Issue |
E3S Web of Conf.
Volume 444, 2023
4th International Conference on Agribusiness and Rural Development (IConARD 2023)
|
|
---|---|---|
Article Number | 03007 | |
Number of page(s) | 6 | |
Section | Rural Environment Development | |
DOI | https://doi.org/10.1051/e3sconf/202344403007 | |
Published online | 14 November 2023 |
Cluster Identification of Agrotourism in Banten Province Indonesia
1 Agribusiness Department, University of Sultan Ageng Tirtayasa, 42163 Serang, Banten, Indonesia
2 Agroecotechnology Department, University of Sultan Ageng Tirtayasa, 42163 Serang, Banten, Indonesia
* Corresponding author: budiaji@untirta.ac.id
The post-pandemic era affects the tourism sector. Before the pandemic, agrotourism was growing in Banten Province in line with the development of special economic zones. To regain the competitiveness of agrotourism in Banten Province, characterization among regions is crucial. This study aims to cluster the regions in Banten Province into groups to identify the agrotourism potency. This study employed a descriptive quantitative study by applying k-medoid clustering in the secondary data of Banten in Figures and eliciting information from the Tourism Department of Banten Province. The number of agrotourism sites, the population number, and the number of restaurants were included in the clustering process. The clustering analysis that results in three clusters was validated and visualized via relative and internal criteria. Cluster 1 was the least potential cluster of agrotourism with members of two municipals Tangerang and Tangerang Selatan. Cluster 2 was the most potential agrotourism region which has members of three regions of Pandeglang, Lebak, and Serang and two municipals of Serang and Cilegon. It is characterized by the highest number of agrotourism sites. Cluster 3 was the second potential agrotourism cluster where the regency of Tangerang was the only member possessing the highest population as a potential market.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.