Implementasi k-Means Clustering untuk Analisis Nilai Akademik Siswa Berdasarkan Nilai Pengetahuan dan Keterampilan

  • Diwa Oktario Dacwanda Universitas Kristen Satya Wacana
  • Yessica Nataliani Fakultas Teknologi Informasi Universitas Kristen Satya Wacana
Keywords: student achievements, clustering, k-means, knowledge, skill

Abstract

Student success is a reflection of the quality of education. The quality and student performance need to be improved through educational facilities, infrastructure, and human resources. Information system is a technology that can be useful in improving the students’ quality and achievement, by analyzing the student achievements. This analysis is carried out by classifying student achievements from their ability to understand the courses. The data includes 266 students with 12 courses, where each course has two scores, i.e., knowledge score and skill score, based on the 2013 curriculum. This study uses k-means clustering method, where the number of clusters is determined by the Davies Bouldin validity index. Three groups of student achievements are generated based on academic scores, namely high, moderate, and low. As a comparison, the grouping of student achievements is also done by computing the knowledge scores only. About 10.15 percent of all students move to other cluster when calculated with knowledge scores only. As the result, the grouping of students with the knowledge and skill scores can improve the student achievements.

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References

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Published
2021-11-30
How to Cite
Dacwanda, D., & Nataliani, Y. (2021). Implementasi k-Means Clustering untuk Analisis Nilai Akademik Siswa Berdasarkan Nilai Pengetahuan dan Keterampilan. AITI, 18(2), 125-138. https://doi.org/10.24246/aiti.v18i2.125-138
Section
Articles