The Holy Quran is the most significant religious text which is followed by the believers of the Islamic religion. The translations of the Quran are the interpretation of its meaning in different languages to assist those who are not familiar with the Arabic language. The Holy Quran consists of 114 Chapters (Surah) and 6236 Verses. These 114 Chapters have different length. The verse is the smallest segment of the texts of the Quran. The Holy Quran as a book is not classified on topics and its verses describe many topics and many verses even from different chapters converge within the same theme. The number of verses and chapters may share similar topics such as faith and morality. One of the solutions to tackle this issue is the Quran classification. Thus, the aim of this study is to classify the Quranic verses by using the Neural Network (NN) classifier based on the predefined topics in order to provide the readers the relevant Quranic verses depending on their need. This research used the most popular and the widespread of the Holy Quran translation in English language by Abdullah Yusuf Ali as the reference dataset. In this regard, the neural network classifier will address this issue through classifying the Al-Baqarah Surah that represents the Quran into two categories which are the Fasting and Pilgrimage topics. Finally, based on the F-measure, the evaluation of Al-Baqarah classification by using NN showed a level of approximately 90%. This proves that NN has succeeded in presenting a encouraging result in this critical area.
صهيب خليل حميد | Suhaib Kh. Hamed | 7800