<![CDATA[The Quran, comprising Allah's absolute divine messages, serves as guidance. Although reading the Quran with tafsir proves beneficial, it may not offer a comprehensive understanding of the entire message conveyed by the Al-Quran. This is due to the Quran addressing diverse topics within each surah, necessitating readers to reference interconnected verses throughout the entire chapter for a holistic interpretation. However, given the extensive and varied verses, obtaining accurate translations for each verse can be a complex and time-consuming endeavor. Therefore, it becomes imperative to categorize the translated text of Quranic verses into distinct classes based on their primary content, utilizing Fuzzy C-Means, Random Forest, and Support Vector Machine. The analysis, considering the obtained Davies-Bouldin Index (DBI) value, reveals that cluster 9 emerges as the optimal cluster for classifying QS An-Nisa data, exhibiting the lowest DBI value of 4.30. Notably, the Random Forest algorithm demonstrates higher accuracy compared to the SVM algorithm, achieving an accuracy rate of 66.37%, while the SVM algorithm attains an accuracy of 50.56%.]]>
