Analisis Data Mining Absensi Siswa SMP Negeri 1 Bilah Barat Dengan Metode Algoritma K-Means Cluestering - Sinurat, Shondy Raja Ferdinand; Nasution, Marnis; Ah, Rahma Muti (Jurnal Informatika, 2024)
The presence of students in Junior High School has significant implications in the world of Education. The success of education depends not only on the content of the curriculum and the quality of teaching, but is also strongly linked to the extent to which students are regularly present at school. The problem of student absenteeism in junior high school is an issue that should not be ignored, as it can negatively affect academic achievement, the development of social skills, and the overall educational experience of students. A student's absence from school can be caused by a complex of factors. These factors include personal problems such as lack of motivation, family problems such as domestic conflicts, poor physical or mental health, as well as environmental factors such as school accessibility. A deep understanding of the reasons behind student absenteeism is an important first step to addressing this problem. Student attendance Data that has been accumulated over the past 6 months can be a valuable source of information in analyzing student attendance. However, extracting useful information from large and complex attendance data is a challenging task. This is where Data Mining comes in. Data Mining is an approach that enables the identification of patterns, trends, and valuable information in large and complex data.