Published: 2025-08-13

Cluster of Foreign Tourists Using the K-Means Method Based on Arrival Data at I Gusti Ngurah Rai Airport (2018-2024)

DOI: 10.35870/ijmsit.v5i2.5208

Fahrur Rozy, Adika Setia Brata, Windy Lestari, Suci Rahmawati
  • Fahrur Rozy: Institut Sains dan Teknologi Nahdlatul Ulama Bali
  • Adika Setia Brata: Institut Sains dan Teknologi Nahdlatul Ulama Bali
  • Windy Lestari: Institut Sains dan Teknologi Nahdlatul Ulama Bali
  • Suci Rahmawati: Institut Sains dan Teknologi Nahdlatul Ulama Bali
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Abstract

Tourism is a vital sector in the economic development of Bali Province, with the number of international tourist arrivals through I Gusti Ngurah Rai International Airport as the main indicator. According to data from the Central Statistics Agency (BPS), the number of international tourists reached its peak in 2019 with over 16.1 million visits. However, in 2020–2021, there was a drastic decline due to the COVID-19 pandemic. In 2022, the number of international tourist arrivals began to show a significant recovery. In 2023, the number of international tourist arrivals to Indonesia reached 11.68 million, most of whom entered through major airports such as I Gusti Ngurah Rai Airport (Bali). The inaccuracy of the tourist arrival grouping process has had a negative impact on business growth and tourism revenue. This study aims to classify the countries of origin of international tourists based on the number of visits using the K-Means Clustering method, an effective unsupervised data mining algorithm for data segmentation. Data was obtained from the Central Statistics Agency (BPS), covering the number of tourist arrivals and their countries of origin from 2018 to 2024. The analysis process involves determining the optimal number of clusters using the Elbow Method, followed by grouping countries based on visit characteristics. The results of the study indicate that tourist countries of origin can be grouped into three main clusters: high-visit countries (Australia and China), medium-visit countries, and low-visit countries. This study provides strategic insights for the government and tourism industry stakeholders in developing more targeted policies, such as improving transportation infrastructure and implementing more effective marketing strategies to maintain and increase the number of tourist visits to Bali.

Keywords

K-Means ; International Tourists ; I Gusti Ngurah Rai Airport ; Data Mining ; Clustering

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How to Cite

Rozy, F., Setia Brata, A., Lestari, W., & Rahmawati, S. (2025). Cluster of Foreign Tourists Using the K-Means Method Based on Arrival Data at I Gusti Ngurah Rai Airport (2018-2024). International Journal of Management Science and Information Technology, 5(2), 362-370. https://doi.org/10.35870/ijmsit.v5i2.5208
Article Information

This article has been peer-reviewed and published in the International Journal of Management Science and Information Technology. The content is available under the terms of the Creative Commons Attribution 4.0 International License.

  • Issue: Vol. 5 No. 2 (2025)

  • Section: Articles

  • Published: %750 %e, %2025

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Author Biographies
Fahrur Rozy
Google Scholar ResearchGate ORCID

Department of Statistics, Institut Sains dan Teknologi Nahdlatul Ulama Bali, Denpasar City, Bali Province, Indonesia

Adika Setia Brata
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Department of Statistics, Institut Sains dan Teknologi Nahdlatul Ulama Bali, Denpasar City, Bali Province, Indonesia

Windy Lestari
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Department of Statistics, Institut Sains dan Teknologi Nahdlatul Ulama Bali, Denpasar City, Bali Province, Indonesia

Suci Rahmawati
Google Scholar ResearchGate ORCID

Department of System Information, Institut Sains dan Teknologi Nahdlatul Ulama Bali, Denpasar City, Bali Province, Indonesia

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