Name is regarded as important to human. A person's name can give impact to him/her either positively or negatively. It is important to recognize or match a person's name with the right person. To do this on the computer, we need to develop a rule so that the computer will be able to recognize the right name with the right person. Before developing the rule, the features about the person's name itself need to be identified. So far, there has been very little work on recognizing a person's name in the Malay texts. This research identifies features of authentic narrator's name in the Malay hadith texts. We extracted the authentic narrators' names manually from hadith texts in the purpose of identifying the features of the names. We extracted manually 455 narrator's name from 150 Malay hadith texts. From the extracted name, exist same person of narrator's name with different types of spelling. There are also many forms of the narrator's names which refers to one person. We also identify the name manner in the Malay hadith texts and added 3 more name manners. Then, we developed the rule to recognize the narrators' names in the Malay hadith texts based on the identified features.

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.

Background: In line with Universiti Sains Islam Malaysia's (USIM) tagline 'Exploring Islamic Science, Spearheading Knowledge', we embarked on a new paradigm of teaching by integrating naqli components into the microbiology and immunology curriculum. The main objective of this integration was to enhance students' appreciation towards Islam and Science, so that they would become good Muslim doctors. The naqli components were delivered through various teaching and learning techniques such as lectures, seminars, and online assignments. Methods: A total of eighty year 3 students from Faculty of Medicine and Health Sciences, USIM were involved in this study. They were exposed to the new method of teaching for the whole academic year, session 2014/2015. The effectiveness of this program was evaluated through questionnaires, given at the end of academic session. Outcome: More than 90% students agreed that the integration were clearly delivered, relevant to the topics at hand and enhanced their knowledge. Most students (>90%) preferred interactive lectures rather than students' initiated method such as seminar and speaker's corner. About two third of the students did not prefer online method. Moreover, 15.9% of them said the allotted time was insufficient and 46% agreed that there was inadequate resources in the library. Conclusions: The integration of naqli components into microbiology subject was favoured by students. However, it should be improved with allocation of more slots, upgrading of online system and increment of relevant library resources. Looking forward, we are convinced this is the way to go in producing holistic doctors equipped with necessary knowledge, both in aqli and naqli to further advance Medicine and Islam.

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.