Topic Classification of Quranic Verses in English Translation
Using Word Centrality Measurement

Topic Classification of Quranic Verses in English Translation
Using Word Centrality Measurement

Quran, Topic classification, Multilabel, Word Centrality, SVM, Naïve Bayes, KNN, Decision Tree

Every Muslim in the world believes that the Quran is a miracle and the words of God (Kalamullah) revealed to the Prophet
Muhammad SAW to be conveyed to humans. The Quran is used by humans as a guide in dealing with all problems in every
aspect of life. To study the Quran, it is necessary to know what topic is being discussed in every single verse. With the help of
technology, the verses of the Quran can be given topics automatically. This task is called multilabel classification where input
data can be classified into one or more categories. This research aims to apply the multilabel classification to classify the
topics of the Quranic verses in English translation into 10 topics using the Word Centrality measurement as the word weighting
value. Then a comparison is made to the 4 classification methods, namely SVM, Naïve Bayes, KNN, and Decision Tree. The
result of the centrality measurement shows that the word ‘Allah’ is the most important or the most central word of the whole
document of the Quran with the scenario using stopword removal. Furthermore, the use of word centrality value as term
weighting in feature extraction can improve the performance of the classification system

Achmad Salim Aiman1
, Kemas Muslim Lhaksmana2
, Jondri3

Telkom University

31-10-2022

Fajar bagus W

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Indonesia

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