| dc.contributor.author |
Supapakorn T. |
|
| dc.contributor.author |
Intarapak S. |
|
| dc.contributor.author |
Vuthipongse W. |
|
| dc.date.accessioned |
2022-12-14T03:17:34Z |
|
| dc.date.available |
2022-12-14T03:17:34Z |
|
| dc.date.issued |
2022 |
|
| dc.identifier.issn |
18140424 |
|
| dc.identifier.uri |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123528361&partnerID=40&md5=427541f9cb49108d363e57f863d90da0 |
|
| dc.identifier.uri |
https://ir.swu.ac.th/jspui/handle/123456789/27531 |
|
| dc.description.abstract |
The objective of this research is to find the influencing variables for classification of foreign tourists’ information in Thailand. The data of 400 foreign tourists were obtained from the Ministry of Tourism and Sports. By using decision tree analysis, the results show that 1) the length of stay can classify tourists by accurately predicting the expenditure per trip accounting for 76.0 % 2) age can categorize tourists with a correct prediction of travel frequency of 63.7 % 3) age, country of residence and travel arrangement can categorize tourists by accurately predicting gender accounting for 63.2 % 4) the length of stay and travel arrangement can classify tourists with 61.8% accurate predictions of the country of residence. © 2022. All Rights Reserved. |
|
| dc.language |
en |
|
| dc.title |
A Decision Tree for Information of Foreign Tourists Traveling to Thailand |
|
| dc.type |
Article |
|
| dc.rights.holder |
Scopus |
|
| dc.identifier.bibliograpycitation |
International Journal of Mathematics and Computer Science. Vol 17, No.1 (2022), p.195-206 |
|