Abstract
Benford’s law is widely applied in testing anomalies in various dataset, including accounting fraud detection and population numbers. It is a statistical regularity, which is said that it works better with larger datasets that span large orders of magnitude distributed in a non-uniform way. In this study, we examine the potential metrics in small-sized Quran dataset that are applicable for the Benford’s law. Against our expectations, we find that the Quran dataset conforms to the Benford’s law. We provide evidence that metrics such as total paragraph per chapter and total verse per chapter conform to Benford’s distribution. However, total verse is closer to Benford’s law prediction compared to total paragraph.
Highlights
The Quran is an Islam religious text that includes God's message delivered to the Prophet Muhammad S.A.W by Gabriel, an angel, to be recited, comprehended, and practised as a guidance or living style for humanity (Oktaviani et al, 2019)
Benford's Law (BL), sometimes identified as the first digit law, describes how integers are distributed in massive databases
Adherence of social networking algorithms and Intelligence Analysis acts to Benford's Law was inspected, where the findings revealed that bots obey BL, implying that utilising this rule can aid in the detection of harmful online programmed entities and associated behaviours on media platforms (Madahali and Hall, 2020)
Summary
The Quran is an Islam religious text that includes God's message delivered to the Prophet Muhammad S.A.W by Gabriel, an angel, to be recited, comprehended, and practised as a guidance or living style for humanity (Oktaviani et al, 2019). Al-Khatib al-Iskafi asserted that only 28 or roughly 25% of the 114 Quran's chapters do not include identical or repetitive passages (Oktaviani et al, 2019). Benford's Law (BL), sometimes identified as the first digit law, describes how integers are distributed in massive databases. This rule considers that the regularity of the first integers of numbers is not uniformly scattered across lots of naturally occurring structures. Zipf's law is a similar empirical rule. Zipf verified that, provided a database containing a language's frequent term, the occurrence of each term is inversely related towards its place in the ordering of term's regularity (Melián et al, 2017)
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