Exploring Semantic Similarity among MUI Fatwas: A Computational Analysis using Generalized Jaccard Similarity

Alfado Rafly  Hermawan; Shofa Wardatul  Jannah

doi:10.12962/j22759970.v4i2.1217

Authors

Alfado Rafly Hermawan Institut Teknologi Sepuluh Nopember
Shofa Wardatul Jannah Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.12962/j22759970.v4i2.1217

Keywords:

Generalized Jaccard Similarity, WordNet, Natural Language Processing, Islamic Documents, MUI

Abstract

Majelis Ulama Indonesia (MUI) plays a crucial role in the Islamic landscape of Indonesia, influencing religious discourse and societal norms. As a primary contributor to policy formulation and the issuance of Islamic fatwas, the MUI significantly impacts the lives of Muslims. However, challenges arise when certain fatwas exhibit similarities, necessitating deeper analysis to understand their differences. Despite limited prior research, there is an urgent need for a computational framework to comprehensively assess fatwa similarities. This study addresses this gap by employing the Generalized Jaccard Similarity method with WordNet, demonstrating its effectiveness compared to the Jaccard method with a 25.86% improvement in string matching quality for evaluating MUI fatwa titles. The Generalized Jaccard similarity analysis reveals that 73 documents exhibit similarity scores greater than 0.5, indicating significant resemblance, while 77,028 documents have scores less than 0.5, indicating lower similarity or dissimilarity. These figures reflect varying degrees of document similarity based on Generalized Jaccard.

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