Muslikh, Ahmad Rofiqul ORCID: https://orcid.org/0009-0000-2457-6803, Akbar, Ismail, Setiadi, De Rosal Ignatius Moses and Islam, Hussain Md Mehedul (2024) Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText. Techno.Com : Jurnal Teknologi Informasi, 23 (1). pp. 37-50. ISSN 2356-2579 (e) ; 1412-2693

Preview	Text Multi-label Classification.pdf Download (533kB) | Preview
Preview	Text Similarity Report Multi-label Classification.pdf Download (551kB) | Preview

Abstract

Studying the Qur'an is a pivotal act of worship in Islam, which necessitates a structured understanding of its verses to facilitate learning and referencing. Reflecting this complexity, each Quranic verse is rich with unique thematic elements and can be classified into a range of distinct categories. This study explores the enhancement of a multi-label classification model through the integration of FastText. Employing a CNN+Bi-LSTM architecture, the research undertakes the classification of Quranic translations across categories such as Tauhid, Ibadah, Akhlak, and Sejarah. Based on model evaluation using F1-Score, it shows significant differences between the CNN+Bi-LSTM model without FastText, with the highest result being 68.70% in the 80:20 testing configuration. Conversely, the CNN+Bi-LSTM+FastText model, combining embedding size and epoch parameters, achieves a result of 73.30% with an embedding size of 200, epoch of 100, and a 90:10 testing configuration. These findings underscore the significant impact of FastText on model optimization, with an enhancement margin of 4.6% over the base model.

Actions (login required)

View Item