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

Abstract

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.

Keywords

• al-quran
• topic classification
• multilabel
• word centrality
• svm
• naive bayes
• knn
• decision tree