Muslims suffer from not knowing the validity of the Hadiths or verification of its degree, which represented the second source of legislation in Islam after the Quran. Although many sites allow users in general and Muslims, in particular, the possibility of verifying the authenticity of the hadith, but it is through an information system which is connected to the database of the Hadith. So, there is no intelligent system that can distinguish the hadith automatically, therefore, in this study, we propose a model that can recognize and categorize the hadith automatically and conclusion the essential features through Hadith classification into Sahih, Hasan, Da'if, and Maudu, based on machine learning techniques. This study is primarily concerned with classifying the hadith according to the memory and reliability of the Hadith's narrators. This classification does not depend only on the text of the hadith, as in the rest of the other Arab documents, but depend on also the Sanad of Hadith. Therefore, this study was conducted on three methodologies; these methodologies help us to obtain an accuracy that is more reliable for hadith classification compared to previous researches in this area. In addition to building a model using the Decision tree technique based on the Sanad of hadith for helping us deciding to judge the validity of the hadith, the accuracy of this classifier reached up to 92.59%. Several Learning algorithms have been used in this study, but we reported the best three classifiers (LinearSVC, SGDClassifier, and Logistic regression), which achieved higher accuracy reached up to 93.69%, 93.51, and 92.27% respectively.
The study detailed in REF proposed an automation model that could classify Al-hadeeth features into Sahih, Hasan, Da'if, and Maudu, using machine learning techniques (LinearSVC, SGDClassifier, and LogisticRegression).
Knowledge Discovery in the Hadith According to the Reliability and Memory of the Reporters Using Machine Learning Techniques
IEEE Access
IEEE Access
Computer Science