Skripsi

KLASIFIKASI BERBASIS ONTOLOGI UNTUK DOKUMEN TUGAS AKHIR DENGAN METODE PEMBOBOTAN FASTTEXT.

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The classification of undergraduate thesis documents is a crucial process for organizing and categorizing student research into appropriate fields of study. This research develops an ontology-based document classification system for final projects by integrating the FastText word embedding method to improve classification accuracy. The system utilizes BidangIlmuInformatika.owl ontology as a knowledge base to determine the hierarchy and relationships between concepts in the field of informatics. FastText method is implemented to generate word vector representations (word embeddings) capable of capturing semantic context from document text. The classification process is performed by calculating the similarity between document vectors weighted with FastText and concept representations in the ontology. The dataset consists of 160 informatics student undergraduate thesis documents divided into four main areas: Data Science and Pattern Recognition, Distributed Systems, Computer Graphics and Visualization, and Natural Language Processing. Evaluation results show that the proposed system achieves an accuracy of 25%. This is because the concepts within the Data Science and Pattern Recognition domain have a broad knowledge structure that spans nearly all areas of Informatics.

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Availability

Detail Information

Series Title

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Call Number

T1629342024

Publisher

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

Collation

xvii, 87 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.015 107

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Prodi Teknik Informatika
Prinsip Matematika dalam Ilmu Komputer

Specific Detail Info

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Statement of Responsibility

MURZ

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