Hierarchical Clustering-Based Geospatial Analysis for a Personalized Tourism Destination Recommender System

Authors

  • Imam Marzuki Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
  • Djarot Hindarto Department of Informatics, Faculty of Information and Communication Technology, Universitas Nasional, Jakarta, Indonesia
  • Afdhol Dzikri Department of Informatics Engineering, Politeknik Negeri Batam, Batam, Indonesia
  • Fardani Annisa Damastuti Department of Multimedia Creative Technology, Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia
  • Yunifa Miftachul Arif Department of Informatics Engineering, Universitas Islam Negeri Maulana Malik Ibrahim, Malang, Indonesia
  • Reza Fuad Rachmadi Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia | Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
  • Mochamad Hariadi Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia | Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
Volume: 15 | Issue: 4 | Pages: 24794-24799 | August 2025 | https://doi.org/10.48084/etasr.11055

Received: 20 March 2025 | Revised: 24 April 2025 and 25 May 2025 | Accepted: 31 May 2025 | Online: 2 August 2025


Corresponding author: Mochamad Hariadi

Abstract

This study applies hierarchical clustering with cosine distance to analyze and visualize tourist travel patterns across various provinces in Indonesia. The methodology includes grouping tourism travel data using hierarchical clustering, assessing cluster quality with the silhouette score, and visualizing the results through dendrograms and geospatial maps. The clustering results reveal distinct travel patterns across regions, which can form more targeted tourism recommendations. The evaluation shows that hierarchical clustering achieved the highest silhouette score of 0.843, demonstrating superior performance compared to the other methods. These findings contribute to the field of tourism management and decision-making by offering data-driven insights for more personalized and effective travel planning.

Keywords:

tourism recommender system, hierarchical clustering, cosine distance, geospatial analysis, silhouette score

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References

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How to Cite

[1]
I. Marzuki, “Hierarchical Clustering-Based Geospatial Analysis for a Personalized Tourism Destination Recommender System”, Eng. Technol. Appl. Sci. Res., vol. 15, no. 4, pp. 24794–24799, Aug. 2025.

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Copyright (c) 2025 Imam Marzuki, Djarot Hindarto, Afdhol Dzikri, Fardani Annisa Damastuti, Yunifa Miftachul Arif, Reza Fuad Rachmadi, Mochamad Hariadi

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