MAPPING OF DOMESTIC AND FOREIGN TOURIST VISITS IN EAST JAVA USING THE DBSCAN METHOD

Authors

  • Marita Qori'atunnadyah Institut Teknologi dan Bisnis Widya Gama Lumajang

DOI:

https://doi.org/10.33480/pilar.v21i1.6073

Keywords:

clustering, DBSCAN, domestic, foreign, tourism

Abstract

Tourism is important in economic growth and regional development, especially in East Java Province with diverse tourist attractions. However, the mapping of domestic and foreign tourist visit patterns in this province is still limited. For this reason, this study uses the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method which can group density-based data without determining the number of clusters from the beginning and handle noise. The study aims to map districts/cities in East Java based on the number of tourist visits from 2018 to 2022, using visit data from the East Java Provincial Culture and Tourism Office. The analysis results show that in domestic tourist data, with parameters MinPts = 3 and ε = 1.00, one main cluster is formed consisting of 31 tourist locations and 7 noisy locations. In foreign tourist data, with ε = 0.6 and MinPts = 3, there is one cluster with 30 tourist locations and 8 other locations are categorized as noisy. Noisy locations tend to have higher visits but do not fit into the main cluster. These findings provide important insights for more targeted tourism promotion strategies and efficient resource allocation in East Java.

Downloads

Download data is not yet available.

References

Armiady, D. (2022). Analisis Metode DBSCAN (Density-Based Spatial Clustering of Application with Noise) dalam Mendeteksi Data Outlier. JURIKOM (Jurnal Riset Komputer), 9(6), 2158. https://doi.org/10.30865/jurikom.v9i6.5080

Badan Pusat Statistik. (2023). Statistik Pariwisata Provinsi Jawa Timur 2022. https://jatim.bps.go.id/id/publication/2023/07/03/5bab263b9158357b69bc9309/statistik-pariwisata-provinsi-jawa-timur-2022.html

Batool, F., & Hennig, C. (2021). Clustering with the Average Silhouette Width. Computational Statistics & Data Analysis, 158, 107190. https://doi.org/10.1016/j.csda.2021.107190

Giordani, P., Ferraro, M. B., & Martella, F. (2020). An Introduction to Clustering with R (Vol. 1). Springer Singapore. https://doi.org/10.1007/978-981-13-0553-5

Kristianto, A. (2022). Implementasi DBSCAN dalam Clustering Data Minat Mahasiswa Setelah Pandemi Covid19. KONSTELASI: Konvergensi Teknologi Dan Sistem Informasi, 2(2). https://doi.org/10.24002/konstelasi.v2i2.5638

Monalisa, S., Juniarti, Y., Saputra, E., Muttakin, F., & Ahsyar, T. K. (2023). Customer segmentation with RFM models and demographic variable using DBSCAN algorithm. TELKOMNIKA (Telecommunication Computing Electronics and Control), 21(4), 742. https://doi.org/10.12928/telkomnika.v21i4.22759

Qori’atunnadyah, M. (2022). Pengelompokkan Wilayah Berdasarkan Rasio Guru-Murid Pada Jenjang Pendidikan Menggunakan Algoritma K-Means. Journal of Informatics Development, 1(2), 33–38.

Qori’atunnadyah, M. (2023a). Fuzzy C-Means for Regional Clustering in East Java Province Based on Human Development Index Indicators. J Statistika, 16(2), 524.

Qori’atunnadyah, M. (2023b). Metode C-Means untuk Pengelompokkan Kabupaten/Kota Provinsi Jawa Timur berdasarkan Indikator Indeks Pembangunan Manusia (IPM). Journal of Informatics Development, 1(2), 51–58. https://doi.org/10.30741/jid.v2i2.1013

Qori’atunnadyah, M. (2024). Mapping Domestic and Foreign Tourists in East Java Using C-Means Clustering. Jurnal Statistika Dan Aplikasinya, 8(1), 54–62. https://doi.org/10.21009/JSA.08105

Qori’atunnadyah, M., Liyundira, F. S., & Indrianasari, N. T. (2023). Grouping Manufacturing Companies Based on Factors Affecting Firm Value Using C-Means Clustering (pp. 28–31). https://doi.org/10.2991/978-94-6463-346-7_6

Rahman, R. R. A., & Wijayanto, A. W. (2021). PENGELOMPOKAN DATA GEMPA BUMI MENGGUNAKAN ALGORITMA DBSCAN. Jurnal Meteorologi Dan Geofisika, 22(1), 31. https://doi.org/10.31172/jmg.v22i1.738

Riyono, J., Pujiastuti, C. E., Latifa, A., & Putri, R. (2024). CLUSTERING NEGARA BERDASARKAN SKOR PENGENDALIAN KONSUMSI TEMBAKAU MENGGUNAKAN ALGORITMA DBSCAN. Jurnal Teknik Informatika Kaputama (JTIK), 8(1).

Sabor, K., Jougnot, D., Guerin, R., Steck, B., Henault, J.-M., Apffel, L., & Vautrin, D. (2021). A data mining approach for improved interpretation of ERT inverted sections using the DBSCAN clustering algorithm. Geophysical Journal International, 225(2), 1304–1318. https://doi.org/10.1093/gji/ggab023

Saputri, F. W., & Arianto, D. B. (2023). PERBANDINGAN PERFORMA ALGORITMA K-MEANS, K-MEDOIDS, DAN DBSCAN DALAM PENGGEROMBOLAN PROVINSI DI INDONESIA BERDASARKAN INDIKATOR KESEJAHTERAAN MASYARAKAT. Jurnal Teknologi Informasi: Jurnal Keilmuan Dan Aplikasi Bidang Teknik Informatika, 7(2), 138–151. https://doi.org/10.47111/jti.v7i2.9558

Syafrianto, A., & Riswanto, E. (2023). Pengelompokkan Jumlah Kunjungan Mahasiswa ke Perpustakaan Kampus Menggunakan Algoritma DBSCAN. G-Tech: Jurnal Teknologi Terapan, 7(1), 75–81. https://doi.org/10.33379/gtech.v7i1.1925

Downloads

Published

2025-03-14

How to Cite

Qori’atunnadyah, M. (2025). MAPPING OF DOMESTIC AND FOREIGN TOURIST VISITS IN EAST JAVA USING THE DBSCAN METHOD. Jurnal Pilar Nusa Mandiri, 21(1), 9–15. https://doi.org/10.33480/pilar.v21i1.6073