DETEKSI KEMIRIPAN DOKUMEN MENGGUNAKAN METODE DOCUMENT CLUSTERING K-MEANS CLUSTERING

KAMIL, MUHAMMAD IKHSAN and Abdiansah, Abdiansah (2023) DETEKSI KEMIRIPAN DOKUMEN MENGGUNAKAN METODE DOCUMENT CLUSTERING K-MEANS CLUSTERING. Undergraduate thesis, Sriwijaya University.

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Abstract

Detection, is an action or process of identifying the presence of something that is concealed. This research aim to develop a software that can be used to detect the similarity between thesis using the K-Means Clustering method, which is one of the simplest and popular unsupervised machine learning algorithms. In this research the detection is done on 56 documents using the silhouette method to determine the optimal number of cluster and davies-bouldin index to evaluate the clustering result. The results of the research show that based on the documents studied, the optimal number of clusters was 35 clusters. In which there are 5 clusters that have a population of more than 2 documents.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Detection, K-Means Clustering, Silhouette Score, Davies-Bouldin Index
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhammad Ikhsan Kamil
Date Deposited:	22 Mar 2024 01:55
Last Modified:	22 Mar 2024 01:55
URI:	http://repository.unsri.ac.id/id/eprint/141868

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