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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Laurenza Aprilya Melati
Universitas Nusantara PGRI Kediri
Computer Science & IT Engineering

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PENCARIAN DAN EVALUASI RELEVANSI DONGENG BAHASA INDONESIA BERBASIS SEMANTIK MENGGUNAKAN LATENT SEMANTIC INDEXING (LSI) Laurenza Aprilya Melati; Daniel Swanjaya; Danar Putra Pamungkas
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6093

Abstract

Information retrieval in Indonesian folktales faces challenges in identifying semantically relevant stories, particularly due to language variation and synonymy. Keyword-based approaches such as TF-IDF often fail to capture semantic relationships, resulting in suboptimal search results. This study applies Latent Semantic Indexing (LSI) using Singular Value Decomposition (SVD) to enhance the relevance of story retrieval based on latent textual meaning. Evaluation was conducted through subjective assessments by 20 respondents, who rated search results generated from four types of queries: single-word, two-word, three-word, and full-sentence queries. The Friedman test indicated significant differences among query types for both LSI (χ²(3, N=20) = 29.27, p < 0.00001) and TF-IDF (χ²(3, N=20) = 35.45, p < 0.00001). LSI consistently yielded higher relevance scores, especially for more complex queries, demonstrating more effective semantic processing than TF-IDF. These findings suggest that semantic-based approaches are better suited for narrative texts such as folktales. Future research is recommended to explore query expansion and integration with deep learning-based semantic representations.
Co-Authors Danar Putra Pamungkas, Danar Putra Daniel Swanjaya