IMPLEMENTASI SISTEM INFORMASI PADA ANALISIS KEHADIRAN SISWA DENGAN ALGORITMA K-MEANS

This study aims to analyze the factors that influence student attendance at school and identify patterns of behavior that appear based on attendance data. The method used in this study is data analysis using the k-means clustering algorithm. Student attendance data that has been collected from the attendance system is processed and analyzed to identify groups of students based on their attendance level. The results showed that school factors and individual factors had a poor average, so they needed to be improved. However, students with an attendance rate above 85% showed a high sensitivity to learning. On the other hand, students with an attendance percentage of less than 100% often apply for sick leave. In addition, through the implementation of an attendance information system, this study made a positive contribution in increasing student attendance as a whole. The results of the analysis using k-means clustering show that students who attend regularly have better attendance rates and positive behavior patterns. On the other hand, students who are frequently absent or legally absent exhibit patterns of behavior that deserve special attention. In conclusion, this study provides an in-depth understanding of the factors affecting student attendance and provides valuable insights for school management. By knowing student behavior patterns based on attendance data, schools can take appropriate actions and offer appropriate solutions to increase student attendance effectively. Thus, this research has the potential to have a positive impact in creating a more productive and supportive learning environment for all students in the school.

Keywords: Attendance, Data Mining, K-Means