Tajweed Pattern Analysis in the 30th Juz of Qur’an Using the Apriori Algorithm - Nazwa Revalina Azzahra Djuarsah; Marisah Lofiana; Mochammad Khaerul Ilman; Rizki Surya Gani; Gallung Marwan Haqiqi Hafidz (Asosiasi Khazanah Cendekia, 2026)
Tajweed knowledge plays an important role in maintaining the integrity and beauty of the recitation of the Qur'an. However, tajweed learning is often conventional and less data-driven. This study aims to analyze the pattern of the emergence of tajweed rules in the 30th Juz of the Qur'an using the Apriori algorithm, a data mining method capable of efficiently identifying associations between items. The dataset used consists of 564 verses, which are classified into 22 categories of tajweed rules based on manual analysis from trusted sources. Through the process of data transformation and the application of Apriori, several significant patterns of the emergence of tajweed rules were obtained. The Mad Thobii rule is the most dominant with the highest support value of 79.96%. The combination of patterns with the highest confidence value was found in the (Ghunnah) → (Mad Thobii) rule at 83.11%, indicating a strong relationship between the two rules. These findings are expected to provide new insights in data-based tajweed learning and become a reference in the development of interactive media for learning the Qur'an. In the future, this research can be expanded to encompass the entire Quran and other association algorithms, such as FP-Growth, can be applied to evaluate the effectiveness and efficiency of tajweed pattern analysis. This research represents an initial step toward utilizing data to strengthen adaptive and relevant religious learning.