Method for Predicting International Tourists in West Java Indonesia Using the Averege-Based Fuzzy Time Series Model

Machine learning is a branch of artificial intelligence where machines are designed to learn on their own without human direction. The machine learning method used by data science is one of them for the prediction process, such as predicting the number of tourists. Tourism is one of the economic sectors that has a direct impact on the people's economy. Based on data from the Central Statistics Agency (BPS), the number of tourists coming to West Java, Indonesia fluctuates, meaning that the number can increase and decrease every month and year. This fluctuating change in the number of tourists has an impact on tourism actors. Therefore we need an appropriate model to make predictions so that related parties, one of which is the local government, can make policies in this sector. The purpose of this study is to propose whether the average-based fuzzy time series model is appropriate for use in predicting the number of foreign tourists coming to West Java, Indonesia. In this study the method used for prediction is the fuzzy time series method and the average-length-based algorithm as a determinant of the length of the interval. The effective interval length can affect the prediction results with a higher level of accuracy. The data used in this study is data from foreign tourists who came to West Java from January 2017 to April 2020 from Badan Pusat Statistik  (BPS) of West Java Indonesia. Based on the results of the prediction test, the Mean Absolute Percentage Error (MAPE) value is 14.71%, the results show that the average-based fuzzy time series model is good for prediction. This can be a decision support for related parties to make policies related to tourism preparation and planning efforts in West Java, Indonesia

Using social media big data for tourist demand forecasting: A new machine learning analytical approach

This study explores the possibility of using a machine learning approach to analysing social media big data for tourism demand forecasting. We demonstrate how to extract the main topics discussed on Twitter and calculate the mean sentiment score for each topic as the proxy of the general attitudes towards those topics, which are then used for predicting tourist arrivals. We choose Sydney, Australia as the case for testing the performance and validity of our proposed forecasting framework. The study reveals key topics discussed in social media that can be used to predict tourist arrivals in Sydney. The study has both theoretical implications for tourist behavioural research and practical implications for destination marketing

Application of the Machine Learning Method for Predicting International Tourists in West Java Indonesia Using the Averege-Based Fuzzy Time Series Model

The purpose of this study is to propose whether an average-based fuzzy time series model is appropriate for use in predicting the number of foreign tourists coming to West Java, Indonesia. Machine learning is a branch of artificial intelligence where machines are designed to learn on their own without human direction. One of the machine learning methods used by data science is for prediction processes, such as predicting the number of tourists. Tourism is one of the economic sectors that has a direct impact on the community's economy. Based on data from the Badan Pusat Statistik (BPS), the number of tourists coming to West Java Indonesia fluctuates, meaning that the number can increase and decrease every month and year. Changes in the number of tourists that fluctuate are one of the problems that have an impact on tourism actors. Therefore, the solution given to answer this problem is that an appropriate model is needed to predict the number of tourists visiting West Java. The contribution of this research is to help related parties in predicting the number of foreign tourists so that it can be used as one to make policies related to tourism preparation and planning efforts in West Java, Indonesia. The method used in this research is a case study approach, where the case study is taken from data on foreign tourists visiting West Java from 2017 to 2020. For the prediction process, the method used is the fuzzy time series method and the average length-based algorithm as the determinant of the interval length. Effective interval length can affect prediction results with a higher level of accuracy. Based on the prediction test results, the Mean Absolute Percentage Error (MAPE) value is 14.71%. These results indicate that the fuzzy time series model based on the average interval length is good for prediction

Modelos de pronósticos de la demanda turística: una revisión de los estudios actuales

Tourism has gained vital importance in recent times as it is one of the economic activities that brings the greatest benefits to a country, both in the social, economic and environmental spheres. Consequently, demand forecasting models in the sector are adequate tools that support decision-making. In this sense, several authors have made important contributions in the field of science that help improve tourism management. This leads to the objective of analyzing current trends in tourism forecasting models using the R bibliometrix tool, covering 254 research articles published between 2017 and 2021. The main results show that the models for forecasting tourism demand they are constantly evolving and there is no single model that works well for all situations. It can also be seen that due to the COVID-19 pandemic, the forecast models for that year were unusable; however, it was the year with the most publications. Similarly, this research allowed to identify the main countries, scientific journals and authors who address the study of tourism demand.El turismo ha cobrado vital importancia en los últimos tiempos al ser una de las actividades económicas que mayores beneficios aportan a un país, tanto en el ámbito social, económico como ambiental. Consecuentemente, los modelos de pronósticos de la demanda en el sector constituyen herramientas adecuadas que sirven de soporte en la toma de decisiones.  En este sentido, varios autores han realizado importantes aportes en el campo de la ciencia que ayudan a mejorar la gestión turística. Lo que conlleva a plantear como objetivo el análisis de las tendencias actuales de los modelos de previsión turística mediante la herramienta R bibliometrix, cubriendo 254 artículos de investigación publicados entre 2017 y 2021. Los principales resultados arrojan que los modelos para el pronóstico de la demanda turística se encuentran en una evolución constante y no existe un modelo único que funcione bien para todas las situaciones. También se puede apreciar que a causa de la pandemia de COVID-19, los modelos de pronóstico para ese año fueron inservibles; sin embargo, fue el año de más publicaciones. De igual modo, la presente investigación permitió identificar los principales países, revistas científicas y autores que abordan el estudio de la demanda turística

Analysing and forecasting tourism demand in Vietnam with artificial neural networks

Mestrado APNORVietnam has experienced a tourism boom over the last decade with more than 18 million international tourists in 2019, compared to 1.5 million twenty-five years ago. Tourist spending has translated into rising employment and income for the tourism sector, making it the key driver to the socio-economic development of the country. Facing the COVID-19 pandemic, Vietnam´s tourism has suffered extreme economic losses. However, the number of international tourists is expected to reach the pre-pandemic levels in the next few years after the COVID-19 pandemic subsides. Forecasting tourism demand plays an essential role in predicting future economic development. Accurate predictions of tourism volume would facilitate decision-makers and managers to optimize resource allocation as well as to balance environmental and economic aspects. Various methods to predict tourism demand have been introduced over the years. One of the most prominent approaches is Artificial Neural Network (ANN) thanks to its capability to handle highly volatile and non-linear data. Given the significance of tourism to the economy, a precise forecast of tourism demand would help to foresee the potential economic growth of Vietnam. First, the research aims to analyse Vietnam´s tourism sector with a special focus on international tourists. Next, several ANN architectures are experimented with the datasets from 2008 to 2020, to predict the monthly number of international tourists traveling to Vietnam including COVID-19 lockdown periods. The results showed that with the correct selection of ANN architectures and data from the previous 12 months, the best ANN models can forecast the number of international tourists for next month with a MAPE between 7.9% and 9.2%. As the method proves its forecasting accuracy, it would serve as a valuable tool for Vietnam´s policymakers and firm managers to make better investment and strategic decisions to promote tourism after the COVID-19 situation.O Vietname conheceu um boom turístico na última década com mais de 18 milhões de turistas internacionais em 2019, em comparação com 1,5 milhões há vinte e cinco anos. As despesas turísticas traduziram-se num aumento do emprego e de receitas no sector do turismo, tornando-o no principal motor do desenvolvimento socioeconómico do país. Perante a pandemia da COVID-19, o turismo no Vietname sofreu perdas económicas extremas. Porém, espera-se que o número de turistas internacionais, pós pandemia da COVID-19, atinja os níveis pré-pandémicos nos próximos anos. A previsão da procura turística desempenha um papel essencial na previsão do desenvolvimento económico futuro. Previsões precisas facilitariam os decisores e gestores a otimizar a afetação de recursos, bem como o equilíbrio entre os aspetos ambientais e económicos. Vários métodos para prever a procura turística têm sido introduzidos ao longo dos anos. Uma das abordagens mais proeminentes assenta na metodologia das Redes Neuronais Artificiais (ANN) dada a sua capacidade de lidar com dados voláteis e não lineares. Dada a importância do turismo para a economia, uma previsão precisa da procura turística ajudaria a prever o crescimento económico potencial do Vietname. Em primeiro lugar, a investigação tem por objetivo analisar o sector turístico do Vietname com especial incidência nos turistas internacionais. Em seguida, várias arquiteturas de ANN são experimentadas com um conjunto de dados de 2008 a 2020, para prever o número mensal de turistas internacionais que se deslocam ao Vietname, incluindo os períodos de confinamento relacionados com a COVID-19. Os resultados mostraram, com a correta seleção de arquiteturas ANN e dados dos 12 meses anteriores, os melhores modelos ANN podem prever o número de turistas internacionais para o próximo mês com uma MAPE entre 7,9% e 9,2%. Como o método evidenciou a sua precisão de previsão, o mesmo pode servir como uma ferramenta valiosa para os decisores políticos e gestores de empresas do Vietname, pois irá permitir fazer melhores investimentos e tomarem decisões estratégicas para promover o turismo pós situação da COVID-19