Comparative Study of Machine Learning Approach on Malay Translated Hadith Text Classification based on Sanad

¹ Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
² Fakulti Pengajian Quran dan Sunnah, Universiti Sains Islam Malaysia, Bandar Baru Nilai, Negeri Sembilan, Malaysia
³ Fakulti Pengajian Islam, Universiti Kebangsaan Malaysia, Bangi, Selangor Malaysia

^* Corresponding author: syahidah@pahang.uitm.edu.my

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.