Klasifikasi Hasil Seleksi Kompotensi Dasar CPNS Menggunakan Metode Decision Tree

Ravensky T. Silangen, Muhammad Yahya Matdoan

Abstract


Civil Servants (PNS) are one of the jobs that are of interest to various groups of people in Indonesia. The need for qualified and competitive human resources in this era of globalization requires the government to be more serious in recruiting prospective civil servants so that the realization of good service and organizational needs for existing position qualifications can be met. The implementation of the 2021 civil servant candidate selection at Pattimura University is carried out based on the regulations of the State Civil Service Agency with several stages of selection, one of which is the Basic Competence Selection with a predetermined value standard. This study aims to classify the test results of Candidates for Civil Servants at Pattimura University. The data used in this study is secondary data obtained from the State Civil Service Agency in 2021. The method used in this study is the Decision Tree method. The results show that there are 4 classes (classification) with an Accuracy value of 75%, Classification Error of 25%, Kappa of 0.947, Recall of 97.14%, and Precision of 93.94%.


Keywords


Decision Tree, Classification, Selection of Basic Competencies.

Full Text:

PDF

References


P. N. S. Di and K. Badan, “Kinerja Pegawai Negeri Sipil,” 2015.

T. Kristiana, “Penerapan Profile Matching Untuk Penilaian Kinerja Pegawai Negeri Sipil ( PNS ),” vol. XI, no. 2, pp. 161–170, 2015.

S. H. Ha and S. H. Joo, “A Hybrid Data Mining Method for the Medical Classification of Chest Pain,” pp. 608–613, 2010.

P. K. Singh, “Clustering Techniques in Data Mining : A Comparison,” pp. 0–5.

J. W. Grzymala-busse, M. Hu, and N. York, “A Comparison of Several Approaches to Missing Attribute Values in Data Mining,” pp. 378–385.

D. P. Utomo, “Analisis Komparasi Metode Klasifikasi Data Mining dan Reduksi Atribut Pada Data Set Penyakit Jantung,” vol. 4, no. April, pp. 437–444, 2020, doi: 10.30865/mib.v4i2.2080.

M. Hidayat and M. Amin, “Analisis Prediksi Drop Out Berdasarkan Perilaku Sosial Educational Data Mining Menggunakan Jari,” vol. 01262, 2015.

A. Basuki and I. Syarif, “Decision Tree,” 2003.

P. Meilina, “Penerapan Data Mining dengan Metode Kalsifikasi Menggunakan Decision Tree dan Regresi,” 2015.

S. Wahyuningsih, D. R. Utari, U. B. Luhur, D. Tree, and K. Validation, “Perbandingan Metode K-Nearest Neighbor , Naïve Bayes dan Decision Tree untuk Prediksi Kelayakan Pemberian Kredit,” pp. 8–9, 2018.

D. Marutho, “Perbandingan Metode Naïve Bayes , KNN , Decision Tree Pada Laporan Water Level Jakarta,” pp. 90–97, 2019.

M. A. K-means and G. Abdurrahman, “Clustering Data Ujian Tengah Semester ( UTS ) Data Mining,” pp. 71–79, 2006.

M. L. Wong, K. S. Leung, and S. Member, “An Efficient Data Mining Method for Learning Bayesian Networks Using an Evolutionary Algorithm-Based Hybrid Approach,” vol. 8, no. 4, pp. 378–404, 2004.

H. Annur, “Klasifikasi Masyarakat Miskin Menggunakan Metode Decision Tree,” vol. 10, pp. 160–165, 2018.

T. Dudkina, I. Meniailov, K. Bazilevych, and S. Krivtsov, “Classification and Prediction of Diabetes Disease using Decision Tree Method,” vol. 2836, pp. 0–1, 2021.

F. T. Informasi and U. Gunadarma, “Metode Decision Tree Untuk Klasifikasi Hasil Seleksi Kompetensi Dasar Pada Cpns 2019 di Arsip Nasional Republik Indonesia,” pp. 107–114, 2020.




DOI: http://dx.doi.org/10.12962/j27213862.v5i2.12353

Refbacks

  • There are currently no refbacks.




Creative Commons License
Inferensi by Department of Statistics ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/inferensi.

ISSN:  0216-308X

e-ISSN: 2721-3862

Web
Analytics Made Easy - StatCounter View My Stats