Comparative Analysis of Text Mining Classification Algorithms for English and Indonesian Qur’an Translation

Abstract

Qur'an, As-Sunnah, and Islamic old book have become the sources for Islam followers as sources of knowledge, wisdom, and law. But in daily life, there are still many Muslims who do not understand the meaning of the sentence in the Qur'an even though they read it every day. It becomes a challenge for Science and Engineering field academicians especially Informatics to explore and represent knowledge through intelligent system computing to answer various questions based on knowledge from the Qur'an. This research is creating an enabling computational environment for text mining the Qur'an, of which purpose is to facilitate people to understand each verse in the Qur'an. The classification experiment uses Support Vector Machine (SVM), Naive Bayes, k-Nearest Neighbor (kNN), and J48 Decision Tree classifier algorithms with Al-Baqarah verses translated to English and Indonesian as the dataset which was labeled by three most fundamental aspects of Islam: 'Iman' (faith), 'Ibadah' (worship), and 'Akhlaq' (virtues). Indonesian translation was processed by using the sastrawi package in Python to do the pre-processing and StringToWord Vector in WEKA with the TF-IDF method to implement the algorithms. The classification experiments are determined to measure accuracy, and f-measure, it tested with a percentage split 66% as the data training and the rest as the data testing. The decision from an experiment that was carried out by the classification results, SVM classifier algorithms have the overall best accuracy performance for the Indonesian translation of 81.443% and the Naïve Bayes classifier has the best accuracy for the English translation, which achieved 78.35%.