Please use this identifier to cite or link to this item: https://oarep.usim.edu.my/jspui/handle/123456789/1121
Title: Comparative Study of Machine Learning Approach on Malay Translated Hadith Text Classification based on Sanad
Other Titles: MATEC Web Conf.
Authors: Mohammad Najib S.R. 
Abd Rahman N. 
Kamal Ismail N. 
Alias N. 
Mohamed Nor Z. 
Alias M.N. 
Issue Date: 2017
Publisher: EDP Sciences
Journal: Open Access 
MATEC Web of Conferences 
Abstract: 
Sanad is one of important part used to determine the authentication of hadith. However, very little research work has been found on classification of Malay translated Hadith based on sanad. There are some researches done using machine learning approach on hadith classification based on sanad but using different objective with different language. This research is to see how Machine Learning techniques are used to classify Malay translated Hadith document based on sanad. In this paper, SVM, NB and k-NN are used to identify and evaluate the performance of Malay translated hadith based on sanad. The performances are evaluated based on standard performance metrics used in text classification which is accuracy and response time. The results show that SVM has the highest accuracy and k-NN has the best response time (time taken in process for classification data) compare to other classifier. In future, we plan to extend this paper with the analysis on interclass similarity and also test on larger dataset. � 2017 The Authors.
URI: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036451625&doi=10.1051%2fmatecconf%2f201713500066&partnerID=40&md5=5a6f2cdd1743412bdafc7631597df903
ISSN: 2261236X
DOI: 10.1051/matecconf/201713500066
Appears in Collections:Scopus

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