Indra Gunawan, Wahyu Sakti Gunawan Irianto, Aji Prasetya Wibawa, Triyanna Widyaningtyas, Anusua Ghosh, Tinton Dwi Atmaja
Over time, tourist visit has increased significantly. Data minning has been used to estimate the rate of tourist. Similarly, Support Vector Machine (SVM) has been applied in several studies which proved that it has an accurate estimate value. This study deployed SVM and Multi-Support Vector Machine independently to estimate the tourist visit rate in Indonesia. The results obtained from this study indicate that the data based on the arrival gate point has a fairly low level of correlation between gates and time series data has an influence on processing the number of foreign visits. The average rate of Mean Absolute Persetage Error (MAPE) that resulted from processing data using separately multi SVM was at 0.57 % and integrated multi SVM was at 19.6%. © 2018 IEEE.
Department of Electrical Engineering, State University of Malang, Malang, Indonesia; School of Electrical and Information Engineering, University of South Australia, Adelaide, Australia; Research Centre for Electrical Power and Mechatronics, Indonesian Institute of Sciences, Bandung, Indonesia
