Implementasi Algoritma FP-Growth Dalam Menemukan Pola Hubungan Antar Perawi Hadis

Shahreza, Mahathir (2025) Implementasi Algoritma FP-Growth Dalam Menemukan Pola Hubungan Antar Perawi Hadis. Skripsi thesis, UIN Imam Bonjol Padang.

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Abstract

Hadiths are the second source of Islamic law after the Qur’an, making the authenticity of the isnad (chain of narrators) and the credibility of narrators essential in determining a Hadith’s validity. Differences in narrator selection methods, such as the strict standards of Imam Al-Bukhari and Imam Muslim versus the more lenient approaches of Ibnu Majah and at-Tirmidhi, create diverse Sanad network characteristics. This study applies the FP-Growth algorithm to identify patterns of relationships among narrators from four main Hadith collections: Shahih al-Al-Bukhari, Shahih Muslim, Sunan at-Tirmidhi, and Sunan Ibnu Majah, using a quantitative approach based on the Knowledge Discovery in Databases (KDD) framework. The KDD process begins with data selection to extract relevant Hadith files and narrator lists, followed by pre-processing to clean the data, and data transformation to convert Hadith chains into transactional format suitable for FP-Growth analysis. In the data mining stage, FP-Growth is applied with a minimum support of 0.03 and a minimum confidence of 0.7, producing association rules among narrators: 16 patterns in Al-Bukhari, 89 in Muslim, 1 in Ibnu Majah, and 4 in Tirmidhi. These results are visualized using FP-Trees and association graphs to illustrate hierarchical and quantitative relationships. The interpretation and evaluation stage examines the findings and assesses their validity. Testing 10 sample association rules from Al-Bukhari confirms that the Hadiths occur together as indicated by FP-Growth outputs, demonstrating the consistency and reliability of the method. This study provides systematic insight into Hadith narrator relationships and demonstrates the effectiveness of FP-Growth in identifying these patterns.

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