Optimalisasi Latent Dirichlet Allocation untuk Ekstraksi Topik Utama dalam Teks Dongeng

Authors

  • Yosan Odhianto Universitas Nusantara PGRI Kediri Indonesia
  • Daniel Swanjaya, M.Kom Universitas Nusantara PGRI Kediri Indonesia
  • Julian Sahertian, M.Kom Universitas Nusantara PGRI Kediri Indonesia

DOI:

https://doi.org/10.29407/xnbmr279

Abstract

Latent Dirichlet Allocation (LDA) adalah algoritma topic modeling yang bekerja tanpa label data dan sangat dipengaruhi oleh pra-pemrosesan dan pengaturan parameter. Penelitian ini bertujuan mengoptimalkan LDA untuk mengekstraksi topik utama dari 100 teks dongeng berbahasa Indonesia. Teks diproses menggunakan berbagai kombinasi teknik pra-pemrosesan seperti tokenisasi, stopword removal, stemming, dan normalisasi. Eksperimen dilakukan dengan memvariasikan jumlah topik (K) serta parameter alpha dan eta. Evaluasi menggunakan coherence score untuk menilai konsistensi semantik topik. Hasil terbaik diperoleh pada kombinasi pra-pemrosesan kedua dengan 15 topik, menghasilkan coherence score tertinggi sebesar 0,4885. Temuan ini menunjukkan bahwa pemilihan pra-pemrosesan dan parameter yang tepat dapat meningkatkan kualitas topik secara signifikan. Penelitian ini diharapkan mendukung pengembangan analisis topik pada teks naratif Indonesia.

Keywords:

coherence score, dongeng, latent dirichlet allocation, pemodelan topik, prapemrosesan teks bahasa indonesia

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Published

2025-07-10

Issue

Vol. 9 No. 2 (2025): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2025

Section

Articles

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Copyright (c) 2025 Yosan Odhianto, Daniel Swanjaya, M.Kom, Julian Sahertian, M.Kom

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How to Cite

Optimalisasi Latent Dirichlet Allocation untuk Ekstraksi Topik Utama dalam Teks Dongeng. (2025). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 9(2), 1705-1712. https://doi.org/10.29407/xnbmr279