![]() ![]() The experiments carried on 1,000 Hadiths from “Sahih Muslim”: 900 Hadiths as training dataset and 100 Hadiths as testing dataset, and the results show a noteworthy accuracy for the proposed hybrid approach. This study presents a new hybrid approach founded on the hidden Markov model (HMM) and gazetteer lists to process “Isnad.” The approach objective is to expect all POIs in the Isnad including narrators’ names. A lot of computational research studies suggest serving Hadith sciences by extracting the narrators’ names and other POIs using various approaches. Isnad contains many words and phrases called “Isnad-Phrases” these phrases have many types or categories called part of Isnads (POIs) like Narrator-Name, Prophet-Name, and Received-Method. ![]() Therefore, to check the authenticity of Hadiths, the three conditions must be satisfied, and to do so, the narrators’ names must be extracted first. The first step of Hadith judgment is the extraction of narrators’ names, after that, the rules of judgment, which were set out by Hadith’s scientists, could be implemented, three of these rules are particularly related to the narrators’ series, and these rules are continuity of the transmission chain, the trustworthiness of the narrators, and the preciseness of the narrators. “Matn” and “Isnad” are the main constituents of Hadith “Matn” is the sayings of the prophet, whereas “Isnad” represents the narrators’ series. Hadith judgment implies checking the validity of Hadith to decide whether it is correct (trustworthy) or false (bogus). In future, we plan to extend this paper with the analysis on interclass similarity and also test on larger dataset. 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. ![]() The performances are evaluated based on standard performance metrics used in text classification which is accuracy and response time. In this paper, SVM, NB and k-NN are used to identify and evaluate the performance of Malay translated hadith based on sanad. This research is to see how Machine Learning techniques are used to classify Malay translated Hadith document 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. However, very little research work has been found on classification of Malay translated Hadith based on sanad. Sanad is one of important part used to determine the authentication of hadith. In the Malay hadith texts based on the identified features. Then, we developed the rule to recognize the narrators’ names In the Malay hadith texts and added 3 more name manners. There are also many forms of the narrator’s names From the extracted name,Įxist same person of narrator’s name with different types of Manually from hadith texts in the purpose of identifying theįeatures of the names. We extracted the authentic narrators’ names Identifies features of authentic narrator’s name in the Malay Recognizing a person’s name in the Malay texts. So far, there has been very little work on Beforeĭeveloping the rule, the features about the person’s name itself Recognize the right name with the right person. ![]() Need to develop a rule so that the computer will be able to It is important to recognize or match a person’s Name can give impact to him/her either positively or ![]()
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