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Globalization has undeniably positioned cross-border tourism as a crucial catalyst for driving economic, social, and cultural exchanges. As an archipelago, Indonesia shares borders with neighboring countries and possesses considerable potential for cross-border tourism. This study aims to explore the spatial typology of cross-border tourism in Indonesia. This study examines three of Indonesia's nine designated cross-border tourism areas, which correspond to the country's three land borders. This study utilized a mixed methods approach to gather data. Regional typology data were collected through map interpretation, direct observation, and secondary sources. Additionally, tourist typologies were obtained by conducting in - depth interviews with 30 border crossers from each border area. The findings highlight the diverse types of cross-border tourism in Indonesia, shaped by spatial, political, historical, and ethnic factors. While these types align with global studies, they also possess unique characteristics specific to Indonesia. Notably, the research shows that borders serve as gateways rather than barriers, with tourist attractions often located near border zones. Additionally, diplomatic relations, historical conflicts, and inter-ethnic connections significantly shape cross-border tourist movements. Furthermore, this study identifies the primary spatial typologies of cross-border tourism in Indonesia, including transit-oriented border tourism, destinationbased tourism, and multifunctional tourism regions. The typologies of tourist movement patterns range from single -point visits to more complex destination loops, varying based on the level of integration between border regions. Additionally, the study finds that Indonesia's border tourism areas function as economic and cultural hubs, with market activities, local attractions, and regional mobility playing significant roles in shaping the tourism landscape. This research contributes to the limited literature on cross-border tourism in Southeast Asia. The findings offer valuable insights for policymakers and stakeholders aiming to enhance tourism development, foster regional connectivity, and optimize cross-border opportunities. Future studies should investigate additional border areas and conduct longitudinal analyses to understand tourism dynamics better.

The expansion of multiple tourism sectors continues to face limitations, restricting the full potential for growth. In this context, the factors contributing to declining demand must be investigated since sustainable nature-based tourism plays a significant role in enhancing individual incomes and supporting environmental conservation. Therefore, this research aimed to investigate the factors influencing social media marketing, tourist attractions, experience and destination image regarding the intention to revisit in the context of adventure tourism in Indonesia. Theory of Planned Behavior (TPB) has been increasingly used in tourism research to elucidate the factors influencing revisit intention. A total of 88 respondents who participated in adventure tourism with special revisit intention were included in the study. The analysis method adopted was regression using Stata version 12.0. The results showed that social media marketing and tourist attractions did not affect revisit intention. In contrast, the experience of tourists and destination image positively influenced revisit intention. Improving the overall tourist experience and cultivating a positive destination image were critical strategies for promoting repeat visits and ensuring the sustainable growth of tourism destination. Considering the importance, social media marketing and tourist attractions could be integrated with efforts to enhance satisfaction and perceived value to drive revisitation effectively. Moreover, tourism managers enhanced satisfaction and perceived value to promote repeat visits and achieve sustainable growth competitiveness in a comprehensive strategy.

Providing personalized recommendations for travelers has always been a key research topic in the field of cultural tourism. The existing recommendation algorithms are mainly based on tourists' historical behavior and preferences for recommendations. However, these algorithms are inadequate in recommending complex cultural tourism attractions and activities in Guangxi. In order to provide more accurate personalized travel recommendations, this study proposes an improved weighted association rule algorithm based on the Frequent Pattern (FP) growth algorithm. At the same time, this study also proposes to add anti pop recommendation algorithms in personalized attraction recommendations, so that personalized travel recommendations can better meet user needs. This study uses Wanro collector, crawler technology, and public data to collect user basic data. The data preprocessing methods include three methods: participle, stop use and keyword extraction. The results showed that the proposed scenic spot recommendation algorithm had an accuracy rate of over 95% in personalized scenic spot recommendation, while traditional recommendation algorithms had a recommendation accuracy rate of below 95%. The reverse popularity recommendation algorithm designed in the study can better enhance users' satisfaction with choosing tourist attractions. The anti pop recommendation algorithm designed in this study can effectively improve user satisfaction in selecting tourist attractions. This algorithm not only improves the personalized travel satisfaction of travelers, but also provides guidance for regional tourism industry development planning.

This study aimed to examine the spatial structure of the tourist attraction cooperation network in the Yangtze River Delta, from the perspective of tourist flow. This study conducted spatial and social network analyses of 470 popular tourist attractions in the Yangtze River Delta region of China, accounting for the occurrence and co-occurrence of tourist attraction information in tourist travel notes. The analyzed tourist attractions show an obvious spatial agglomeration effect, including four high-density agglomeration areas and two medium-density agglomeration areas. Degree centrality, closeness centrality, and betweenness centrality were used to examine the tourism function, distribution function, and connection function of nodes in the network; nodes were divided into various types of roles according to their function. There are eight condensed subgroups, but their scales are unbalanced. In these condensed subgroups, several tourist attractions with an intermediate function can be selected as transit and stopover points on tourist routes. This study can contribute to the understanding of tourists’ spatial behavior, clarify the role and status of nodes in the cooperation network of tourist attractions based on tourism flow, and help them to formulate measures for the joint marketing of tourist attractions, and promote the development of tourism in the Yangtze River Delta region.

BACKGROUND AND OBJECTIVES: Batu Katak is a small dream village located on the edge of Gunung Leuser National Park in North Sumatra, Indonesia. Nature tourism is full of natural charm. It serves as an ideal retreat from busy crowds, providing an opportunity to spend a vacation connecting with the natural world. One part of the village is home to a remarkable karst forest, teeming with diversity and characterized by its cave systems and intriguing landscapes, while the famous National Park is situated just across the river. The attraction of Batu Katak nature tourism is trekking, and the uniqueness of the flora and fauna offered, including the giant Rafflesia flower and Amorphophallus found in the area. This study aims to examine the effect of tourist attraction and tourist satisfaction on the revisit intention of tourists in the case of Batu Katak Natural Tourism in Indonesia. METHODS: Tourists who had visited Batu Katak Nature Tourism comprised the study's population. In this investigation, the sample size was 350 people who had just visited the Batu Katak Park nature tourism. Structural equation modeling, a utilized in this study as a multivariate analysis technique, illustrates the concurrent linear relationships between unobserved elements (latent variables) and observation variables (indicators). A componentbased model of structural equations method called partial least squares is implemented. FINDINGS: This investigation discovered that tourist attraction affects the revisit intention positively and significantly. The influence of the tourist attraction was both positive and significant in enhancing satisfaction levels. Tourist satisfaction effect revisit Intention positively and significantly. This analysis also examines the indirect effects of a tourist attraction on revisit intention by tourist satisfaction as mediation. The study revealed that tourist satisfaction plays a mediating role in the relationship between tourist attractions and the likelihood of returning. CONCLUSION: Tourist attractions have made strong effect on tourist satisfaction. Tourist satisfaction has a significant contribution to revisit intention, and tourist attractions have a strong effect on tourist revisit intention. The findings of this study reveal that the presence of tourist attractions is the key factor in establishing a tourist destination. Tourist attractions after visiting a natural tourist area will affect their desire and decision to revisit, and this requires the management's capacity to sustain and improve tourist sites according to tourist needs, desires, satisfaction, and perceptions. The findings also found that tourist attractions affect revisit intention through the satisfaction of tourists. Tourist satisfaction is a crucial factor in motivating individuals to revisit destinations. When tourists are content with their experiences, it amplifies the impact of attractions on their likelihood of returning.

The world’s population and the proportion of it living in cities and urban areas has exploded in recent decades. In the European Union, 62% of the population lives in urban areas and 80% in suburban areas, and these proportions are projected to increase further in the coming decades. It has long been researched and proven that ‘urban greenery’ can play a major role in mitigating the so-called urban heat island effect, and during the COVID-19 pandemic the role of daily recreation has come to the forefront. The combined memorial, recreational, and touristic use of cemeteries can help to ensure their economic management, and thus the long-term preservation of their value. In international tourism the model of managing cemeteries as tourist attractions already exists; however, this is not yet part of conventional practice. In addition to traditional cemetery tourism (e.g., visiting the graves of celebrities or enjoying artistic treasures and values), cemeteries are used as venues for events and sports activities. In Western Europe forest and park cemeteries have been established since the 19th century, and their large green areas and open spaces are a prerequisite for their use as public parks. Thus, the use of cemeteries as public parks is a common if quite specific practice. Our aim with this article is to identify the green space values of Budapest’s cemeteries, in addition to their well-known cultural and architectural significance, as well as to define the potential and means of their involvement in tourism-related activities. Another aim of our study is to raise awareness of green cemeteries within the tourism profession as potentially wider tourist attractions. We consider it important to draw the attention of decision-makers to the significance of the greenspace values when preserving or reusing closed cemeteries. Based on our work, other major cities in Hungary can identify and exploit the touristic and green space potential of their cemeteries.

Adjusting pre-trained artificial intelligence models to domain-specific problems is essential for many business problems. But domain-specific data is often scarce and expensive to collect. Moreover, fine-tuning on small datasets is challenging, as it carries risks of overfitting and catastrophic forgetting. This paper systematically investigates the effectiveness of fine-tuning pre-trained image-to-text models for domain-specific applications, emphasizing how model performance scales with dataset size. We compare two state-of-the-art architectures, Generative Image-to-Text (GIT) and Florence-2, using small and large datasets of Liechtenstein tourism attractions. Our analysis reveals a nuanced relationship between model architecture and data efficiency. On the small dataset, measured by BLEU score, GIT outperformed Florence-2 (0.71 vs 0.03). However, with the larger dataset, Florence-2 surpassed GIT by 33–37%. Similarly, CIDEr scores improved dramatically from 0.00 to 0.97 for GIT and from 0.33 to 0.95 for Florence-2, underscoring the critical importance of data volume. Our results suggest that fine-tuned models are capable of generating contextually accurate captions, capturing architectural details, historical context, and geographical information of tourist attractions, as well as potentially benefiting other domains like cultural heritage preservation and education. Our methodology emphasizes computational efficiency, requiring less than 3 GB of GPU memory for both GIT and Florence-2, making these approaches accessible to organizations with limited resources. This research contributes both theoretical insights into model scaling properties and practical guidance on selecting appropriate architectures based on available data resources. The results demonstrate that while fine-tuning can enable reasonable performance even with limited domain-specific data, architecture selection should be informed by anticipated data availability. Furthermore, evaluating multiple models is highly recommended.

This study aims to explore tourists’ sentiment tendencies and focal points by analyzing online reviews of 5A-level tourist attractions. After conducting data cleaning, word segmentation, stop-word filtering, and part-of-speech tagging, we preprocessed the review texts and utilized the ROSTCM6 software for sentiment analysis. The study found that most tourists hold a positive attitude toward their experiences at 5A-level attractions, though there remains room for improvement in certain facilities and services. This research provides valuable feedback for attraction managers to enhance the visitor experience.

This study aimed to evaluate the tourism potential of Kroczyce municipality, Poland, with a focus on its natural attractions and their role in fostering sustainable tourism. Kroczyce, chosen for its pristine natural features, presents an ideal case for examining the integration of sustainable practices in tourism. The research confirmed that the municipality’s natural landscape, rock formations, water reservoirs, protected areas, and caves are significant tourist attractions and sustainable assets. Additionally, it revealed a strong awareness among residents and visitors of the importance of sustainable tourism. The study’s findings suggest that local authorities can effectively develop Kroczyce as a sustainable tourism destination. This development involves investing in eco-friendly infrastructure, promoting conservation, and engaging the local community, ensuring tourism aligns with environmental conservation. Such an approach not only enhances the tourist experience but also preserves the natural ecosystem for future generations. Furthermore, transitioning Kroczyce into a nature tourism hub could yield substantial socio-economic benefits. The expected increase in tourism can stimulate job creation, boost local trade, and improve amenities, contributing positively to the region’s overall development. This case study provides a roadmap for similar municipalities aiming to develop sustainable, nature-based tourism.

This study designed a tour-route-planning and recommendation algorithm that was based on an improved AGNES spatial clustering and space-time deduction model. First, the improved AGNES tourist attraction spatial clustering algorithm was created. Based on the features and spatial attributes, city tourist attraction clusters were formed, in which the tourist attractions with a high degree of correlation among attributes were gathered into the same cluster. It formed the precondition for searching tourist attractions that would match tourist interests. Using tourist attraction clusters, this study also developed a tourist attraction reachability model that was based on tourist-interest data and geospatial relationships to confirm each tourist attraction’s degree of correlation to tourist interests. A dynamic space-time deduction algorithm that was based on travel time and cost allowances was designed in which the transportation mode, time, and costs were set as the key factors. To verify the proposed algorithm, two control algorithms were chosen and tested against the proposed algorithm. Our results showed that the proposed algorithm had better results for tour-route planning under different transportation modes as compared to the controls. The proposed algorithm not only considered time and cost allowances, but it also considered the shortest traveling distance between tourist attractions. Therefore, the tourist attractions and tour routes that were suggested not only met tourist interests, but they also conformed to the constraint conditions and lowered the overall total costs.