Multi-Label Topic Classification on the Qur'an using the K-Nearest Neighbor and Latent Semantic Analysis Methods - Ghina Annisa Shabrina; Kemas Muslim Lhaksmana (Publikasi Indonesia, 2024)
The Qur'an, comprising over 80,000 words, 6,236 verses, and 114 surahs, presents a multifaceted and deeply significant text that demands a nuanced understanding of historical context, classical Arabic, and exegesis. To analyze and classify its content, various methodologies have been employed, including K-Nearest Neighbor (KNN) and Latent Semantic Analysis (LSA). This research investigates the effectiveness of combining KNN with LSA for multi-label topic classification of Qur'anic verses. The study reveals that KNN alone achieved a micro average F1-score of 0.49, demonstrating reliable performance particularly for topics such as