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Information and communication technology's role in developing smart city platforms has allowed cities to grow smarter in recent years. In order to develop a smart city platform, digital twin technology can be implemented to monitor and simulate the city's conditions. Furthermore, it can function as a precise decision-support system. Digital twins can be combined with augmented reality technology to develop a smart city platform. The combination of these two technologies aims to visualize data for monitoring and simulating conditions in a city. The primary concern about the necessity of a digital twin platform in smart cities pertains to creating a robust digital twin-enabled smart city platform that can efficiently monitor urban conditions and provide significant insights for decision-making. Hence, this research aims to develop a smart city platform with digital twins as its foundation. This platform would enable real-time data visualization inside an environment that facilitates clear and effective information communication to users. The smart city platform development method is divided into four layers, namely developing (1) the basic layer that contains basic information about the city; (2) the 3D layer that contains the city's 3D assets; (3) the digital twin layer for real-time data integration; (4) the augmented layer for augmenting the digital twin data. This research also proposes an architecture that will become the basis of the flow for the digital twin platform development. The result of developing the platform is a smart city platform based on a digital twin that can be used to monitor the condition of the city. This platform can be input for users or the community in planning their daily activities and can be decision support to the government in developing the city. [ABSTRACT FROM AUTHOR]

In this study, we propose probabilistic inference model on a hybrid IT value model using Bayesian Network (BN) that represents uncertain relationships between 13 variables of the model. Those variables are performance, market, innovation, IT support, core competence, capabilities, knowledge, human resources, IT development, IT resources, capital, labor, and IT spending. The relationships between variables in the model are determined using probabilistic approach, including the structure, nature, and direction of relationships. We derive a probabilistic graphical model and measure the relationships between variables. The results of this study shows that the probabilistic approach with Bayesian Network can show that capabilities and core competence are the most important variables to produce high performance output. [ABSTRACT FROM AUTHOR]