Analisis Pengelompokan Tingkat Pemahaman Materi Siswa Berdasarkan Nilai Ujian Menggunakan Algoritma K-Means - Cahaya Muzaddidah; Arief Hermawan; Donny Avianto (LKP Unity Academy, 2025)
Student understanding of learning content is an important indicator of educational success. This study aims to group student understanding based on their exam results in mathematics, English, science, social studies, and Arabic using the K-means Clustering algorithm. The data used consisted of 60 rows of student performance data, which were processed and standardized using Google Colab. The number of clusters was limited to two groups (K=2) to categorize students as having a high level of understanding or a basic level of understanding. The results showed that the K-means algorithm successfully identified groups of students with different levels of understanding based on their average exam scores. The group with a high level of understanding achieved an average score of more than 87.4. Teachers can use these Clustering results as a basis for developing more individualized and effective learning strategies for each group of students.