KLASTERISASI DATA UNSUPERVISED MENGGUNAKAN METODE K-MEANS

Simple item page
dc.contributor.authorPramesti, Hanggara Bima
dc.contributor.supervisorFitriansyah, Aidil
dc.date.accessioned2021-06-17T04:07:05Z
dc.date.available2021-06-17T04:07:05Z
dc.date.issued2020-04
dc.description.abstractEach year the research of student’s thesis is increasing and it is possible to have the same or similar topics, where this thesis document can be grouped or clusterized based on the similiarity pattern of titles. Before doing a thesis document clustering, the title of the thesis will be weighted using the Text Mining method and Term Frequency-Inverse Document Frequency (TF-IDF). The grouping method used is the K-Means method which is an unsupervised clustering technique with the calculation distance of similarities using Cosine Similarity and the selection of initial cluster centroids that have been developed using Improved K-Means, which combines distance and density optimization methods. The final result of the clustering using 73 data title text of the thesis student generates seven clusters where members of each cluster have a high similiarity seen from the title text of a fellow cluster member.en_US
dc.description.sponsorshipFakultas Matematika dan Ilmu Pengetahuan Alam Kampus Bina Widya Pekanbaru, 28293, Indonesia hanggara.bima5572@student.unri.ac.iden_US
dc.identifier.otherwahyu sari yeni
dc.identifier.urihttps://repository.unri.ac.id/handle/123456789/9971
dc.language.isoenen_US
dc.subjectClustering,en_US
dc.subjectCosine Similiarityen_US
dc.subjectImproved K-Meansen_US
dc.subjectK-Meansen_US
dc.subjectTF-IDFen_US
dc.titleKLASTERISASI DATA UNSUPERVISED MENGGUNAKAN METODE K-MEANSen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Hanggara Bima Pramesti_compressed.pdf
Size:
397.27 KB
Format:
Unknown data format
Description:
artikel
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Sistem Infomasi