Comparison of K-Medoids Method and Analytical Hierarchy Clustering on Students' Data Grouping

Lisna Zahrotun - Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Utaminingsih Linarti - Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Banu Suandi As - Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Herri Kurnia - Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Liya Sabila - Universitas Ahmad Dahlan, Yogyakarta, Indonesia

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One sign of how successfully the educational process is carried out on campus in a university is the timely graduation of students. This study compares the Analytic Hierarchy Clustering (AHC) approach with the K-Medoids method, a data mining technique for categorizing student data based on school origin, region of origin, average math score, TOEFL, GPA, and length study. This study was carried out at University X, which contains a variety of architectural styles. The R department, the S department, the T department, and the U department make up one of them. K-Medoids and AHC techniques Utilize the number of clusters 2, 3, and 4 and the silhouette coefficient approach. The evaluation's findings indicate a value. Although there is a linear silhouette between the AHC and K-Medoids methods, the AHC approach (departments R: 0.88, S: 0.87, T: 0.88, and U: 0.88) has a more excellent Silhouette value than K-Medoids (department R: 0.35, department S: 0.65 number of cluster 2, department T: 0.67 number of cluster 2 and program Study U: 0,52). The results of the second approach, which includes the K-Medoids and AHC procedures, are determined by the data distribution to be clustered rather than by the quantity of data or clusters. Based on this methodology, University X can refer to the grouping outcomes for the four departments with two achievements to receive results on schedule.


Grouping; K-Medoids; Silhouette Coefficient; Analytical Horarcy Clustering

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