Smart City and Halal Tourism during the Covid-19 Pandemic in Indonesia / Cidade Inteligente e Turismo Halal durante a Pandemia Covid-19 na Indonésia
Bibliographic record
Abstract
ABSTRACTThis article will explore the use of technology in smart cities for the development of Halal Tourism during the Covid-19 pandemic in Indonesia. The function of technology for Halal Tourism services can be utilized for the prevention and transmission of Covid-19 and for realizing changes in the tourism system that are integrally developed with aspects of public health. The method in this article uses content analysis techniques that are based on written or visual material with the main content sourced from journal articles indexed by Scopus and WoS, then operationalized by determining the aims and objectives of the research, compiling the latest content, analyzing content, comparing results analysis, refine results, and conclude findings. This article concludes that smart cities can improve services rapidly to the public in accessing information about Halal Tourism and help control and manage the Covid-19 pandemic in tourism places by increasing detection, mitigating outbreaks, and making effective decisions when situations are critical. Social protection and economic stimulus by the government for tourism actors affected by Covid-19 and technological innovations such as virtual tourism as new services in developing local tourism potential are solutions to new normal conditions while preserving the values of Indonesia's cultural heritage.KEYWORDSTourism; Covid-19; Halal Tourism; Smart City; Indonesia. RESUMOEste artigo analisa o uso de tecnologia por cidades inteligentes, para o desenvolvimento do Turismo Halal durante a pandemia Covid-19, na Indonésia. A presença da tecnologia nos serviços associados ao Turismo Halal pode auxiliar na prevenção à transmissão do Covid-19 e na realização de mudanças no sistema turístico, que são desenvolvidas integralmente em termos de saúde pública. Neste artigo, o método inclui a Análise de Conteúdo de materiais visuais ou escritos, provenientes de artigos em periódicos indexados nas bases Scopus e WoS, operacionalizados a partir os objetivos e metas da pesquisa. A seguir os dados coletados mais recentes foram compilados, comparados e analisados; a análise refinou os resultados, concluindo-se com os principais achados da pesquisa. Neste artigo conclui-se que as cidades inteligentes podem qualificar os serviços oferecidos ao público, através do acesso rápido a informações sobre o Turismo Halal, e assim contribuindo com o controle e gerenciamento da pandemia Covid-19 em locais turísticos, aumentando a detecção, mitigando surtos e tomando decisões eficazes quando as situações são críticas. A proteção social e o estímulo econômico do governo para com os atores do turismo afetados pela Covid-19 e as inovações tecnológicas, como o turismo virtual e os novos serviços no desenvolvimento do potencial turístico local, são soluções para quando das novas condições normais, e para preservação dos valores do patrimônio cultural indonésio.PALAVRAS-CHAVETurismo; Covid-19; Turismo Halal; Cidade Inteligente; Indonésia. AUTORIAAan Jaelani – Doctor. Associate Professor Department of Islamic Economics, Institut Agama Islam Negeri Syekh Nurjati, Cirebon, Indonesia. Orcid: http://orcid.org/0000-0003-2593-7134. Email: iainanjal@gmail.comSlamet Firdaus – Doctor. Associate Professor Department of Islamic Studies, IAIN Syekh Nurjati, Cirebon, Indonesia. Orcid: http://orcid.org/0000-0002-0011-277X. Email: slamet.firgo@gmail.comDidi Sukardi – Doctor. Associate Professor Department of Islamic Business Law, Institut Agama Islam Negeri Syekh Nurjati, Cirebon, Indonesia. Orcid: http://orcid.org/0000-0003-1368-5128. Email: didisukardimubarrak@gmail.com.Syaeful Bakhri - Senior Lecturers Department of Islamic Tourism, Institut Agama Islam Negeri Syekh Nurjati Cirebon, Indonesia. Orcid: http://orcid.org/0000-0003-4703-7719. Email: sultan01aulia@yahoo.comAfif Muamar - Senior Lecturers Department of Islamic Business Law, Institut Agama Islam Negeri Syekh Nurjati Cirebon, Indonesia. Orcid: http://orcid.org/0000-0001-5491-5327. Email: afifmuamar85@yahoo.com REFERENCESAllam, Z. (2019). Cities and the digital revolution: Aligning technology and humanity. Springer Nature. LinkAllam, Z., & Jones, D. S. (2020). On the coronavirus (Covid-19) outbreak and the smart city network: universal data sharing standards coupled with artificial intelligence (AI) to benefit urban health monitoring and management. Healthcare, 8(1), 46. LinkAllam, Z., & Jones, D. S. (2021). 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Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".