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All Journal Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Huda, Dias Nabila
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Computer Science & IT

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Clustering Analysis of Umrah Pilgrim Data Based on the K-Medoid Method Huda, Dias Nabila; Anggrawan, Anthony; Hairani, Hairani
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 3 No 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4601

Abstract

The Umrah pilgrimage is becoming increasingly popular among Indonesians, with millions of participants yearly. This trend creates a need for service providers to understand the characteristics of pilgrims to improve service quality, marketing strategies, and competitiveness. Analyzing data on pilgrims helps service providers develop more effective strategies and tailor packages to match their needs, ensuring competitiveness in a growing market. This study aims to clusters Umrah pilgrims based on age, gender, district, and chosen package using the K-Medoid clustering method. This research uses the K-Medoid method for the reason that it is more resistant to noise and outliers compared to other clustering methods. The most centrally located point in the data set is called a ”medoid,” which is an object in a cluster that has the lowest difference to all other objects in the cluster. The results of this study are that the K-Medoid method successfully grouped pilgrims into three clusters: Cluster 1 with 63 members, Cluster 2 with 25 members, and Cluster 3 with 25 members. The findings indicate that the Milad Mastour package is preferred by older pilgrims, primarily from Mataram and West Lombok. The Arbain package is favored by younger pilgrims from the same regions, while adult pilgrims mostly choose the Regular package. The implication of this research is that it can provide insights for service providers to design more specific programs that align with the profiles of pilgrims based on age and district.
Co-Authors Anthony Anggrawan Hairani Hairani