Proceedings of the 2nd International Conference on Environmental, Energy, and Earth Science, ICEEES 2023, 30 October 2023, Pekanbaru, Indonesia

Research Article

Topic Modeling of Indonesian Children's Literature Using Latent Semantic Analysis

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  • @INPROCEEDINGS{10.4108/eai.30-10-2023.2343063,
        author={Winda  Monika and Vita  Amelia and Qory Islami Aris and Arbi Haza Nasution},
        title={Topic Modeling of Indonesian Children's Literature Using Latent Semantic Analysis},
        proceedings={Proceedings of the 2nd International Conference on Environmental, Energy, and Earth Science, ICEEES 2023, 30 October 2023, Pekanbaru, Indonesia},
        publisher={EAI},
        proceedings_a={ICEEES},
        year={2024},
        month={4},
        keywords={topic modeling indonesian children's literature latent semantic analysis},
        doi={10.4108/eai.30-10-2023.2343063}
    }
    
  • Winda Monika
    Vita Amelia
    Qory Islami Aris
    Arbi Haza Nasution
    Year: 2024
    Topic Modeling of Indonesian Children's Literature Using Latent Semantic Analysis
    ICEEES
    EAI
    DOI: 10.4108/eai.30-10-2023.2343063
Winda Monika1,*, Vita Amelia1, Qory Islami Aris1, Arbi Haza Nasution2
  • 1: Library Science, Universitas Lancang Kuning, Indonesia
  • 2: Informatics Engineering, Universitas Islam Riau, Indonesia
*Contact email: windamonika@unilak.ac.id

Abstract

The Covid-19 pandemic resulted a significant reduction in the number of publications of literary works, especially works of children's literature, which once dominated the first ranking of the most popular subjects at the Indonesian Publishers Association. So that efforts are needed to raise the enthusiasm of Indonesian children's literature. In addition, data on children's literature is scattered across various portals that compile metadata/bibliography and synopsis summaries of children's literature stories from various sources or online portals. A portal that collects data on all Indonesian children's literature that is grouped automatically according to the most dominant topics reflecting the context of each literary work is urgently needed to facilitate search, develop teaching materials, and inspire the creation of new literary works. This study uses qualitative methods and experiments with several stages of research including metadata analysis of models of children's literature, secondary data collection, topic modeling of children's literature with Latent Semantic Analysis (LSA). The results of the study showed that the topic clustering was most often raised by children's authors published on the Mizan.