The Implementation of Random Forest Classification for Identifying Infrastructure Priorities in Smart Cities
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Abstract
The main challenge in developing smart cities lies in the difficulty of determining effective and efficient infrastructure priorities. This study aims to implement classification techniques and to identify infrastructure priorities in smart cities. The research method employed is experimental, using the Random Forest classification algorithm and secondary data from various sources related to urban infrastructure. The study results indicate that the experimental method with classification techniques can identify infrastructure priorities with a high degree of accuracy. Data analysis on population density, economic growth, traffic congestion, and other variables reveals a significant relationship between infrastructure needs and these variables. The experimental model developed with the Random Forest algorithm can predict infrastructure needs with high accuracy, making it a valuable tool for city governments in making more precise decisions. The application of the Random Forest algorithm also demonstrates that the identified infrastructure priorities align with real needs on the ground, ultimately improving the efficiency of smart city management. Therefore, this study makes a tangible contribution to supporting smart city development through a more effective data-driven approach
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