Penerapan Algortima K-Means untuk Mengelompokkan Siswa Berdasarkan Tingkat Pemahaman dan Kemandirian Belajar dalam Kurikulum Merdeka - Ingke Fuji Utami Br Barus; Novriyenni Novriyenni; Imeldawaty Gultom (Asosiasi Riset Teknik Elektro dan Informatika Indonesia, 2025)
The Merdeka Curriculum implemented in various schools in Indonesia aims to provide flexibility in learning, where students can learn according to their individual needs, interests, and pace. One of the challenges in implementing this curriculum is how to effectively identify classroom activity and student discipline. SD Islamiyah, as a school that implements the Merdeka Curriculum, also faces challenges in understanding variations in student classroom activity and discipline. Some students are able to learn in a disciplined manner with little guidance, while others require more intensive support from teachers. Therefore, a system is needed that can group student data more systematically so that teachers can develop teaching strategies that suit the needs of each group of students. One algorithm that can be used in data grouping is k-means clustering. The K-Means algorithm is a non-hierarchical algorithm derived from the data clustering method. The K-Means algorithm begins with the formation of cluster partitions at the beginning, then iteratively refines these cluster partitions until there are no significant changes in the cluster partitions. The K-Means method partitions data into groups so that data with similar characteristics are placed in the same group and data with different characteristics are grouped into other groups. This method can help group students more accurately based on their Class Activity and Discipline. From the results of the analysis, it was concluded that the student data group with Class Activity was Moderately Active Students, with Discipline being Disciplined, and an average score of 71-80.