Perbandingan K-Means dan Hierarchical Clustering untuk Pengelompokan Calon Penerima Beasiswa Berdasarkan Data Akademik Sosial dan Ekonomi Mahasiswa

Septia Harliansyah; Muhammad Hafizh Al-Ghifari Rangkuti; Muhammad Zikri Ramadhan

doi:10.30865/json.v7i4.9852

Authors

Septia Harliansyah Universitas Pembangunan Panca Budi
Muhammad Hafizh Al-Ghifari Rangkuti
Muhammad Zikri Ramadhan Universitas Pembangunan Panca Budi

DOI:

https://doi.org/10.30865/json.v7i4.9852

Keywords:

Clustering, Data Mining, Hierarchical Clustering, K-Means, Silhouette Score

Abstract

This study aims to compare the performance of the K-Means and Hierarchical Clustering algorithms in grouping
scholarship applicants based on academic and socio-economic data. The methodology includes data preprocessing through
normalization, determining the optimal number of clusters using the Elbow method, and applying both clustering algorithms.
The evaluation is conducted using the Silhouette Score to measure cluster quality based on cohesion and separation. The
results show that K-Means produces a higher Silhouette Score compared to Hierarchical Clustering, indicating that K-Means
is able to form more compact clusters with clearer separation between them. In contrast, Hierarchical Clustering tends to
produce less optimal cluster structures, especially when applied to datasets with a relatively large number of data points.
Therefore, K-Means can be recommended as a more effective method for grouping scholarship applicants based on academic
and socio-economic data. This study is expected to serve as a reference in selecting appropriate clustering methods in data
mining, particularly to support more objective and accurate decision-making processes.

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