Article Sidebar

Submitted
October 17, 2021
Accepted
December 4, 2021
Published
March 7, 2022
Download
PDF Untitled
Statistic
Read Counter : 684 Download : 525

Main Article Content

Abstract

Bali is one of the hearts of tourism in Indonesia. The existence of the Covid-19 pandemic has made this tourist paradise also affected the wheels of the economy. Based on this, this study aims to determine the density clustering of one of the economic supporters in Bali, namely hospitality. The study began with the quadrant method and Ripley's K-Function to measure the distribution pattern of hospitality. From the results of the two methods, the distribution pattern of hotels in Bali is more towards clusters than random or regular distribution. If the point distribution pattern is more towards the cluster, it is continued with the Density-Based Spatial Clustering of Application Noise (DBSCAN) algorithm to form spatial clustering. In the DBSCAN algorithm, a combination of parameters, namely minimum points (MinPts) and epsilon (Eps), is carried out with evaluation using the silhouette average width value. From the results of the DBSCAN algorithm, the clustering results show that the distribution of hotels in Bali forms clusters and tends to approach the surrounding tourist attractions, such as near the beach, city market, and mountainous areas. It can help policymakers if they want to prioritize economic recovery after the Covid-19 pandemic.

Keywords

Quadrant Method Ripley's K-Function Density-Based Spatial Clustering of Application Noise (DBSCAN) Silhoutte Average Width.

Article Details

How to Cite
Fauzan, A., Novianti, A., Ramadhani, R. R. M. A., & Adhiwibawa, M. A. S. (2022). Analysis of Hotels Spatial Clustering in Bali: Density-Based Spatial Clustering of Application Noise (DBSCAN) Algorithm Approach. EKSAKTA: Journal of Sciences and Data Analysis, 3(1), 25–38. https://doi.org/10.20885/EKSAKTA.vol3.iss1.art4
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. C. M. Reinhart, This time trully is different, 2020. [Online]. Available: https://www.project-syndicate.org/commentary/covid19-crisis-has-no-economic-precedent-by-carmen-reinhart-2020-03?barrier=accesspaylog [Accessed 1 October 2021].
  2. M. Hotopf, E. Bullmore, R. C. O’Connor, and E. A. Holmes, The scope of mental health research during the COVID-19 pandemic and its aftermath, Br. J. Psychiatry, 217 (4) (2020) 540–542.
  3. E. A. Holmes et al., Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science, The Lancet Psychiatry, 7 (6) (2020) 547–560.
  4. I. N. Subadra and H. Hughes, Pandemic in paradise: Tourism pauses in Bali, Tour. Hosp. Res 2021.
  5. L. S. Wedaningsih, N. U. Vipriyanti, W. Maba, and I. G. Y. Partama, Mapping the employee layoff of star hotels in Denpasar City: an effort to reduce the impacts of the Covid-19 pandemic, SOSHUM J. Sos. dan Hum., 11 (1) (2021) 100–111.
  6. Bali Statistics Agency, Tingkat Penghunian Kamar (TPK) Hotel Bintang Menurut Kelas di Provinsi Bali (Persen), 2021, [Online]. Available: https://bali.bps.go.id/indicator/16/230/1/tingkat-penghunian-kamar-tpk-hotel-bintang-menurut-kelas-di-provinsi-bali.html [Accessed 1 October 2021].
  7. World Health Organization (WHO), WHO Coronavirus (COVID-19) Dashboard, 2021. [Online]. Available: https://covid19.who.int/table?tableDay=yesterday [Accessed 1 October 2021].
  8. K. Mumtaz, M. Studies, and T. Nadu, An analysis on Density Based Clustering of multi dimensional spatial data, Indian J. Comput. Sci. Eng., 1 (1) (2010) 8–12.
  9. J. Sander, Density-Based Clustering, in Encyclopedia of Machine Learning, C. Sammut and G. I. Webb, Eds. Boston, MA: Springer US (2010) 270–273.
  10. J. Sander, M. Ester, H. P. Kriegel, and X. Xu, Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications, Data Min. Knowl. Discov. 2 (2) (1998) 169–194.
  11. B. N. Sari and A. Primajaya, Penerapan clustering DBSCAN untuk pertanian padi di kabupaten Karawang, J. Inform. dan Komput 4 (1) (2019) 28–34.
  12. T. D. Harjanto, A. Vatresia, and R. Faurina, Analisis penetapan skala prioritas penanganan balita stunting menggunakan metode DBSCAN clustering, J. Rekursif, 9 (1) (2021) 30–42.
  13. X. Han, C. Armenakis, and M. Jadidi, DBSCAN optimization for improving marine trajectory clustering and anomaly detection, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch 43 (B4) (2020) 455–461.
  14. K. Sheridan, T. G. Puranik, E. Mangortey, O. J. Pinon, M. Kirby, and D. N. Mavris, An application of DBSCAN clustering for flight anomaly detection during the approach phase, AIAA Scitech 2020 Forum, 1 (PartF) (2020) 1–20.
  15. R. Scitovski and K. Sabo, DBSCAN-like clustering method for various data densities, Pattern Anal. Appl 23 (2) (2020) 541–554.
  16. T. Fan, N. Guo, and Y. Ren, Consumer clusters detection with geo-tagged social network data using DBSCAN algorithm: a case study of the Pearl River Delta in China, GeoJournal 86 (1) (2021) 317–337.
  17. D. P. Isnarwaty and Irhamah, Text clustering pada akun twitter layanan ekspedisi JNE , J&T, dan Pos Indonesia menggunakan metode Density-Based Spatial Clustering of Applications with Noise ( DBSCAN ), J. Sains dan Seni 8 (2) (2019) 2–9.
  18. M. P. M, C. Dewi, E. P. Siam, G. A. Wijayanti, N. Aulia, and R. Nooraerni, Comparison of DBSCAN and K-Means clustering for grouping the village status in Central Java 2020, J. Mat. Stat. dan Komputasi 17 (3) (2021) 394-404.
  19. R. Adha, N. Nurhaliza, U. Soleha, P. Studi, S. Informasi, and F. Sains, Perbandingan algoritma DBSCAN dan K-Means clustering untuk pengelompokan kasus Covid-19 di dunia, J. Sains, Teknol. dan Ind 18 (2) (2021) 206–211.
  20. Mustakim, M. Z. Fauzi, Mustafa, A. Abdullah, and Rohayati, Clustering of Public Opinion on Natural Disasters in Indonesia Using DBSCAN and K-Medoids Algorithms, J. Phys. Conf. Ser 1783 (1) (2021) 1-6.
  21. Z. Han, M. Cheng, F. Chen, Y. Wang, and Z. Deng, A spatial load forecasting method based on DBSCAN clustering and NAR neural network, J. Phys. Conf. Ser 1449 (1) (2020) 1-6.
  22. I. P. Hartawan, Pola persebaran hotel resort di kawasan pariwisata Ubud, Jurnal Anala 9 (2) (2021)1–22.
  23. T. O. Barnad, I. G. A. A. R. Asmiwyati, and N. N. A. Mayadewi, Pola ruang sebaran objek dan fasilitas penunjang wisata berbasis sistem informasi geografis di kawasan Taman Nasional Bali Barat, J. Arsit. Lansek 7 (1) (2021) 66-75.
  24. S. Setyaningsih and A. Alam, Impact of Covid-19 pandemic on sharia hotels and their handling strategies (a case in Indonesia), International Conference on Islamic Economics, Islamic Finance, & Islamic Law (ICIEIFIL) (2021) 26–54.
  25. The Environmental Data Standards Council (EDSC), Latitude/ Longitude data standard 2006.
  26. Z. S. Abdallah, L. Du, and G. I. Webb, Data preparation, in Encyclopedia of machine learning and data mining, Second Edi., New York: Springer References (2017) 318–325.
  27. M. Komorowski, D. C. Marshall, J. D. Salciccioli, and Y. Crutain, Exploratory data analysis, in MIT Critical Data: Secondary Analysis of Electronic Health Records, 2nd ed., USA: Springer Open, (2016) 185–203.
  28. M. N. Aidi, Konfigurasi Titik dalam Ruang. Bogor: Institut Pertanian Bogor 2013.
  29. H. Schabenberger, Spatial count regression Repository.John Wiley & Sons.CRAN, 2009.
  30. E. Marcon, S. Traissac, and G. Lang, A statistical test for Ripley’s K function rejection of poisson null hypothesis, ISRN Ecol 2013 (March) 1–9, 2013.
  31. A. Dennett, Analysing spatial patterns III: point pattern analysis, in Geocomputation 2020-2021 Work Book, University College London 2021.
  32. J. A. Lentz, Developing a Geospatial Protocol for Coral Epizootiology (2012) 99-101.
  33. M. T. Furqon and L. Muflikhah, Clustering the potential risk of Tsunami using Density-Based Spatial Clustering of Application with Noise (DBSCAN), J. Enviromental Eng. Sustain. Technol 3 (1) (2016) 1–8.
  34. P. B. Nagpal and P. A. Mann, Comparative study of density-based clustering algorithms, Int. J. Comput. Appl 27 (11) (2011) 44–47.
  35. I. M. S. Putra, Algoritma DBSCAN (Density-Based Spatial Clustering of Application with Noise) dan contoh perhitungannya (2018) 1-37.
  36. M. Ester, H. P. Kriege, J. Sander, and X. Xu, A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, Compr. Chemom (1996) 226–231.
  37. H. Cui, W. Wu, Z. Zhang, F. Han, and Z. Liu, Clustering and application of grain temperature statistical parameters based on the DBSCAN algorithm, J. Stored Prod. Res 93 (April) (2021) 101819.
  38. Q. Ye, W. Gao, and W. Zeng, Color image segmentation using Density-Based Clustering, in IEE International Conference on Acoustics, Speech, & Signal Processing (2003) 401-404.
  39. S. Prabakaran, M. Wayland, and C. Penfold, An introduction to machine learning 2017.
  40. P. J. Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math 20 (C) (1987) 53–65.
  41. J. M. Flenniken, S. Stuglik, and B. V. Iannone, Quantum GIS (QGIS): an in