Clustering of students tahfidz ability based on k-means method

Seeing the number of students who have difficulty memorizing the Koran, and not knowing the kyai that each student has different memorization abilities makes the students slow in memorizing the Koran. One way to be able to sort out the ability of students in memorizing is by classifying them into several cluster groups based on the abilities of the students. Actually, research on this needs to be developed using data mining. In this paper, we propose using the clustering method. The clustering method chosen is to use the K-Means algorithm in order to group tahfidz based on the abilities possessed by students. Researchers used the attributes of speed of memorization, makhorijul letters, tajwid, and fluency in reading to determine the group of tahfidz ability in the case study at Luqman Al Hakim Kudus Vocational School. The data obtained were 70 data on the tahfidz ability of SMK Luqman Al Hakim Kudus students for the 2018-2020 period with 4 attributes, namely memorization speed, makhorijul letters, recitation, and fluency in reading the Qur'an. The results obtained are the formation of 4 clusters, where in cluster 1 there are 27% of students with most students having very good tahfidzul abilities, in cluster 2 there are 51% of students, most of whom have tahfidzul abilities with good criteria, in cluster 3 only there are 1% of students who have good tahfidz abilities, and in cluster 4 there are 20% of students who have good tahfidz abilities, with a high level of clustering accuracy can be seen from the low error value test results using the BCV (Between-Class Variation) method with WCV (Within-Class Variation) of 0.387.