K-Means Clustering Algorithm to See the Correlation of Tahfidz Activities with Student’s Learning Outcomes - Agus Nur Khomarudin; Asri Hidayat (Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi, 2022)
Computer technology is currently used as a tool to support educational activities and assist teachers in quickly, precisely, and accurately processing student data. The Qur'an has been memorized by many students at MAN 1 Bukittinggi as a result of long-running Tahfidz activities, which have also helped them stand out in teaching and learning evaluation meetings held at the end of each semester. In conveying the activities which take place in the field, the Tahfidz teacher has not used real data and certainly will not describe the real state of Tahfidz activity and student memorization. The solution to this problem is applying the Data Mining technique for grouping data of Tahfidz activity and student learning outcomes, in order to discover the correlation between the two and whether there is any effect or not. The data processed in this research is Tahfidz activity data and student learning outcomes data for class XI (eleven) sourced from piles of data, the data used are Tahfidz memorization data, Tahfidz scores, and student grades at MAN 1 Bukittinggi. The data mining development methodology used in this research is Cross-Industry Standart Process for Data Mining (CRIPS-DM). The results of the research by applying the K-Means Clustering algorithm has produced two clusters. The first cluster shows the characteristic that Tahfidz activity doesn’t have significant effect on student learning outcomes. The second cluster shows the characteristics that Tahfidz activity has a significant effect on student learning outcomes. The data from the clustering of each cluster is then analyzed and the results of the cluster analysis are used by the school as a consideration in evaluating Tahfidz activity and learning outcomes whether it has been effective or not at MAN 1 Bukittinggi.