Semantic Feature Analysis for Multi-Label Text Classification on Topics of the Al-Quran Verses


Gugun Mediamer, Adiwijaya, Journal of Information Processing Systems Vol. 20, No. 1, pp. 1-12, Feb. 2024  

https://doi.org/10.3745/JIPS.02.0209
Keywords: Text Classification, Tensor Space Model, The Al-Quran Verses, Word Embedding
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Abstract

Nowadays, Islamic content is widely used in research, including Hadith and the Al-Quran. Both are mostly used in the field of natural language processing, especially in text classification research. One of the difficulties in learning the Al-Quran is ambiguity, while the Al-Quran is used as the main source of Islamic law and the life guidance of a Muslim in the world. This research was proposed to relieve people in learning the Al-Quran. We proposed a word embedding feature-based on Tensor Space Model as feature extraction, which is used to reduce the ambiguity. Based on the experiment results and the analysis, we prove that the proposed method yields the best performance with the Hamming loss 0.10317.


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Cite this article
[APA Style]
Mediamer, G. & (2024). Semantic Feature Analysis for Multi-Label Text Classification on Topics of the Al-Quran Verses. Journal of Information Processing Systems, 20(1), 1-12. DOI: 10.3745/JIPS.02.0209.

[IEEE Style]
G. Mediamer and Adiwijaya, "Semantic Feature Analysis for Multi-Label Text Classification on Topics of the Al-Quran Verses," Journal of Information Processing Systems, vol. 20, no. 1, pp. 1-12, 2024. DOI: 10.3745/JIPS.02.0209.

[ACM Style]
Gugun Mediamer and Adiwijaya. 2024. Semantic Feature Analysis for Multi-Label Text Classification on Topics of the Al-Quran Verses. Journal of Information Processing Systems, 20, 1, (2024), 1-12. DOI: 10.3745/JIPS.02.0209.