Cluster Analysis of Alumni Job Waiting Times Using the K-Means Algorithm with Elbow and Silhouette Methods
DOI:
https://doi.org/10.30741/jid.v5i1.2080Keywords:
K-Means, Clustering, Elbow, Silhouette, Tracer Study, Employment Waiting PeriodAbstract
Evaluating graduate employability is a key indicator for assessing the quality of higher education institutions. One effective approach involves analyzing alumni tracer study data using data mining techniques. This study aims to categorize alumni data based on the waiting period for employment after graduation using the K-Means algorithm. To determine the optimal number of clusters, two methods were employed: the Elbow method (Sum of Squared Errors/SSE) and the Silhouette Score. The research process included data preprocessing—comprising data selection, transformation, and normalization—to enhance data quality prior to clustering. Test results indicated that the Elbow method yielded an optimal cluster count of k=4, whereas the Silhouette method showed the best result at k=3, with a score of 0.62. Consequently, k=3 was selected as the optimal number of clusters due to superior cluster quality. The clustering results revealed that the majority of alumni (67.6%) experienced a short waiting period (averaging ±2 months), while 16.5% faced a long waiting period (±8 months), and 15.9% fell into the medium category (±5 months). These findings are expected to serve as a basis for higher education institutions to evaluate and improve graduate quality, ensuring better alignment with the demands of the job market.
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