Open AccessAutomating quranic verses labeling using machine learning approach
Author(s) -
A. Adeleke,
Nurnabilah Samsudin,
Aida Mustapha,
Samina Khalid
Publication year - 2019
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v16.i2.pp925-931
Subject(s) - artificial intelligence , naive bayes classifier , c4.5 algorithm , machine learning , support vector machine , computer science , task (project management) , natural language processing , algorithm , engineering , systems engineering
Classification of Quranic verses into predefined categories is an essential task in Quranic studies. However, in recent times, with the advancement in information technology and machine learning, several classification algorithms have been developed for the purpose of text classification tasks. Automated text classification (ATC) is a well-known technique in machine learning. It is the task of developing models that could be trained to automatically assign to each text instances a known label from a predefined state. In this paper, four conventional ML classifiers: support vector machine (SVM), naïve bayes (NB), decision trees (J48), nearest neighbor ( k -NN), are used in classifying selected Quranic verses into three predefined class labels: faith ( iman ), worship ( ibadah ), etiquettes ( akhlak ). The Quranic data comprises of verses in chapter two ( al-Baqara ) of the holy scripture. In the results, the classifiers achieved above 80% accuracy score with naïve bayes (NB) algorithm recording the overall highest scores of 93.9% accuracy and 0.964 AUC.