Foreigner Visits Estimation Based on Multi Support Vector Machine

Autor:	Tinton Dwi Atmaja, Anusua Ghosh, Wahyu Sakti Gunawan Irianto, Aji Prasetya Wibawa, Triyanna Widyaningtyas, Indra Gunawan
Přispěvatelé:	Gunawan, Indra, Irianto, Wahyu Sakti Gunawan, Wibawa, Aji Prasetya, Widyaningtyas, Triyanna, Ghosh, Anusua, Atmaja, Tinton Dwi, International Symposium on Advanced Intelligent Informatics, SAIN 2018 Yogyakarta, Indonesia 29-30 August 2018
Rok vydání:	2018
Předmět:	estimate Correlation Support vector machine Estimation Prediction algorithms Statistical classification Training set Computer science Statistics Visit rate support vector machine (SVM) foreign tourist visits Time series
Zdroj:	2018 International Symposium on Advanced Intelligent Informatics (SAIN).
Popis:	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%. Refereed/Peer-reviewed
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9b80257e03fa0413c589e84266a246e https://doi.org/10.1109/sain.2018.8673369 Zobrazit plný text záznamu