Data-Driven Insights for Tourism Development in East Java Using Directed Graphs

This study aims to analyze the tourism network in East Java using Google Trends data and directed graph models. Data were collected based on search queries combining the keyword "wisata" (tourism) with city or regency names in East Java for the year 2023. The analysis employed the PageRank algorithm to identify key network hubs and the Adamic-Adar index to predict new connections between regions. Spatial visualization was conducted using QGIS, while network analysis was carried out using NetworkX. The results revealed that Malang and Surabaya act as central hubs in the tourism network, with high connectivity to other regions. Meanwhile, regions such as Pacitan were identified as isolated nodes within the network. Based on these findings, the study recommends strategies to enhance inter-regional connectivity, including infrastructure development and integrated tourism promotion. This study provides data-driven insights that can assist tourism authorities in improving the attractiveness and sustainability of the tourism sector in East Java.

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Publication Details

Journal
Riemann Research of Mathematics and Mathematics Education
Published
2025-02-07
DOI
https://doi.org/10.38114/reimann.v7i1.97
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Data-Driven Insights for Tourism Development in East Java Using Directed Graphs

Badara Shofi Dana, Galih Pradananta
Riemann Research of Mathematics and Mathematics Education
Complex Network Analysis Techniques
article

Data-Driven Insights for Tourism Development in East Java Using Directed Graphs

Badara Shofi Dana, Galih Pradananta
article en

Abstract

This study aims to analyze the tourism network in East Java using Google Trends data and directed graph models. Data were collected based on search queries combining the keyword "wisata" (tourism) with city or regency names in East Java for the year 2023. The analysis employed the PageRank algorithm to identify key network hubs and the Adamic-Adar index to predict new connections between regions. Spatial visualization was conducted using QGIS, while network analysis was carried out using NetworkX. The results revealed that Malang and Surabaya act as central hubs in the tourism network, with high connectivity to other regions. Meanwhile, regions such as Pacitan were identified as isolated nodes within the network. Based on these findings, the study recommends strategies to enhance inter-regional connectivity, including infrastructure development and integrated tourism promotion. This study provides data-driven insights that can assist tourism authorities in improving the attractiveness and sustainability of the tourism sector in East Java.

Riemann Research of Mathematics and Mathematics EducationVol. 7(1)
Universitas Tulungagung (ID), Universitas Islam Negeri Sayyid Ali Rahmatullah Tulungagung (ID)
Openalex Percentile: Top 94%
Complex Network Analysis Techniques
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