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

Zahrotun, Lisna (2023) Bukti Koresponden Comparison of K-Medoids Method and Analytical Hierarchy Clustering on Students' Data Grouping. [Artikel Dosen]

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Abstract

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

Item Type:	Artikel Dosen
Subjects:	T Technology > T Technology (General)
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Informatics Engineering (S1-Teknik Informatika)
Depositing User:	Mrs. Lisna Zahrotun
Date Deposited:	12 Aug 2023 05:15
Last Modified:	12 Aug 2023 05:15
URI:	http://eprints.uad.ac.id/id/eprint/44627

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