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.

In a way to make the result of Information Retrieval (IR) more accurate, a stemmer is needed to differentiate the words in searching useful information. This research aims to analyze both processing speed and accuracy of the Malay Language Stemmer such as Fatimah Stemmer and UniSZA Stemmer. This research will also compare the performance of Fuzzy Logic Ranking Function using the both stemmer. Evaluation of Recall and Precision using the relevant judgement list by the expert. The results presented UniSZA Stemmer clearly dominated the Fatimah Stemmer processing speed performance with faster times recorded in each set of the experiment, however, in term of accuracy, unfortunately Fatimah Stemmer has clearly dominated the UniSZA stemming accuracy performance with having much more correct stemmed words for each set of the experiment. The results also showed that Fuzzy Logic Ranking with Fatimah Stemmer has outperformed Fuzzy Logic Ranking with UniSZA Stemmer and English Porter Stemmer on 5 out of 8 Topic Set of query results on the Mean Average Precision measure. Fuzzy Logic Ranking with Fatimah Stemmer also gets the best result on the Precision at Rank 10, Mean Average Precision and the percentage of no relevant document in the top ten retrieved measures, on the topic that has most queries which is topic 'Umum' that has a total of 11 queries.

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.

Ranking is likely the most important process of an Information Retrieval (IR) system that will be used to evaluate and measure the effectiveness of an IR system. This paper aims to produce the implementation of Fuzzy Logic Controller of Mamdani-type Fuzzy Inference System for defining the ranking function by using the BM25 Model in the Malay IR System that also includes the Malay Stemmer. The result of the ranking function then will be compared to the result of Vector Space Model that is also applied in Malay IR System and be evaluated using relevant document by the Hadith expert. The results showed that FBMIR has slightly outperformed Vector Space Model on 3 Topic Set of query results such as Iman,Ilmu and Wuduk on the Precision at Rank 10 and the percentage of no relevant document in the top ten retrieved measures.