Foreign Tourist Arrivals Forecasting Using Recurrent Neural Network Backpropagation through Time

Vihikan, Wayan Oger; Putra, I Ketut Gede Darma; Dharmaadi, I Putu Arya

doi:10.12928/telkomnika.v15i3.5993

Cited by 13 publications

(8 citation statements)

References 9 publications

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“…Next will be the normalization process for the data that passes the preprocessing stage uses the equation below. In this study there is a comparison between the performance of two activation functions, namely binary sigmoid and tanh, so that there are two ranges of normalization values, 0 -0.9 for binary sigmoid activation function and -0.9 -0.9 for tanh activation function [9]. (1) with:…”

Section: Methodsmentioning

confidence: 99%

Foreign Tourist Arrival Forecasting to Bali Using Cascade Forward Backpropagation

Asvikarani

Widiartha

Raharja

2020

kursor

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Bali has a recognized tourism potential in the world arena. In order to improve the quality and development of the tourism sector in the midst of global competition, it is necessary to formulate appropriate strategies by decision makers such as private parties and government. In support of more accurate decision making, the authors make a system of forecasting the number of foreign tourist visits to Bali Province using Cascade Forward Backpropagation (CFB) method with coverage of Australia, Japan, and United Kingdom which are the top 3 countries with the highest foreign tourist arrival to Bali in that years. Factors used as input in forecasting include the number of visits of foreign tourists the previous year, the population of countries of origin of foreign tourists, Gross Domestic Product at current prices of countries of origin of foreign tourists, and Relative Consumer Price Index Origin of foreign tourists. In this study, optimization of activation function parameters, hidden neurons, and learning rate to obtain forecasting results with the lowest error rate. Forecasting results using the CFB method produces a fairly good accuracy with MAPE range of 6 - 30% where the activation function tanh work better than sigmoid activation function.

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Section: Methodsmentioning

confidence: 99%

Foreign Tourist Arrival Forecasting to Bali Using Cascade Forward Backpropagation

Asvikarani

Widiartha

Raharja

2020

kursor

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“…[68] proposed a kernel-based extreme learning machine to forecast tourist arrivals, being a successful model because its better precision compared to other methods. In [66] BPNNs are used to predict tourist arrivals in Bali. Lastly, in 2018 Kamel et al [1] investigated the performance of seven different ML methods (multi-layer perceptron, RBF, generalized regression neural networks, KNN, classification and regression trees, SVR and Gaussian process regression), showing that there are differences between these methods, but also that there is no best method in the obtained results, which were analyzed by mean absolute percentage error.…”

Section: Tourism Demand Forecastingmentioning

confidence: 99%

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Guerra-Montenegro

Sánchez-Medina

Laña

et al. 2021

Applied Soft Computing

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This research work presents a detailed survey about Computational Intelligence (CI) applied to various Hotel and Travel Industry areas. Currently, the hospitality industry's interest in data science is growing exponentially because of their expected margin of profit growth. In order to provide precise state of the art content, this survey analyzes more than 160 research works from which a detailed categorization and taxonomy have been produced. We have studied the different approaches on the various forecasting methods and subareas where CI is currently being used. This research work also shows an actual distribution of these research efforts in order to enhance the understanding of the reader about this topic and to highlight unexploited research niches. A set of guidelines and recommendations for future research areas and promising applications are also presented in a final section.

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“…In a travel route, TSP can be Journal Homepage: http://iaescore.com/journals/index.php/IJECE 1276 Ì ISSN: 2088-8708 analogous to visiting tourist attractions exactly once in one day where the start and end node is the tourist's lodge. There are plenty of algorithms which can be implemented to solve TSP, i.e.,Genetic Algorithm (GA) [4], Simulated Annealing (SA) [5], Taboo Search [6], Particle Swarm Optimization [7], Harmony Search [8], Quantum Annealing [9], Ant Colony optimalization (ACO) [10], neural network [11] and so on. We use Simulated Annealing algorithm (SA) to generate travel route schedule, because SA is an algorithm with the best travel solution quality based on 3 aspects: mean value, standard deviation, running time [12].…”

Section: Introductionmentioning

confidence: 99%

Travel route scheduling based on user’s preferences using simulated annealing

Baizal

Lhaksmana

Rahmawati

et al. 2019

IJECE

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<p>Nowadays, traveling has become a routine activity for many people, so that many researchers have developed studies in the tourism domain, especially for the determination of tourist routes. Based on prior work, the problem of determining travel route is analogous to finding the solution for travelling salesman problem (TSP). However, the majority of works only dealt with generating the travel route within one day and also did not take into account several user’s preference criteria. This paper proposes a model for generating a travel route schedule within a few days, and considers some user needs criteria, so that the determination of a travel route can be considered as a multi-criteria issue. The travel route is generated based on several constraints, such as travel time limits per day, opening/closing hours and the average length of visit for each tourist destination. We use simulated annealing method to generate the optimum travel route. Based on evaluation result, the optimality of the travel route generated by the system is not significantly different with ant colony result. However, our model is far more superior in running time compared to Ant Colony method.</p>

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Foreign Tourist Arrivals Forecasting Using Recurrent Neural Network Backpropagation through Time

Cited by 13 publications

References 9 publications

Foreign Tourist Arrival Forecasting to Bali Using Cascade Forward Backpropagation

Foreign Tourist Arrival Forecasting to Bali Using Cascade Forward Backpropagation

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Travel route scheduling based on user’s preferences using simulated annealing

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