Smart destinations require a management system that provides convenient real-time use of digital technology in creating, communicating, and delivering value to visitors. Research related to smart island destinations is still limited, let alone those that utilize blockchain technology to create the smartness of the destination. This research is an empirical study that captures the perceptions of stakeholders in the blue economy on smart islands and the use of blockchain technology in order to build smart islands, a destination that consists of many islands that lack connectedness, such as the Seribu Islands in Jakarta, Indonesia. Data were collected using qualitative and quantitative approaches (mixed methods). Qualitative data were collected from scientific journal publications and followed up with VOS viewer analysis, and quantitative data with the questionnaire survey responses from 150 blue economy industry players in the Seribu Islands and structural equation modeling showed that good digital literacy and blue economy management have significant influence on blockchain technology and impact smart islands. This study indicates that, for islands that have challenges in accessibility and connectivity, the presence of blockchain and smart technology is needed to integrate various resources from each stakeholder so that the blue economy in the islands can be developed more effectively and efficiently, while at the same time ensuring the achievement of sustainability.

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Blockchain Technology to Enhance Integrated Blue Economy: A Case Study in Strengthening Sustainable Tourism on Smart Islands

—The challenge to increasing understanding of mathematics lessons for students in madrasah schools makes the learning process require the support of adaptive alternative learning media. In this study, we propose a serious game-based learning media supported by virtual reality and ambient intelligence technology to equip students with adaptive responses to subject matter scenarios. Ambient intelligence works based on recommendations generated by the Multi-Criteria Recommender System (MCRS). In calculating a similarity between users and reference data, MCRS uses cosine-based similarity calculations, and average similarity is used for ranking. We developed this learning media experiment called Math-VR using the Unity game engine. The experimental test results show that MCRS-based ambient intelligence technology can provide an adaptive response to the choice of geometry subject matter recommendations for students according to their pre-test results. The analysis results show that the recommendation system as part of ambient intelligence has the highest accuracy rate of 0.92 when using 80 reference data.

http://thesai.org/Downloads/Volume14No5/Paper_24-Math_VR_Mathematics_Serious_Game_for_Madrasah_Students.pdf

Math-VR: Mathematics Serious Game for Madrasah Students using Combination of Virtual Reality and Ambient Intelligence

Gamification has been increasingly used to foster motivation and engagement in adopting and maintaining a desired behavior. In recent years, the tailoring of gamification, based on the users’ necessities, has gained a central role in research. To this end, we propose an End-User Development (EUD) solution that enables users who are not experts in programming to create and customize gamified systems according to their specific needs and preferences. In particular, our EUD solution focuses on providing users with intuitive verbal primitives for creating Trigger-Action rules, which are the basic building blocks of gamified mechanics. To illustrate the potential of our approach, we provide some examples of how users can adopt our system to create customed gamification strategies. These examples demonstrate the flexibility of our EUD system, highlighting how it can be adapted to a wide range of applications.

Enhance Gamification Design Through End-User Development: a Proposal

Decentralized recommender system for ambient intelligence of tourism destinations serious game using known and unknown rating approach.

Batu City is a place with many types of tourism and had many tourists in 2019. However, there was an imbalance of tourist attractions visited from the total number. Tourists are only fixated on famous tourist spots. Therefore, a recommendation system is needed that can provide recommendations for tourists. In this study, we use the Multi-Criteria Recommender System (MCRS) method based on the rating value between users to obtain recommendations from the system regarding selecting tourist destinations. The authors use the 6 As Tourism Destinations (6AsTD) framework for user assessment criteria in this study. The framework consists of six indicators that assess the success of tourism destinations, including attractions, accessibility, amenities, support services, activities, and available packages. The six components are considered the key to the success of a tourist destination under the marketing approach. This study aimed to obtain a recommendation system for selecting tourist destinations using the multi-criteria concept based on the 6AsTD framework. Based on the experimental results, the proposed method has an accuracy rate of up to 72%.

Duvan Deswantara Putra

Papers

Decentralized recommender system for ambient intelligence of tourism destinations serious game using known and unknown rating approach.

Selecting Tourism Site Using 6 As Tourism Destinations Framework Based Multi-Criteria Recommender System