Model Klasifikasi Kenaikan Pangkat Pegawai Negeri Sipil Menggunakan Decision Tree C4.5
-
Kamaruddin Kamaruddin
*
Universitas Teknologi Akba Makassar, Makassar,
Indonesia
- Suman Wijaya Universitas Teknologi Akba Makassar, Makassar, Indonesia
- Mashud Mashud Universitas Teknologi Akba Makassar, Makassar, Indonesia
- Sitti Aisa Universitas Teknologi Akba Makassar, Makassar, Indonesia (*) Corresponding Author
Abstract
The promotion process of Civil Servants (PNS) is a critical component of human resource management aimed at improving performance and professionalism. However, in practice, the process often faces challenges such as subjective assessments and inefficiencies in data processing. This study aims to develop a classification model for determining the eligibility of regular promotions for civil servants using the Decision Tree C4.5 algorithm based on historical personnel data. The dataset consists of 6,193 records obtained from the Regional Personnel and Human Resource Development Agency of Pangkajene and Kepulauan Regency. The attributes used include years of service, performance scores, and employment type. The research stages involve data preprocessing, feature selection, model construction, and evaluation using confusion matrix, accuracy, precision, recall, and F1-score. The results indicate that the proposed model achieves an accuracy of 95.4%, with precision of 87.6%, recall of 83.7%, and F1-score of 85.6%. Additionally, the model generates interpretable decision rules that support decision-making processes. Therefore, the application of data mining using the C4.5 algorithm is proven effective in enhancing objectivity, transparency, and efficiency in civil servant promotion decision-making
Downloads
References
R. Indonesia, “Peraturan Pemerintah Republik Indonesia Nomor 11 Tahun 2017 tentang Manajemen Pegawai Negeri Sipil,” Republik Indonesia, Jakarta, 2017.
J. Han, M. Kamber, and J. Pei, Data mining: Concepts and techniques, 3rd ed. in The Morgan Kaufmann Series in Data Management Systems. Burlington, MA: Morgan Kaufmann, 2012.
I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data mining: Practical machine learning tools and techniques, 4th ed. in The Morgan Kaufmann Series in Data Management Systems. Cambridge, MA: Morgan Kaufmann, 2016.
J. R. Quinlan, C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann, 1993.
S. B. Kotsiantis, “Decision trees: A recent overview,” Artif. Intell. Rev., vol. 39, no. 4, pp. 261–283, 2013, doi: 10.1007/s10462-011-9272-4.
E. Hasmin and S. Aisa, “Penerapan algoritma C4.5 untuk penentuan penerima beasiswa mahasiswa,” CogITo Smart J., vol. 5, no. 2, pp. 308–320, 2019, doi: 10.31154/cogito.v5i2.219.308-320.
S. Hozeng, S. Aisa, and S. Dipanegara, “Aplikasi data mining dengan menggunakan metode decision tree untuk prediksi penentuan resiko kredit,” SISITI Semin. Ilm. Sist. Inf. dan Teknol. Inf., vol. 5, no. 2, pp. 1–10, 2016, doi: 10.36774/sisiti.v5i2.159.
F. Gorunescu, Data mining: Concepts, models and techniques. Berlin, Germany: Springer, 2011. doi: 10.1007/978-3-642-19721-5.
D. T. Larose and C. D. Larose, Discovering knowledge in data: An introduction to data mining, 2nd ed. Hoboken, NJ: Wiley, 2014.
B. Santosa, Data mining: Teknik pemanfaatan data untuk keperluan bisnis, 1st ed. Yogyakarta: Graha Ilmu, 2007.
D. P. Kusumaningtyas and N. Legowo, “Data mining techniques to predict high leadership promotion of civil servants in Indonesia based on talent pool assessments,” J. Syst. Manag. Sci., vol. 13, no. 6, pp. 336–346, 2023, doi: 10.33168/JSMS.2023.0620.
S. R. Safavian and D. Landgrebe, “A survey of decision tree classifier methodology,” IEEE Trans. Syst. Man. Cybern., vol. 21, no. 3, pp. 660–674, 1991, doi: 10.1109/21.97458.
U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From data mining to knowledge discovery in databases,” AI Mag., vol. 17, no. 3, pp. 37–54, 1996, doi: 10.1609/aimag.v17i3.1230.
S. Wahyuddin et al., Data warehouse dan data mining. Medan: Yayasan Kita Menulis, 2023.
F. M. Sabir, M. Mashud, A. Halid, A. Asrul, and R. Rumallang, “Penerapan metode multi criteria decision making sebagai sistem penunjang keputusan promosi jabatan karyawan di PT. Fastfood Indonesia Tbk,” J. Minfo Polgan, vol. 13, no. 1, pp. 150–163, 2024, doi: 10.33395/jmp.v13i1.13500.
H. Saputra, A. Y. Muniar, and M. Mashud, “Sistem pendukung keputusan penilaian kinerja karyawan menggunakan fuzzy logic Tsukamoto,” Neptunus J. Ilmu Komput. dan Teknol. Inf., vol. 3, no. 3, pp. 278–288, 2025, doi: 10.61132/neptunus.v3i3.1028.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Model Klasifikasi Kenaikan Pangkat Pegawai Negeri Sipil Menggunakan Decision Tree C4.5
Pages: 1-9
Copyright (c) 2025 Kamaruddin Kamaruddin, Suman Wijaya, Mashud Mashud, Sitti Aisa
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).