City analytic development for modeling population using data analysis prediction

Smart city is a city development based on technology and information, by the availability of its information and infrastructure integration between the Government and the business components and potential of the area. To provide those information, we need city analytics, which is a device that aimed to process information to become meaningful, and then visually displayed. To support that, needs data mining. Data mining is a set of processes to unearth any information from data collection, and forming knowledge in a particular group to be easy to analyze. The analysis prediction processes of automation data mining that able to discover the factors that lead to a particular result, predicted the most likely outcome, and identified the level of confidence in making predictions. Mining data to analyze and makes it a model then testing it to form results required by the user or policy and decision makers. At this study, the modeling data analyzed population decision using tree methods, which contained some variation among them; there are a method of Classification and Regression Tree (CART), Carts, Bagging and Random Forrest. Analysis in tested methods results Bagging CART provided the best accurateness prediction by accuracy reached 90%, while the others less than 85%.