The Implementation of Latent Semantic Indexing on Knowledge Retrieval Process in Knowledge Management System Development.

This study examines Latent Semantic indexing (LSI) using Singular Value Decomposition (SVD) in the knowledge retrieval process, namely indexing Indonesian text. There are three stages in this process: (1) text processing, which consists of tokenisation, filtering and stemming process, (2) developing...

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Publicado en:Pertanika Journal of Social Sciences & Humanities Vol. 25S; pp. 99 - 108
Autores principales: Fitriasari, Novi Sofia, Megasari, Rani, Yuniarsih, Arum
Formato: Artículo
Publicado: Universiti Putra Malaysia Nov2017 Special Issue
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: The Implementation of Latent Semantic Indexing on Knowledge Retrieval Process in Knowledge Management System Development.
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        au:
          Fitriasari, Novi Sofia
          Megasari, Rani
          Yuniarsih, Arum
        affil: Departemen Pendidikan limit Komputer, Universitas Pendidikan Indonesia, Jl. Dr.Setiabudhi Nomor 229 Bandung 40154, Indonesia
      su:
        Latent semantic analysis
        Information retrieval
        Singular value decomposition
        Stemming (Linguistics)
        Filtering software
      sug:
        subj:
          Latent semantic analysis
          Information retrieval
          Singular value decomposition
          Stemming (Linguistics)
          Filtering software
      keyword:
        Knowledge Retrieval
        LSI
        SVD
      ab: This study examines Latent Semantic indexing (LSI) using Singular Value Decomposition (SVD) in the knowledge retrieval process, namely indexing Indonesian text. There are three stages in this process: (1) text processing, which consists of tokenisation, filtering and stemming process, (2) developing LSI using SVD and (3) evaluating and measuring performance. The result showed Mean Average Precision around 77.90% on scenario matrix dimension 120 and average precision for first retrieval around 83.33% on scenario matrix dimension 90.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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