Monica, Stephanie and Natalia, Friska and Sudirman, Sud (2019) Clustering Tourism Object in Bali Province Using K-Means and X-Means Clustering Algorithm. In: IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems, 28-30 June 2018, Exeter, UK.
Full text not available from this repository.Abstract
Tourism is an important element in Indonesian economy as it brings consistent flow of foreign currencies as well as supporting local industries. One way for the Indonesian government to improve this industry is to identify areas and localities which requires more attention and investment. This paper presents our finding from analysing the large amount of data that the Indonesian Government Tourism Office, specifically regarding tourism in Bali. We use K-Means and X-Means algorithms to cluster the various type of tourist attractions in Bali according to their popularity and Power BI to develop the interactive dashboard. The visualisation of the results is subsequently generated for non-technical persons to be able to understand. The output of this research should feed into the decision making process taken by the Bali Provincial Government in order to improve the number of visitors to the numerous tourist attractions spread throughout the region.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Keywords: | Indexes , Clustering algorithms , Data mining , Government , Data preprocessing , Knowledge discovery |
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 001 Knowledge 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods 900 History and Geography > 910 Geography and Travel > 910 Geography and Travel, Tourism |
| Divisions: | Faculty of Engineering & Informatics > Information System |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 23 Nov 2021 05:03 |
| Last Modified: | 23 Nov 2021 05:03 |
| URI: | https://kc.umn.ac.id/id/eprint/19250 |
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