PERBANDINGAN ALGORITMA K-MEANS DAN K-MEDOIDS DALAM PENGELOMPOKAN PROVINSI DI INDONESIA BERDASARKAN INDIKATOR KEADAAN SEKOLAH DASAR

I Putu Arya Vidyananta; Kadek Teguh Dermawan

doi:10.23960/jitet.v13i3S1.8145

Authors

I Putu Arya Vidyananta Univeristas Pendidikan Ganesha
Kadek Teguh Dermawan

DOI:

https://doi.org/10.23960/jitet.v13i3S1.8145

Abstract Views: 54 File Views: 31

Keywords:

Clustering, K-Means, K-Medoids, Silhouette Score

Abstract

This study is motivated by the inequality of primary education quality in Indonesia, reflected in disparities in the number of schools, teachers, students, and facilities across provinces. Data-driven analysis is needed to map these conditions so the government can design more targeted policies. This research applies clustering by comparing K-Means and K-Medoids algorithms using primary school data from the Ministry of Primary and Secondary Education portal. The study follows the CRISP-DM framework, including problem understanding, data preparation, modeling, and evaluation. The optimal cluster number was determined using the Elbow method and Silhouette Score. Results show that K-Means with two clusters achieved the best performance with a Silhouette Score of 0.7069, higher than K-Medoids at 0.6702. The first cluster represents most provinces with smaller education scales, while the second cluster includes larger provinces with significantly more schools, students, and teachers. These findings suggest that K-Means is more suitable for mapping primary education conditions in Indonesia and may support evidence-based policies for educational equity.

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References

R. Kurniawan, M. M. M. Mukarrobin, and M. Mahradianur, “Klasterisasi Tingkat Pendidikan Di Dki Jakarta Pada Tingkat Kecamatan Menggunakan Algoritma K-Means,” Technologia: Jurnal Ilmiah, vol. 12, no. 4, pp. 234–239, 2021.

N. Nurahman and D. D. Aulia, “klasterisasi pendidikan masyarakat untuk mengetahui daerah dengan pendidikan terendah menggunakan algoritma K-Means,” Jurnal Informatika dan Rekayasa Perangkat Lunak, vol. 5, no. 1, pp. 38–44, 2023.

N. K. Zuhal, “Study Comparison K-Means Clustering Dengan Algoritma Hierarchical Clustering,” in Seminar Nasional Teknologi & Sains, 2022, pp. 200–205.

J. Heidari, N. Daneshpour, and A. Zangeneh, “A novel K-means and K-medoids algorithms for clustering non-spherical-shape clusters non-sensitive to outliers,” Pattern Recognit, vol. 155, p. 110639, 2024, doi: https://doi.org/10.1016/j.patcog.2024.110639.

W. A. Suputra, I. Candiasa, and I. Suryawan, “Klasterisasi hasil ujian nasional SMA/MA dengan algoritma k-means. Wahana Matematika dan Sains: Jurnal Matematika, Sains, dan Pembelajarannya, 15 (1), 22-30,” 2021.

F. Zahra, A. Khalif, and B. N. Sari, “PENGELOMPOKAN TINGKAT KEMISKINAN DI SETIAP PROVINSI DI INDONESIA MENGGUNAKAN ALGORITMA K-MEDOIDS,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 2, Apr. 2024, [Online]. Available: https://journal.eng.unila.ac.id/index.php/jitet/article/view/4199

M. D. Salman et al., “Comparison of K-Means and K-Medoids Clustering Algorithm Performance in Grouping Schools in Riau Province Based on Availability of Facilities and Infrastructure,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 5, no. 3, pp. 797–806, Jun. 2025, doi: 10.57152/malcom.v5i3.1950.

I. G. S. D. Putra and I. N. T. A. Putra, “IMPLEMENTASI METODE NAÏVE BAYES PADA ANALISIS SENTIMEN PENGGUNA APLIKASI MOBILE KITA BISA,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 13, no. 2, 2025.

Z. Setiawan et al., Buku Ajar Data Mining. PT. Sonpedia Publishing Indonesia, 2023.

N. W. Wardani, P. G. S. C. Nugraha, and G. S. Mahendra, “Implementasi Naïve Bayes Pada Data Mining Untuk Mengklasifikasikan Penjualan Barang Terlaris Pada Perusahaan Ritel,” JST (Jurnal Sains dan Teknologi), vol. 12, no. 3, pp. 656–668, 2023.

C. Ergenç and R. Aktaş, “Clustering S&P 500 companies by machine learning for sustainable decision-making,” Economics and Business Review, vol. 11, no. 3, pp. 91–117, 2025.

D. D. Hariyanti, G. A. Pradnyana, and I. G. M. Darmawiguna, “Kombinasi metode Naive Bayes dan K-Medoid dalam memprediksi penjurusan siswa di sekolah menengah atas,” J. Ilmu Komput, vol. 14, no. 2, p. 88, 2021.

N. Wijaya, “PERFORMA ALGORITMA K-MEANS DAN FUZZY C-MEANS DALAM ANALISIS KLASTER PENDIDIKAN DI TINGKAT KECAMATAN JAKARTA,” Jurnal Komputer dan Informatika, vol. 18, no. 2, pp. 80–86, 2023.

A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and J. Heming, “K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data,” Inf Sci (N Y), vol. 622, pp. 178–210, 2023.

Y. Hasan, “Pengukuran Silhouette Score dan Davies-Bouldin Index pada Hasil Cluster K-Means dan DBSCAN,” KAKIFIKOM (Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer), vol. 6, no. 1, pp. 60–74, Apr. 2024, [Online]. Available: https://ejournal.ust.ac.id/index.php/KAKIFIKOM/article/view/3938

G. S. Mahendra, T. Santhi, K. D. A. Sutrisna, P. P. Cahayani, I. G. Hendrayana, and P. G. S. C. Nugraha, “Sistem Pendukung Keputusan untuk Merekomendasikan Wisata di Kabupaten Klungkung Menggunakan Metode MOORA,” RIGGS: Journal of Artificial Intelligence and Digital Business, vol. 4, no. 1, pp. 567–575, 2025.

G. S. Mahendra and I. N. I. Wiradika, “Sistem Pendukung Keputusan Pemilihan Daya Tarik Wisata Favorit Menggunakan PIPRECIA-CoCoSo dengan Implementasi Python,” Teknomatika, vol. 14, no. 01, pp. 1–12, 2024.

K. A. Pratama, G. A. Pradnyana, and I. K. R. Arthana, “Pengembangan Sistem Cerdas Untuk Prediksi Daftar Kembali Mahasiswa Baru Dengan Metode Naive Bayes (Studi Kasus: Universitas Pendidikan Ganesha),” SINTECH (Science and Information Technology) Journal, vol. 3, no. 1, pp. 22–34, 2020.