Implementasi Metode Seleksi Fitur Untuk Penentuan Indikator pada Klasifikasi Kompetensi Pemetaan Kuadran 9 di Pemprov Jateng

Yudha Widi Harjanto; Harries Arizonia Ismail; Satrio Agung Prakoso

doi:10.55606/jitek.v5i3.8366

Authors

Yudha Widi Harjanto Universitas AKI Semarang
Harries Arizonia Ismail Universitas AKI Semarang
Satrio Agung Prakoso Universitas AKI Semarang

DOI:

https://doi.org/10.55606/jitek.v5i3.8366

Keywords:

Data Mining, Quadrant 9 Competency Classification, Feature Selection, Naive Bayes , Forward Selection

Abstract

Quadrant 9 (nine) is a model of employee competency mapping which will be used as a strategy for developing talented individuals and individuals who have weaknesses. In addition, quadrant 9 (nine) is also used in succession planning by identifying anyone who has the potential to become a leader in the organization. This study aims to obtain a data mining algorithm with a more accurate selection feature to find indicators that affect the classification of quadrant 9 competencies. The Forward Selection feature selection algorithm based on Naive Bayes is proven to be accurate and effective in determining the most influential attributes, namely IQ/Thinking Capacity, Self-Management, Social Communication, Service Orientation, Decision Making, Managing Others with 79.10% accuracy results and is included in the "Good Kappa" category.

References

[1] Badan Kepegawaian Daerah, [Online]. Available: https://bkd.jatengprov.go.id/.

[2] Peraturan Presiden, "Peraturan Presiden Nomor 81 Tahun 2010 tentang Grand Design Reformasi Birokrasi 2010-2015 Republik Indonesia," 2010.

[3] Kementerian Pendayagunaan Aparatur Negara dan Reformasi Birokrasi, "Permenpan RB Nomor 3 Tahun 2020".

[4] Kementerian Pendayagunaan Aparatur Negara, "Surat Edaran Menpan Nomor 16 Tahun 2012 tentang tata cara pengisian jabatan struktural yang lowong secara terbuka di lingkungan instansi pemerintah," 2012.

[5] F. Gorunescu, Data Mining: Concepts, Model and Techniques, vol. 12, P. J. Kacprzyk and P. L. C. Jain, Eds., Berlin, Jerman: Springer, 2011.

[6] Han, Data Mining Concept And Technique, 3rd Edition ed., A. Stephan, Ed., Champaign: Multiscience Press, 2012.

[7] O. Maimon and L. Rokach, Data Mining and Knowledge Discovery, 2nd, Springer, 2010.

[8] I. H. Witten, f. Eibe and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed ed., A. Stephan and Burlington, Eds., United States of America: Morgan Kaufmann, 2011.

[9] X. Wu and V. Kumar, The top ten Algorithms in Data Mining, Taylor & Francis Group, LLC, 2009.