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.

Text representation plays an important role in text classification. Commonly, text representation uses the term frequency technique. Hadith texts consist of two parts; they are the chain of narrators and the content. The term frequency technique is usually used for the content text representation. This research explains the text representation for chain of narrators in hadith texts. The chain of narrators was depicted using graph technique as text representation. A total of 18 hadith texts were used in representing chain of narrators as text representation. The narrator's name and relationships between narrators extracted from hadith texts which produced 82 narrator names and 85 connections between narrators. The 82 names were used as nodes, while the 85 connections as the relationships. The text representation graph subsequently used in the hadith text classification research based on chain of narrators.

Text document expresses enormous sort of information but it lacks the imposed structure of a traditional database. Unstructured data, particularly free running text data has to be transformed into a structured data. Extracting information from text is part of NLP process. The implementation of the NER algorithm for NLP is normally influenced by the domain of the studies. Besides, there is no existing system that is designed to detect the types of named entity in hadith text; develop POS tags and rule based extraction for narrator's name in Hadith Text in the Malay language. The POS tags were developed from 1000 hadith texts. The POS tags were created involving a total of 256 words which is part of narrator's names. The rule based was developed to determine five types of narrator's chain. Further research will determine the relationship between each narrator and the construction of narration's chain.