Implementation of the K-Medoids Algorithm in a Clustering System for Determining Promotional Strategies at SMK Bina Satria

Rizky Wira Nanda Pasaribu; Marah Doly Nasution

doi:10.59934/jaiea.v5i1.1743

Authors

Rizky Wira Nanda Pasaribu Universitas Muhammadiyah Sumatera Utara
Marah Doly Nasution Universitas Muhammadiyah Sumatera Utara

DOI:

https://doi.org/10.59934/jaiea.v5i1.1743

Keywords:

K-Medoids, Clustering, Promotional Strategy, Member Get Member, Data Mining

Abstract

The competition between public and private vocational high schools (SMK) requires private schools, such as SMK Bina Satria, to adopt more effective and data-driven promotional strategies. This study aims to implement the K-Medoids algorithm in the clustering process of student origin school data to group the contribution potential of partner schools toward new student enrollment. The variables used in the analysis include the total number of student contributions and the participation status in the Member Get Member (MGM) program. This research employs a quantitative approach using data mining methods. The stages include data collection, preprocessing, determining the optimal number of clusters using the Elbow Method and Silhouette Score, implementation of the K-Medoids algorithm, and development of a web-based system for visualizing the results. The analysis results show that origin schools can be grouped into three clusters: High Potential Loyal Partners, Potential Future Partners, and Incidental Contributors. The developed system assists the school in determining more targeted and efficient promotional strategies. With this implementation, SMK Bina Satria can formulate promotion policies based on objective partner segmentation and enhance the effectiveness of its promotional programs to attract new students.

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