PENINGKATAN EFISIENSI PEMANTAUAN KEHADIRAN SISWA MENGGUNAKAN CLASTERING K-MEANS PADA MADRASAH I'DADIYAH SALAFIYAH SYAFI'IYAH
DOI:
https://doi.org/10.69714/87vcvz50Keywords:
Attendance Patterns, Data Mining, K-Means Clustering, Student AttendanceAbstract
This research aims to increase efficiency in monitoring student attendance at Madrasah I'dadiyah Salafiyah Syafi'iyah by utilizing the K-Means Clustering analysis method. Monitoring student attendance is still carried out conventionally, so it often takes time and is less effective in identifying overall student attendance patterns. For this reason, in this research, student attendance data collected from the madrasa attendance system was analyzed using K-Means Clustering, a machine learning technique that can group students based on their attendance patterns. This process produces several groups which make it easier for the madrasah to identify students who frequently attend, rarely attend, or frequently do not attend. In this way, madrasas can take more appropriate steps in dealing with attendance problems, such as paying special attention to students who are often absent. The results of this research indicate that the application of K-Means Clustering can increase the efficiency of attendance monitoring and provide a stronger basis for decision making to improve the attendance system at the I'dadiyah Salafiyah Syafi'iyah madrasah.
References
Tan, P.-N., Steinbach, M., & Kumar, V. (2018). Introduction to Data Mining (3rd ed.). Pearson Education.
Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann.
Chien, C.-F., & Chen, S.-H. (2016). Clustering Students' Behavior Based on Their Learning Styles and Preferences Using K-Means Algorithm. Journal of Educational Technology Systems, 45(3), 352-367.
Nguyen, D., Nguyen, T., & Nguyen, H. (2017). Improving the Accuracy of Student Attendance Monitoring Using Data Mining Techniques. International Journal of Educational Management, 31(2), 123-137.
Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17(3), 37-54.
Muhammad Arhami & Muhammad Nasir (2020). Data Mining – Algoritma dan Implementasi. ANDI penerbit, Politeknik Negeri LHOKSEUMAWE.
Nabila, H., Retno, D., & Saputro, S. (2022). Clustering Data Campuran Numerik dan Kategorik
Menggunakan Algoritme Ensemble Quick RObust Clustering using linKs (QROCK). Prisma, Prosiding Seminar Nasional Matematika, 5(1), 716–720. https://journal.unnes.ac.id/sju/index.php/prisma/article/view/54590
Yuni Franata Sinurat, Masrizal, & Irmayanti (2024). Data Mining Pengelompokan Siswa Berprestasi Menggunakan Metode Clustering. NEM penerbit.
S. T. M. K. Yahya, Data Mining. CV Jejak (Jejak Publisher), 2022. [Online]. Available: https://books.google.co.id/books?id=0J2mEAAAQBAJ
