KLASIFIKASI DATA TIME SERIES ARUS LALU LINTAS JANGKA PENDEK MENGGUNAKAN ALGORITMA ADABOOST DENGAN RANDOM FOREST - Ahmad Rofiqul Muslikh; Heru Agus Santoso; Aris Marjuni (Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro, 2019)
Data traffic in Indonesia is used for management control traffic flow, while the data on get results from the survey will be undertaken directly localized, the survey will be undertaken are less effective, and the data obtained from the survey results were used as a reference in control traffic flow, and therefore to obtain the data traffic flow more effective in need of a new approach that can classified and predict the data in the can with higher accuracy, so that density and congestion can be predicted earlier. In this study used the approach of using Adaboost and Random Forest algorithms to classification and predict the survey data that are time series, the results of testing for prediction using Adaboost with Random Forest With Confusion Matrix as a measuring accuracy rate of 87,8%, and the rate of error in getting at 0 , 0629. On the results using Adaboost with a Random Forest approach proved to be more efficient in predicting the survey data rather than simply relying on the original data to predict traffic flow