ANALISIS DATA MINING MENGGUNAKAN METODE CLUSTERING TERHADAP PRESTASI SISWA I'DADIYAH SUKOREJO

Abstract

This study analyzes the performance patterns of students at Madrasah I’dadiyah Sukorejo using data mining methods, specifically clustering. The analyzed factors include exam scores and participation in extracurricular activities, as both are considered to significantly influence academic performance. Exam scores reflect mastery of subjects, while extracurricular activities often positively impact students' social skills and learning motivation.[1] The K-Means algorithm was selected to classify students into three main groups: high-performing, average-performing, and low-performing students. The clustering results are expected to provide strategic guidance for the school to improve the quality of education. Low-performing students can receive additional guidance or motivational training, while average-performing students can be encouraged to participate more actively in extracurricular activities to enhance interpersonal skills. Understanding these performance patterns helps the school design more effective programs to maximize students’ academic potential based on their needs. This study also opens opportunities for further exploration of other factors affecting academic performance, such as family conditions and the home learning environment. Thus, this approach becomes an essential step in creating a more inclusive and high-quality education system.