Smart City Evaluation Model in Bandung, West Java, Indonesia

Nuraeni, Aisyah; Firmansyah, Hendra Sandhi; Pribadi, Ganjar Setya; Munandar, Ahmad; Herdiani, Leni; Nurwathi,

doi:10.1109/tssa48701.2019.8985465

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…This is because some researchers agree that branding is an economic subindicator, but some researchers claim that branding is a separate indicator because entrepreneurship, the employment market, industrial economies, and financial transactions are the focus of the smart economy, while branding is a way of doing so. It has marketing areas such as tourism and culture [16]. Although smart society is one of the most widely used indicators of smart city measurement, smart society is one of the lowest score indicators with a value of <10%, especially in relation to education [25][26].…”

Section: Results Datamentioning

confidence: 99%

Smart City: The main assist factor for smart cities Analysis: a systematic review

Ningrum¹

2024

IJIES

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Initially, A smart city was originally a solution used to deal with the environmental crisis that took place in the 20th century. Smart city concepts derive from the use of technology and knowledge to enhance society's efficiency and competitiveness. The indicators are needed that support the achievement of a smart city. We will analyze the indicators that can impact the smart city achievement in this article. The aim of this study is to identify the indicators that influence the process of developing a smart city in order to be able to help other cities to establish sustainable policies and work plans so that they can prepare themselves for a smart city consistently. From the results of content analysis and descriptions of literature reviews, it is concluded that the indicators most used in the assessment of smart cities are divided into eight groups of indicators, including governance, economy, living, mobility, environment, people, branding, and demography.

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Section: Results Datamentioning

confidence: 99%

Smart City: The main assist factor for smart cities Analysis: a systematic review

Ningrum¹

2024

IJIES

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Development of Buffer Zone into Living Lab - Garuda Smart City Framework: Systematic Review

Emmanuel,

Supangkat,

Gaol

et al. 2023

2023 10th International Conference on ICT for Smart Society (ICISS)

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Implementation of Digital Geotwin-Based Mobile Crowdsensing to Support Monitoring System in Smart City

2023

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According to the UN (United Nations) data released in 2018, the growth in the world’s population in urban areas is increasing every year. This encourages changes in cities that are increasingly dynamic in infrastructure development, which has an impact on social, economic, and environmental conditions. On the other hand, this also raises the potential for new problems in urban areas. To overcome potential problems that occur in urban areas, a smart, effective, and efficient urban monitoring system is needed. One solution that can be implemented is the Smart City concept which utilizes sensor technology, IoT, and Cloud Computing to monitor and obtain data on problems that occur in cities in real time. However, installing sensors and IoT throughout the city will take a long time and be relatively expensive. Therefore, in this study, it is proposed that the Mobile Crowdsensing (MCS) method is implemented to retrieve and collect data on problems that occur in urban areas from citizen reports using their mobile devices. MCS implementation in collecting data from the field is relatively inexpensive and does not take long because all data and information are sent from citizens or the community. The data and information that has been collected from the community are then integrated and visualized using the Digital Geotwin-based platform. Compared to other platforms, which are mostly still based on text and GIS in 2D, the advantage of Digital Geotwin is being able to represent and simulate real urban conditions in the physical world into a virtual world in 3D. Furthermore, the use of the Digital Geotwin-based platform is expected to improve the quality of planning and policy making for stakeholders. This research study aims to implement the MCS method in retrieving and collecting data in the form of objects and problem events from the field, which are then integrated into the Digital Geotwin-based platform. Data collected from MCS are coordinate data and images of problem objects. These are the contributions of this research study: the first is to increase the accuracy in determining the coordinates of a distant object by adding a parameter in the form of the approximate coordinates of the object. Second, 3D visualization of the problem object using image data obtained through the MCS method and then integrating it into the Digital Geotwin-based platform. The results of the research study show a fairly good increase in accuracy for determining the coordinates of distant objects. Evaluation results from the visualization of problem objects in 3D have also proven to increase public understanding and satisfaction in capturing information.

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Smart City Evaluation Model in Bandung, West Java, Indonesia

Cited by 3 publications

References 4 publications

Smart City: The main assist factor for smart cities Analysis: a systematic review

Smart City: The main assist factor for smart cities Analysis: a systematic review

Development of Buffer Zone into Living Lab - Garuda Smart City Framework: Systematic Review

Implementation of Digital Geotwin-Based Mobile Crowdsensing to Support Monitoring System in Smart City

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