Skripsi

ANALISIS SEBARAN TOPIK ARTIKEL ILMIAH MENGGUNAKAN INDOBERT DAN K-MEANS

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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.

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Availability

Detail Information

Series Title

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Call Number

T1629752024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xviii, 75 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.071 07

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Pendidikan Teknik Informatika
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MURZ

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