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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic IJoICT (International Journal on Information and Communication Technology) Informatics for Educators and Professional : Journal of Informatics Jurnal Statistika dan Matematika (Statmat) Jurnal Sains Teknologi dalam Pemberdayaan Masyarakat International Journal of Information Technology and Computer Science Applications (IJITCSA) AMMA : Jurnal Pengabdian Masyarakat
Khairunnisa Fadhilla Ramdhania
Universitas Bhayangkara Jakarta Raya
Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Other

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Regional Mapping Based on Tourism Destinations in West Java: K-Medoid Clustering Analysis Almajid, Nafis; Dina Atika, Prima; Fadhilla Ramdhania, Khairunnisa
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1011

Abstract

The growth of the tourism sector in West Java demands an optimal development strategy. This study aims to cluster regions in West Java based on the characteristics of their tourist destinations using the K-Medoid algorithm. This algorithm was chosen because of its superiority in producing optimal clusters and robustness to outliers. Data on tourist destination characteristics were analyzed using the K-Medoid algorithm and the Elbow method to determine the optimal number of clusters. As a result, three clusters with different characteristics were formed. The first cluster, "Medium potential and achievement", consists of 1 region with unoptimized potential for campsite tourism. The second cluster, "High potential and moderate achievement", consists of 25 regions with a diversity of attractions and a high number of visits. The third cluster, "Medium potential and high achievement", consists of 1 region with popular historical and cultural attractions and high visitation. The model evaluation showed a DBI score of 0.08, indicating good clustering quality. This research is expected to provide insights for the government and related stakeholders to formulate targeted tourism development policies in West Java. The K-Medoid algorithm helps identify certain patterns, providing deeper insights into regional differences in terms of tourism.
Comparative Analysis of K-Means and Hierarchical Clustering for Regional Welfare Disparity Identification in West Java Province Muhamad Dani Yusuf; Tb Ai Munandar; Khairunnisa Fadhilla Ramdhania
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 3 (2025): September - December 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i3.213

Abstract

This study aims to cluster regencies/cities in West Java Province based on public welfare indicators using the K-Means Clustering and Hierarchical Clustering methods. The data used includes health, economic, population density, and average length of schooling indicators in 2023. Cluster quality evaluation was performed using the silhouette score. The results show that K-Means Clustering with five clusters yields the highest silhouette score of 0.219. For comparison, Hierarchical Clustering with the Ward Linkage method and eight clusters was chosen, having a silhouette score of 0.202, which is the largest among other Hierarchical Clustering methods. The identification of each cluster's characteristics in K-Means reveals areas with multidimensional challenges (Cluster 1), industrial areas with unemployment issues (Cluster 2), areas with high stunting prevalence despite good access to basic facilities (Cluster 3), densely populated urban areas with good welfare but high unemployment (Cluster 4), and areas with very high health complaints and low welfare (Cluster 5). K-Means clusters (except Cluster 4) tend to have a low average length of schooling, below 12 years. Consistency in cluster patterns was found between K-Means and Ward Linkage, especially in advanced urban areas and areas with multidimensional welfare challenges in southern West Java. These findings are expected to serve as a reference for the government and policymakers in formulating more targeted and effective development strategies.
Co-Authors Adi Muhajirin Ajif Yunizar Pratama Yusuf Almajid, Nafis Hasan Fatoni Illa Nur’aini Kardinah Indriana Meutia Khalida, Rakhmi Lisa Adhani Lisa Adhani Mayadi Mayadi Mayadi Muhamad Dani Yusuf Muhammad Ganang Martyana Pilipus Neri Agustima Prima Dina Atika Purnomo, Rakhmat RAFIKA SARI Rakhmi Khalida Rakhmi Khalida Reghita Suryani Putri Renilda Filiandini Rizki Surya Pratama, Daffa Siti Setiawati SITI SETIAWATI Syifa Rahmadhani Tb Ai Munandar Tb Ai Munandar, Tb Ai Tubagus Maulana Kusuma