Similarity Detection for Hadith of Fiqh of Women using Cosine Similarity and Boyer Moore Method

M. Yunus, Badruzzaman and Irfan, Mohamad and Budiawan Zulfikar, Wildan and Darmalaksana, Wahyudin (2020) Similarity Detection for Hadith of Fiqh of Women using Cosine Similarity and Boyer Moore Method. International Journal of Advanced Trends in Computer Science and Engineering, 9 (1). pp. 63-75. ISSN 2278-3091

ijatcse11912020.pdf - Published Version

Download (630kB) | Preview
Official URL:


Nowadays, people can get information easily including about fiqh and hadith as a source of Islamic law. The problem is, there are so many articles about jurisprudence whose understanding refers to the laws or rules relating to the hadith whose validity cannot be ascertained. The study aims to determine the degree of similarity between the hadith contained in articles with reliable sources such as books and books. One of the outputs of this study is an application that can determine the similarity of hadith using Cosine Similarity and Boyer Moore by matching strings starting from the right position to the leftmost position and using the cosine similarity method to determine the similarity based on the calculation of the distance between vectors A and B that produce angles cosine x between the two vectors. In the testing phase, the proposed model can run as planned. In one test scenario, the number of keywords tested was 9 cases compared to the categories in the database with an accuracy of 80%. And determine the similarity of two or more objects Using the cosine similarity method with weights The percentage of similarity is proportional to the sample of words entered, which is equal to 36%.

Item Type: Article
Uncontrolled Keywords: Boyer Moore, Cosine Similarity, Fiqh, Hadith, Text Mining
Subjects: Islam
Al-Qur'an (Al Qur'an, Alquran, Quran) dan Ilmu yang Berkaitan
Al-Hadits dan yang Berkaitan
Divisions: Fakultas Ushuluddin > Program Studi Ilmu Al-Qur'an dan Tafsir
Depositing User: Mr. Andi Ruswandi, S.Pd.I
Date Deposited: 03 Mar 2020 07:37
Last Modified: 03 Mar 2020 07:37

Actions (login required)

View Item View Item