Machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic: a multisource Internet data approach

Andariesta, Dinda Thalia; Wasesa, Meditya

doi:10.1108/jtf-10-2021-0239

Cited by 30 publications

(24 citation statements)

References 36 publications

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“…The use of machine learning has been widely used in the field of tourism, including to predict tourism demand (Ahmed et al, 2007;Li, 2022;Yu & Chen, 2022), marketing strategies for rural tourism (Xie & He, 2022), and recommendations for smart tourism strategies (Ho, 2022). For the case of tourism in Indonesia, its use is still limited, including predicting international tourist arrivals during the Covid-19 period (Andariesta & Wasesa, 2022) and estimating international tourists (Purnaningrum & Athoillah, 2021). The image of data mining through machine learning is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Predicting determinant factors and development strategy for tourist villages

Ariyani¹,

Fauzi²,

Umar³

2023

10.5267/j.dsl

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Tourist village program is one development priority program for rural development. Despite numerous opportunities to develop tourist villages such as the availability of natural resources and high demand for tourist villages recently, some challenges are still faced to develop tourist villages, especially in a developing country such as Indonesia. Governance problems, infrastructure, and effective partnership are among other factors that remain challenging in developing tourist villages. This study attempts to identify factors that determine the state of tourist villages in Indonesia and determine the appropriate strategies for better tourist village development. Using the case of tourist villages in Kedung Ombo, Central Java, a water based attractive tourist village, this study uses both machine learning and multicriteria approaches by means of Promethee in order to address the objective of the study. This study shows that government support, application of information technology, infrastructure, local participation, partnership, and attractive variations, are among the determinant factors that affect tourist village development. The study also reveals that the appropriate strategies for tourist village development include, improving infrastructure, institutional strengthening, and capacity building. This study could be used to assist local national as well as sub-national governments to effectively manage tourist villages in Indonesia.

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Section: Methodsmentioning

confidence: 99%

Predicting determinant factors and development strategy for tourist villages

Ariyani¹,

Fauzi²,

Umar³

2023

10.5267/j.dsl

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“…The role of disaggregated search data in improving tourism forecasts is investigated in the case of Sri Lanka by Wickramasinghe and Ratnasiri (2021) , yet the main merit of their study is estimation of foregone economic benefits due to the pandemic. Furthermore, a multisource internet data approach is recommended for predicting international tourist arrivals to Indonesia during COVID-19 because of its higher forecasting accuracy ( Andariesta & Wasesa, 2022 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Tourism in the EU after the COVID-19 Crisis

Gunter

Smeral

Zekan

2022

Journal of Hospitality & Tourism Research

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The COVID-19 pandemic has restricted both business and social life over the last two years. Stop-and-go policies enacted as containment measures have further impacted the global economy, and tourism in particular. Tourism demand shows only weak signs of a sustainable recovery. The medium-term outlook remains highly uncertain, and yet few studies have addressed the development of the tourism and leisure industries in the years ahead. In this context, we forecast demand in selected EU countries in terms of total expenditure on outbound travel (tourism imports) using a panel pooled Fully Modified Ordinary Least Squares (FMOLS) approach. Baseline and downside scenarios are elaborated to project demand for foreign travel until 2025.

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“…In order to define the solutions, a research framework must first be developed. This research method is modified based on research [10] to build predictive models from data collection to evaluation. The research framework is shown in Figure 1.…”

Section: A Research Frameworkmentioning

confidence: 99%

Application of Predictive Analytics To Improve The Hiring Process In A Telecommunications Company

Jayanti

Wasesa

2022

CoreIT

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Industry 4.0 refers to the increasing tendency towards automation and data exchange in technologies like Big Data and AI. The existence of technology means telecommunication companies have to adapt. Therefore, it takes great people so that the company can continue to survive. The problem that companies often face in hiring great people is that it costs a lot and takes a long time to recruit. Predictive analysis can assist in identifying system issues and solutions. This study aims to develop predictive analytics that can improve recruitment screening based on CVs and find the best predictive model for the company to reduce costs and long recruitment cycles using technology. The authors built an analytical prediction model in four stages: data collection, data preprocessing, model building, and model evaluation. This technique uses Random Forest and Naive Bayes classification algorithms. Both systems properly predicted more data sets with 70% accuracy, 70% precision, and a recall rate above 80%. Compared between the two techniques, Random Forest outperforms Naive Bayes for this predictive model. A lot of people are talking about predictive analytics for hiring, but there aren't many data mining frameworks that can help to find rules based on the CVs of people who have worked for companies before.Keywords: Recruitment, Human Resource, HR Analytic, Predictive Analytic, Random Forest, Naïve Bayes

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Machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic: a multisource Internet data approach

Cited by 30 publications

References 36 publications

Predicting determinant factors and development strategy for tourist villages

Predicting determinant factors and development strategy for tourist villages

Forecasting Tourism in the EU after the COVID-19 Crisis

Application of Predictive Analytics To Improve The Hiring Process In A Telecommunications Company

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