ANALISIS SEBARAN TOPIK ARTIKEL ILMIAH MENGGUNAKAN INDOBERT DAN K-MEANS

AQILA, SITI MARITZA and Yusliani, Novi (2024) ANALISIS SEBARAN TOPIK ARTIKEL ILMIAH MENGGUNAKAN INDOBERT DAN K-MEANS. Undergraduate thesis, Sriwijaya University.

	Text RAMA_55201_09021282126048.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_55201_09021282126048_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (6MB) | Request a copy
	Text RAMA_55201_09021282126048_0008118205_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (565kB)
	Text RAMA_55201_09021282126048_0008118205_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (372kB) | Request a copy
	Text RAMA_55201_09021282126048_0008118205_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (245kB) | Request a copy
	Text RAMA_55201_09021282126048_0008118205_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy
	Text RAMA_55201_09021282126048_0008118205_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (461kB) | Request a copy
	Text RAMA_55201_09021282126048_0008118205_06.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (71kB) | Request a copy
	Text RAMA_55201_09021282126048_0008118205_07_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (104kB) | Request a copy
	Text RAMA_55201_09021282126048_0008118205_08_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (180kB) | Request a copy

Abstract

The significant increase in the number of scientific articles published along with advances in science and technology poses challenges in managing and organizing articles to support the efficiency of literature analysis. This research aims to develop a scientific article topic distribution analysis system by utilizing the IndoBERT model and the K-Means algorithm. The dataset used comes from 12 nationally accredited scientific journals on the Science and Technology Index (SINTA). Clustering evaluation was conducted using silhouette score, with the highest value of 0.932656 in journals with EISSN 25799258. This clustering model is then applied to predict new articles based on the relevance of their journal topics, with inscope or outscope categories determined based on the threshold value.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sebaran Topik Artikel Ilmiah, IndoBERT, K-Means, silhouette score
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Siti Maritza Aqila
Date Deposited:	08 Jan 2025 02:46
Last Modified:	08 Jan 2025 02:46
URI:	http://repository.unsri.ac.id/id/eprint/162975

Actions (login required)

View Item