TY  - THES
N1  - Ir. Maria Ulfah Siregar, S.Kom., MIT., Ph.D.,
ID  - digilib47009
UR  - https://digilib.uin-suka.ac.id/id/eprint/47009/
A1  - QOMARIYAH, NIM. 17106050005
Y1  - 2021/07/16/
N2  - Universities as educational institutions have very large amounts of academic
data and student administrative data, which may not be used properly. Student
academic data that has accumulated from year to year needs to be analyzed to
produce information can map the distribution of students.
Student academic data processing utilizes data mining processes using
clustering techniques, K-Means and K-Medoids. This study aims to implement and
analyze the comparison of which algorithm is more optimal based on the cluster
validation test with the Davies Bouldin Index (DBI). The data used are academic
data of UIN Sunan Kalijaga students in the 2013-2015 batch.
Based on the research that has been done on the clustering process, the
number of clusters 2,3,4,5, and 6. In the k-Means process, the best number of
clusters is 5 with a DBI value of 0.781. In the k-Medoids process, the best number
of clusters is 3 with a DBI value of 0.929. Based on the value of the DBI validation
test that the k-Means algorithm is more optimal than the k-Medoids. So that the
cluster of students with the highest average GPA of 3,325 is 401 students
PB  - UIN SUNAN KALIJAGA YOGYAKARTA
KW  - student data
KW  -  K-Means Clustering
KW  -  K-Medoids Clustering
M1  - skripsi
TI  - STUDI KOMPARASI ALGORITMA K-MEANS CLUSTERING
DAN K-MEDOIDS CLUSTERING DALAM PENGKLASTERAN
DATA MAHASISWA
AV  - restricted
EP  - 107
ER  -