Abstract
This study aims to determine the segmentation of sustainable tourism development planning based on support system facilities in Rembang Regency using statistical analysis methods. By testing the Kaiser-Meyer-Olkin (KMO) test using 20 tourism objects as a sample, the results of the KMO assumption have been fulfilled so that the segmentation analysis of the potential for sustainable tourism objects can use the K-Means Cluster for the selection of alternative segmentation analysis. The results obtained from this research segmentation of tourism potential are divided into 3 groups, namely the Sustainable Development Support segment in cluster 1, which consists of 12 tourism objects, which are tourist attractions whose routes are spread in all directions from the city center. The Popular Local Tourism segment in cluster 2, as many as 4 tourism objects, is a group whose all routes are to the south of the city center. While the last segment of new local tourism potential, as many as 4 tourism objects, is a group of tourist objects whose routes all go south from the city center. From each segmentation group, a sustainable tourism area development planning strategy will be made.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Badan Perencanaan Pembangunan Dearah Kabupaten Rembang (2022) Wisata Rembang [online]. Available: https://bappeda.rembangkab.go.id/tag/wisata-rembang/. Last accesed 26 Aug 2022
García-Melón M, Gómez-Navarro T, Acuña-Dutra S (2012) A combined ANP-delphi approach to evaluate sustainable tourism. Environ Impact Assess Rev 34:41–50. https://doi.org/10.1016/j.eiar.2011.12.001
Hanafi MM, Halim A (2009) Analysis of financial statements. UPP.STIM YKPN, Yogyakarta
Septiningrum L, Rizana AF, Soesanto RP, Rumanti AA (2022) Success factors for elevating indonesian tourism area potential. In: IEEE international conference on industrial engineering and engineering management (IEEM)
Lohmann G, Netto AP (2016) Tourism theory: concepts, models, and systems. Cabi
Lee-Ross D, Lashley C (2010) Entrepreneurship and small business management in the hospitality industry. Routledge
Page SJ (2014) Tourism management. Routledge
Rembang Regency Government (2022) Pemkab Rembang Terus Dorong Desa Kembangkan Potensi Wisata [online], available: https://rembangkab.go.id/berita/pemkab-rembang-terus-dorong-desa-kembangkan-potensi-wisata/. Las accessed: 24 Aug 2022
Hozumi Y, Wang R, Yin C, Wei G (2021) UMAP-assisted K -means clustering of large-scale SARS-CoV-2 mutation datasets. Comput Biol Med 131:1–14. https://doi.org/10.1016/j.compbiomed.2021.104264
Xie H, Zhang L, Lim CP, Yu Y, Liu C, Liu H (2019) Improving K-means clustering with enhanced Firefly Algorithms. Appl Soft Comput J 84:1–22. https://doi.org/10.1016/j.asoc.2019.105763
Sharma V, Bala M (2020) An improved task allocation strategy in cloud using modified K-means clustering technique. Egypt Inf J 21(4):201–208. https://doi.org/10.1016/j.eij.2020.02.001
Tan PN, Steinbach M, Kumar V (2016) Introduction to data mining. Pearson Education India
Patel E, Kushwaha DS (2020) Clustering cloud workloads: K-means vs gaussian mixture model. Proced Comput Sci 171:158–167
Gbadoubissa JEZ, Ari AAA, Gueroui AM (2020) Efficient k-means based clustering scheme for mobile networks cell sites management. J King Saud Univ-Comput Inf Sci 32(9):1063–1070
Sulistyadi Y, Eddyono F, Entas D (2019) Indikator Perencanaan Pengembangan Pariwisata Berkelanjutan. Aura Bandar Lampung
Kotary DK, Nanda SJ (2020) Automatic determination of K in distributed K-means clustering automatic determination of K in distributed K-means. Proced Comput Sci 165(2019):556–564. https://doi.org/10.1016/j.procs.2020.01.050
Naghizadeh A, Metaxas DN (2020) Condensed silhouette: an optimized optimized filtering filtering process process for cluster cluster selection in selection in K-Means. Proced Comput Sci 176:205–214. https://doi.org/10.1016/j.procs.2020.08.022
Clayman CL, Srinivasan SM, Sangwan RS, Clayman CL, Srinivasan SM, Sangwan RS (2020) K-means clustering and principal components analysis of microarray data L1000 components landmark genes K-means clustering and of principal analysis of microarray data of L1000 landmark genes. Proced Comput Sci 168:97–104. https://doi.org/10.1016/j.procs.2020.02.265
Xu H, Croot P, Zhang C (2021) Discovering hidden spatial patterns and their associations with controlling factors for potentially toxic elements in topsoil using hot spot analysis and K-means clustering analysis. Environ Int Col 151:1–14
Guo W, Wang W, Zhao S, Niu Y, Zhang Z, Liu X (2022) Density peak clustering with connectivity estimation. Knowl-Based Syst 243:108501. https://doi.org/10.1016/j.knosys.2022.108501
Adianta A, Ali S, Nugeraha P (2019) Analisis Segmentasi Demografisdan Perilaku pada Bisnis Pariwisata. J Perspekt Bisnis 2(2):10–18
Acknowledgements
This research was supported by Telkom University which assisted in providing the time and opportunity to collect data in Rembang Regency, Central Java, Indonesia. We would also like to thank the Tourism Office and the management of tourist attractions in Rembang Regency who have provided information related to the data needed in the research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Fa’rifah, R.Y., Achmad, F., Septiningrum, L., Wiratmadja, I.I. (2023). Segmentation of Potential Sustainable Tourism Based on Support System Facility Perspective. In: Rosyidi, C.N., Laksono, P.W., Jauhari, W.A., Hisjam, M. (eds) Proceedings of the 6th Asia Pacific Conference on Manufacturing Systems and 4th International Manufacturing Engineering Conference. iMEC-APCOMS 2022. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-1245-2_26
Download citation
DOI: https://doi.org/10.1007/978-981-99-1245-2_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1244-5
Online ISBN: 978-981-99-1245-2
eBook Packages: EngineeringEngineering (R0)