Semantic analysis of Hadith for topic classification using Latent Semantic Indexing (LSI) / Aiman Haziq Ibrahim

The aim of this project is to provide a framework utilizing Latent Semantic Indexing (LSI) to categorize topics in Hadith texts for semantic analysis. Islamic teachings place a high value on the hadith literature, which records the words and deeds of Prophet Muhammad (peace be upon him). To make it...

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Main Author:	Ibrahim, Aiman Haziq
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/95548/1/95548.pdf
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id	my-uitm-ir.95548
record_format	uketd_dc
spelling	my-uitm-ir.955482024-05-31T02:52:44Z Semantic analysis of Hadith for topic classification using Latent Semantic Indexing (LSI) / Aiman Haziq Ibrahim 2024 Ibrahim, Aiman Haziq Algorithms The aim of this project is to provide a framework utilizing Latent Semantic Indexing (LSI) to categorize topics in Hadith texts for semantic analysis. Islamic teachings place a high value on the hadith literature, which records the words and deeds of Prophet Muhammad (peace be upon him). To make it simple to access, retrieve, and comprehend pertinent information, Hadith writings must be effectively organized and categorized depending on their topics. The subjectivity, labor-intensive manual categorization, and insufficient capture of semantic links within texts are only a few of the drawbacks of the currently available approaches for Hadith topic classification. To address these challenges, LSI-based framework was proposed that leverages the latent semantic meaning in Hadith texts. LSI captures the underlying semantic relationships between words and enables more accurate topic classification. The research framework consists of six phases, including a preliminary study, requirement analysis, data finding, development, evaluation, and documentation. The data finding involves collecting and preprocessing reliable Hadith datasets. Development focuses on creating an information retrieval system using LSI. The evaluation assesses the system's performance through metrics like cosine similarity, precision, recall, and F1 Score. The experiment assessed the effectiveness of LSI by utilizing ten queries and relevant judgements, precision ranged from 5.4% to 100%, recall from 0% to 65%, yielding an average F1 Score of 19.4%. Finally, documentation encompasses writing a comprehensive report that includes background, methodology, findings, and conclusions. 2024 Thesis https://ir.uitm.edu.my/id/eprint/95548/ https://ir.uitm.edu.my/id/eprint/95548/1/95548.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Sadjirin, Rosian
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Sadjirin, Rosian
topic	Algorithms
spellingShingle	Algorithms Ibrahim, Aiman Haziq Semantic analysis of Hadith for topic classification using Latent Semantic Indexing (LSI) / Aiman Haziq Ibrahim
description	The aim of this project is to provide a framework utilizing Latent Semantic Indexing (LSI) to categorize topics in Hadith texts for semantic analysis. Islamic teachings place a high value on the hadith literature, which records the words and deeds of Prophet Muhammad (peace be upon him). To make it simple to access, retrieve, and comprehend pertinent information, Hadith writings must be effectively organized and categorized depending on their topics. The subjectivity, labor-intensive manual categorization, and insufficient capture of semantic links within texts are only a few of the drawbacks of the currently available approaches for Hadith topic classification. To address these challenges, LSI-based framework was proposed that leverages the latent semantic meaning in Hadith texts. LSI captures the underlying semantic relationships between words and enables more accurate topic classification. The research framework consists of six phases, including a preliminary study, requirement analysis, data finding, development, evaluation, and documentation. The data finding involves collecting and preprocessing reliable Hadith datasets. Development focuses on creating an information retrieval system using LSI. The evaluation assesses the system's performance through metrics like cosine similarity, precision, recall, and F1 Score. The experiment assessed the effectiveness of LSI by utilizing ten queries and relevant judgements, precision ranged from 5.4% to 100%, recall from 0% to 65%, yielding an average F1 Score of 19.4%. Finally, documentation encompasses writing a comprehensive report that includes background, methodology, findings, and conclusions.
format	Thesis
qualification_level	Bachelor degree
author	Ibrahim, Aiman Haziq
author_facet	Ibrahim, Aiman Haziq
author_sort	Ibrahim, Aiman Haziq
title	Semantic analysis of Hadith for topic classification using Latent Semantic Indexing (LSI) / Aiman Haziq Ibrahim
title_short	Semantic analysis of Hadith for topic classification using Latent Semantic Indexing (LSI) / Aiman Haziq Ibrahim
title_full	Semantic analysis of Hadith for topic classification using Latent Semantic Indexing (LSI) / Aiman Haziq Ibrahim
title_fullStr	Semantic analysis of Hadith for topic classification using Latent Semantic Indexing (LSI) / Aiman Haziq Ibrahim
title_full_unstemmed	Semantic analysis of Hadith for topic classification using Latent Semantic Indexing (LSI) / Aiman Haziq Ibrahim
title_sort	semantic analysis of hadith for topic classification using latent semantic indexing (lsi) / aiman haziq ibrahim
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/95548/1/95548.pdf
_version_	1804889963675779072

Semantic analysis of Hadith for topic classification using Latent Semantic Indexing (LSI) / Aiman Haziq Ibrahim

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