A Multi-Label Classification of Al-Quran Verses Using Ensemble Method and Naïve Bayes


  • Muhammad Rizqi Choirulfikri Telkom University, Bandung, Indonesia
  • Kemas Muslim Lhaksamana * Mail Telkom University, Bandung, Indonesia
  • Said Al Faraby Telkom University, Bandung, Indonesia
  • (*) Corresponding Author
Keywords: Al-Quran; Multi-label; Classification; Ensemble Method; Naïve Bayes

Abstract

Al-Quran is the holy book as a guide and also a source of law for muslims. Thus, understanding and studying Al-Quran is very important for muslims. To make it easier for muslims to understand and study the Qur'an, it is necessary to classify the verses of the Al-Qur'an. This study built a system that can perform multi-label classification of Al-Quran verses. Multi-label means that the classification will divide each verse of the Al-Quran into more than 1 topic. The model is built using the ensemble method by combining several Naïve Bayes algorithms. The ensemble method was chosen because research with different datasets can obtain good performance. The naïve Bayes algorithm was also chosen because it has a simple calculation so it requires a fairly short computation time. The preprocessing step is also carried out to see the comparison of performance results. To measure the performance of the system that has been built, the calculation of hamming loss is used. Based on the experimental results with several testing scenarios, the best performance results are obtained by combining Multinomial NB and Bernoulli NB with a hamming loss value of 0.1167. Thus, the use of the ensemble method can improve performance compared to without the ensemble method. This research can also of course build a multi-label classification model for the verses of Al-Quran with the ensemble method

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Article History
Submitted: 2022-02-02
Published: 2022-03-31
Abstract View: 140 times
PDF Download: 147 times
How to Cite
Choirulfikri, M., Lhaksamana, K., & Faraby, S. (2022). A Multi-Label Classification of Al-Quran Verses Using Ensemble Method and Naïve Bayes. Building of Informatics, Technology and Science (BITS), 3(4), 473-479. https://doi.org/10.47065/bits.v3i4.1287
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