Yusof, Norzihani and Rosidi, Siti Aishah Rosidi and Ibrahim, Nuzulha Khilwani Ibrahim and Ahmed Ali, Ahmed El-Mogtaba Bannga
(2020)
Thematic textual hadith classification: an experiment in rapidminer using support vector machine (SVM) and naïve bayes algorithm.
International Journal of Advanced Trends in Computer Science and Engineering, 9 (4).
pp. 5967-5972.
E-ISSN 2278 - 3091
Abstract
There are many existing problems in Hadith studies trending
in the study field. The issues are changeable from the
digitalization of the Hadith data to an exact case study of
estimation of narrators’ chain for a particular Hadith.
However, in this paper, we are not concentrating on the such
learning of estimating, confirming or authenticating a
Hadith. It focuses more on the data mining use to the Hadith
dataset. We put on the Hadith dataset onto one of machine
learning tools which is text classification. The Hadith dataset
is put into experiment for Hadith textual classification. It
concentrates on the thematic classification based on the
themes and words occurrences from the Hadith text (matn).
The Hadith textual classification does not trace on the hukm
and position or class of Hadith. This research does not
categorize the Hadith into hukm Sahih, Hasan, Dhaif, or
Mawdhoo’. However, the Hadith thematic dataset of this
study use only Hadith from Sahih Bukhari, where all Hadith
in the Book is categorized as sahih by Imam Al-Bukhari. The
classification for this thematic Hadith dataset is implemented
using Rapidminer, a machine learning tool using Naïve Bayes
and Support Vector Machine (SVM) methods. From the
results, the different value of accuracy for both SVM and
Naïve Bayes Algorithm was 2.4%. The Naïve Bayes
Algorithm displayed better result comparing to SVM. We
believe that the result could be better by improving the data,
algorithms, algorithm tuning or ensemble methods for the
future experiments
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