Arif Pribadi, F. Kumiawan, M. Hariadi, S. M. S. Nugroho
{"title":"Urban distribution CCTV for smart city using decision tree methods","authors":"Arif Pribadi, F. Kumiawan, M. Hariadi, S. M. S. Nugroho","doi":"10.1109/ISITIA.2017.8124048","DOIUrl":null,"url":null,"abstract":"Supervision is important in the application of Smart City. Closed-Circuit Television (CCTV) is one of the main tools of Smart City surveillance. Some CCTVs connected with Information and Communication Technology (ICT) form Smart Monitoring. In order to support Smart Mobility, CCTV is installed to monitor road conditions. The problem is the installation of CCTV which is not always appropriate with the location conditions. Consequently situations such as road density, accidents, crime can not be monitored optimally. This research aims to find the relation of type CCTV camera with location placement. Classification technology is used to construct predictive models. Using the Decision Tree algorithm obtained an accuracy of the prediction model is 87.96%.","PeriodicalId":308504,"journal":{"name":"2017 International Seminar on Intelligent Technology and Its Applications (ISITIA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Seminar on Intelligent Technology and Its Applications (ISITIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISITIA.2017.8124048","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
Supervision is important in the application of Smart City. Closed-Circuit Television (CCTV) is one of the main tools of Smart City surveillance. Some CCTVs connected with Information and Communication Technology (ICT) form Smart Monitoring. In order to support Smart Mobility, CCTV is installed to monitor road conditions. The problem is the installation of CCTV which is not always appropriate with the location conditions. Consequently situations such as road density, accidents, crime can not be monitored optimally. This research aims to find the relation of type CCTV camera with location placement. Classification technology is used to construct predictive models. Using the Decision Tree algorithm obtained an accuracy of the prediction model is 87.96%.
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