Data Mining Analysis as a Determinant of CPNS Passing Score Utilizing the K-Means Algorithm (Case Study of the BMKG Office in North Sumatra)
Keywords:
Data Mining, Passing Grade, CPNS, K Means, Clustering, BMKG North SumatraAbstract
This research aims to analyse the application of data mining as a basis for determining the passing grade of prospective civil servants at the BMKG Office in North Sumatra using the K Means algorithm. The background of this research is based on the importance of establishing an objective, measurable passing grade that aligns with the distribution pattern of the participants' abilities. The method used is a quantitative approach with stages of collecting participant score data, data cleaning, normalisation, clustering process using the K Means algorithm, and interpretation of the clustering results. The processed data is grouped based on the level of achievement, resulting in several clusters that represent the participants' ability categories, such as high, medium, and low. The analysis results show that the K Means algorithm can help identify participant score patterns more systematically and provide a more accurate picture in determining the passing grade threshold. Thus, the use of data mining through the K Means algorithm can serve as an alternative decision-support tool in determining the CPNS passing grade more fairly, effectively, and based on data at the BMKG Office in North Sumatra.
References
M. Yamin, “Implementasi Pembelajaran Aktualisasi Latsar CPNS pada Badan Pengembangan Sumber Daya Manusia Provinsi Sulawesi Selatan,” no. 101, 2015.
D. Sioyong, “IMPLEMENTASI DATA MINING MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING PENYAKIT PASIEN RAWAT JALAN PADA KLINIK DR . ATIRAH,” vol. 7, no. 4, pp. 2144–2151, 2023.
W. I. Rahayu, S. F. Pane, L. A. Frimanda, and K. Kunci, “IMPLEMENTASI DATA MINING DENGAN METODE K-MEANS CLUSTERING UNTUK MENENTUKAN IKLAN AUDIO BERDASARKAN USER BEHAVIORS PADA APLIKASI AUDIO SOCIAL MEDIA SVARA DI PT . ZAMRUD KHATULISTIWA TECHNOLOGY,” vol. 10, no. 2, pp. 13–19, 2018.
D. I. Wilayah and K. Depok, “Implementasi algoritma k-means untuk clustering kasus kriminal di wilayah kota depok 123,” vol. 2, no. 1, pp. 408–415, 2025.
F. P. Dewi, P. S. Aryni, and Y. Umaidah, “Implementasi Algoritma K-Means Clustering Seleksi Siswa Berprestasi Berdasarkan Keaktifan dalam Proses Pembelajaran,” vol. 7, no. 2, pp. 111–121, 2022.
D. Irawan, G. Wijaya, and T. T. Warisaji, “Penerapan Algoritma K-Means Clustering untuk Segmentasi Nasabah Bank,” vol. 6, no. 1, 2025.
N. Hendrastuty, “Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa,” vol. 3, pp. 46–56, 2024.
B. Ariansah et al., “Penerapan K-Means Clustering untuk Pengelompokan Data Industri Kecil Menengah di Provinsi Jambi,” vol. 6, no. September, pp. 359–371, 2025, doi: 10.35957/jtsi.v6i2.13553.
I. Kurikulum, “Implementasi kurikulum 2013,” 2013.
P. P. Desain, “PARADIGMA PENDIDIKAN DESAIN”.
“No Title”.
M. Terkait, “K-Means – Penerapan, Permasalahan dan Metode Terkait,” vol. 3, no. Pebruari, pp. 47–60, 2007.
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