relation: https://digilib.uin-suka.ac.id/id/eprint/47009/
title: STUDI KOMPARASI ALGORITMA K-MEANS CLUSTERING  DAN K-MEDOIDS CLUSTERING DALAM PENGKLASTERAN  DATA MAHASISWA
creator: QOMARIYAH, NIM. 17106050005
subject: Tehnik Informatika
description: 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
date: 2021-07-16
type: Thesis
type: NonPeerReviewed
format: text
language: id
identifier: https://digilib.uin-suka.ac.id/id/eprint/47009/1/17106050005_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
format: text
language: id
identifier: https://digilib.uin-suka.ac.id/id/eprint/47009/2/17106050005_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf
identifier:   QOMARIYAH, NIM. 17106050005  (2021) STUDI KOMPARASI ALGORITMA K-MEANS CLUSTERING DAN K-MEDOIDS CLUSTERING DALAM PENGKLASTERAN DATA MAHASISWA.  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.