PENERAPAN DATA MINING DALAM PENILAIAN KINERJA AKADEMIK  SISWA/I SMP YPI PULOGADUNG DENGAN METODE K-MEANS  CLUSTERING

Salsa Nabilatul Adzra; Fuad Nur Hasan; Antonius Yadi Kuntoro

doi:10.33884/jif.v13i02.10396

Authors

Salsa Nabilatul Adzra Universitas Bina Sarana Informatika
Fuad Nur Hasan Universitas Bina Sarana Informatika
Antonius Yadi Kuntoro Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.33884/jif.v13i02.10396

Keywords:

Data mining, K-Means clustering, Student clustering, RapidMiner

Abstract

Improving the quality of education requires an objective, systematic, and data-driven academic performance assessment system. One technological approach that can be used to support this is data mining, specifically the K-Means Clustering method. This studyaims to cluster student academic data based on report card grades for the odd semester of the 2024/2025 academic year using the K-Means algorithm. Data processing was performed using RapidMiner software, with the optimal number of clusters selected at three (K=3) based on the Davies Bouldin Index (DBI) of 0.077. The clustering results form three main categories: Cluster 0 contains 174 students with average academic performance, Cluster 1 contains only one student with the lowest performance, and Cluster 2 contains 107 students with high academic performance. This grouping provides more structured and useful information for schools in designing targeted academic development strategies. This study demonstrates the effectiveness of the K-Means Clustering method in identifying student academic patterns and classifications.

References

Aditia Yudhistira and R. Andika, “Pengelompokan Data Nilai Siswa Madrasah Ta’Hiliyah Menggunakan Metode K-Means Clustering,” J. Ris. Sist. Inf., vol. 2, no. 1, pp. 53–59, 2023, doi: 10.69714/0v1pkz05.

A. Y. Kuntoro, H. Hermanto, T. Asra, F. Syukmana, and H. Wahono, “Classification of Student Majors with C4.5 and Naive Bayes Algorithms (Case Study: SMAN 2 Bekasi City),” Semesta Tek., vol. 23, no. 1, pp. 1–13, 2020, doi: 10.18196/st.231251.

Reza Pahlevi Kurniawan and Ferdiansyah, “Penerapan Algoritme K-Means Clustering Untuk Mengelompokkan Siswa Berdasarkan Nilai Akademik Di Smp Negeri 207 Ssn,” Pros. Semin. Nas. Mhs. Fak. Teknol. Inf., vol. 2, no. 2, pp. 530–538, 2023.

N. Hendrastuty, “Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa,” J. Ilm. Inform. dan Ilmu Komput., vol. 3, no. 1, pp. 46–56, 2024, doi: 10.58602/jima-ilkom.v3i1.26.

Z. Zulham and B. S. Hasugian, “Pengelompokan Siswa Dalam Menentukan Penerima Beasiswa Berdasarkan Prestasi Akademik Dengan Algoritma K-Means,” War. Dharmawangsa, vol. 16, no. 3, pp. 231–241, 2022, doi: 10.46576/wdw.v16i3.2220.

Y. R. Sari, A. Sudewa, D. A. Lestari, and T. I. Jaya, “Penerapan Algoritma K-Means Untuk Clustering Data Kemiskinan Provinsi Banten Menggunakan Rapidminer,” CESS (Journal Comput. Eng. Syst. Sci., vol. 5, no. 2, p. 192, 2020, doi: 10.24114/cess.v5i2.18519.

Darlinda and J. N. Utamajaya, “Sistem Pendukung Keputusan Penerima Beasiswa Program Indonesia Pintar Menggunakan Metode Algoritma K-Means Clustering,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 2, p. 167, 2022, doi: 10.30865/jurikom.v9i2.3971.

P. Rahayu et al., Buku Ajar Data Mining, vol. 1, no. January 2024. 2018.

A. J. Wahidin et al., Data Mining Data mining, vol. 2, no. January 2013. 2023. [Online]. Available: https://repository.uinjkt.ac.id/dspace/bitstream/123456789/71093/1/DATA MINING.pdf

F. N. Hasan and N. Blesyova, “Analisis Sentimen Terhadap Bea Cukai Pada X Menggunakan Metode Support Vector Machine,” vol. 8, no. 6, pp. 12051–12056, 2024, [Online]. Available: https://www.researchgate.net/publication/381830313

H. A. Siregar, A. Azlan, and N. Y. Lumban Gaol, “Penerapan Data Mining Pada Penjualan Rumah Makan Kasih Ibu Menggunakan Metode K-Means Clustering,” J. Sist. Inf. Triguna Dharma (JURSI TGD), vol. 2, no. 5, p. 750, 2023, doi: 10.53513/jursi.v2i5.8955.

Muttaqin et al., Pengenalan Data Mining, no. July. 2023.