Please use this identifier to cite or link to this item:
https://oarep.usim.edu.my/jspui/handle/123456789/1121
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Mohammad Najib S.R. | en_US |
dc.contributor.author | Abd Rahman N. | en_US |
dc.contributor.author | Kamal Ismail N. | en_US |
dc.contributor.author | Alias N. | en_US |
dc.contributor.author | Mohamed Nor Z. | en_US |
dc.contributor.author | Alias M.N. | en_US |
dc.date.accessioned | 2020-01-03T04:59:33Z | - |
dc.date.available | 2020-01-03T04:59:33Z | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 2261236X | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036451625&doi=10.1051%2fmatecconf%2f201713500066&partnerID=40&md5=5a6f2cdd1743412bdafc7631597df903 | - |
dc.description.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. | en_US |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | EDP Sciences | en_US |
dc.relation.ispartof | Open Access | en_US |
dc.relation.ispartof | MATEC Web of Conferences | - |
dc.source | Scopus | - |
dc.title | Comparative Study of Machine Learning Approach on Malay Translated Hadith Text Classification based on Sanad | en_US |
dc.title.alternative | MATEC Web Conf. | en_US |
dc.identifier.doi | 10.1051/matecconf/201713500066 | - |
dc.identifier.scopus | 2-s2.0-85036451625 | - |
dc.identifier.volume | 135 | - |
dc.identifier.ArtNo | 66 | - |
dc.contributor.affiliations | Faculty of Quran and Sunnah Studies | - |
dc.contributor.affiliations | Universiti Teknologi MARA (UiTM) | - |
dc.contributor.affiliations | Universiti Sains Islam Malaysia (USIM) | - |
dc.contributor.affiliations | Universiti Kebangsaan Malaysia (UKM) | - |
dc.FundingDetails | FRGS/1/2015/ICT01/UITM/03/1 | - |
dc.FundingDetails | This research was funded by the Malaysian Government under Fundamental Research Grant Scheme (FRGS) (FRGS/1/2015/ICT01/UITM/03/1) in Universiti Teknologi MARA, Shah Alam. | - |
dc.editor | Nik Hisyamudin M.N.Al Emran I.Sofian M.Amir K.Mohd Faizal M.B.Izzuddin Z.Mohd Rasidi I.Mohd Azlis Sani Md.J.Ahmad Mubarak T.A. | en_US |
dc.Conferencename | 8th International Conference on Mechanical and Manufacturing Engineering, ICME 2017 | - |
dc.Conferencedate | 22 July 2017 through 23 July 2017 | - |
dc.Conferencecode | 132057 | - |
dc.description.nature | Final | en_US |
item.grantfulltext | open | - |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en_US | - |
crisitem.author.orcid | 0000-0002-0530-4527 | - |
Appears in Collections: | Scopus |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Comparative Study of Machine Learning Approach on Malay Translated Hadith Text Classification based on Sanad.pdf | 360.71 kB | Adobe PDF | View/Open |
SCOPUSTM
Citations
10
checked on Apr 22, 2024
Page view(s)
1
checked on Apr 25, 2024
Google ScholarTM
Check
Altmetric
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.