Enhancing Decision-Making in Local Government through K-Means Clustering of Structural Official’s Performance
Abstract
Employee performance evaluation is a critical process in public sector management. However, in Karanganyar Regency, this process has been traditionally conducted on an individual basis, leading to inefficiencies and a lack of actionable insights. This study addresses the gap by applying the K-Means clustering algorithm to categorize the performance of structural officials based on 2021 Employee Performance Target (SKP) data. Key performance indicators include SKP Value, Service Orientation, Commitment, Cooperation, Leadership, and Work Initiative. Using RapidMiner, the data was clustered into three categories: “very good,” “good,” and “satisfactory.” The clustering quality was validated using the Davies-Bouldin Index (DBI), achieving an optimal value of 0.113, which indicates high intra-cluster similarity. The results provide a data- driven foundation for more efficient performance assessments, aiding decision-making in promotions and personnel management. This study demonstrates the potential of machine learning, specifically K-Means clustering, in improving administrative processes and strategic decision-making within local government.
References
Y. Dawanaka, W. Waworundeng, and A. Purwanto, “Employee Performance in Improving Population Administration Services in the Population and Civil Registration Office of East Halmahera Regency,” J. La Bisecoman, vol. 3, no. 6, pp. 271–277, 2023, doi: 10.37899/journallabisecoman.v3i6.928.
D. Podungge, I. Mashudi, and K. Napu, “Analysis of Performance Assessment System Model of Civil Servants in Gorontalo Province Training and Education Agency,” J. La Soc., vol. 1, no. 4, pp. 27–32, 2020, doi: 10.37899/journal-la-sociale.v1i4.143.
P. Henman, “Improving public services using artificial intelligence: possibilities, pitfalls, governance,” Asia Pacific J. Public Adm., vol. 42, no. 4, pp. 209–221, 2020, doi: 10.1080/23276665.2020.1816188.
C. van Noordt and G. Misuraca, “Artificial intelligence for the public sector: results of landscaping the use of AI in government across the European Union,” Gov. Inf. Q., vol. 39, no. 3, p. 101714, Jul. 2022, doi: 10.1016/j.giq.2022.101714.
I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Comput. Sci., vol. 2, no. 3, p. 160, May 2021, doi: 10.1007/s42979-021-00592-x.
A. Moubayed, M. Injadat, A. Shami, and H. Lutfiyya, “Student Engagement Level in an e-Learning Environment: Clustering Using K-means,” Am. J. Distance Educ., vol. 34, no. 2, pp. 137–156, 2020, doi: 10.1080/08923647.2020.1696140.
M. Mala Khairunnisa, A. Triayudi, and E. Tri Esti Handayani, “Application of K-Means Clustering on the Performance Evaluation of Lecturers Based on Student Questionnaire,” J. Mantik, vol. 4, no. 1, pp. 760–766, 2020, [Online]. Available: https://iocscience.org/ejournal/index.php/mantik
A. A. Aldino, D. Darwis, A. T. Prastowo, and C. Sujana, “Implementation of K-Means Algorithm for Clustering Corn Planting Feasibility Area in South Lampung Regency,” J. Phys. Conf. Ser., vol. 1751, no. 1, 2021, doi: 10.1088/1742-6596/1751/1/012038.
R. Vankayalapati, K. B. Ghutugade, R. Vannapuram, and B. P. S. Prasanna, “K-means algorithm for clustering of learners performance levels using machine learning techniques,” Rev. d’Intelligence Artif., vol. 35, no. 1, pp. 99–104, 2021, doi: 10.18280/ria.350112.
D. Abdullah, S. Susilo, A. S. Ahmar, R. Rusli, and R. Hidayat, “The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data,” Qual. Quant., vol. 56, no. 3, pp. 1283–1291, 2022, doi: 10.1007/s11135-021-01176-w.
P. Anitha and M. M. Patil, “RFM model for customer purchase behavior using K-Means algorithm,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 5, pp. 1785–1792, 2022, doi: 10.1016/j.jksuci.2019.12.011.
Z. Hou, R. Yan, and S. Wang, “On the K-Means Clustering Model for Performance Enhancement of Port State Control,” J. Mar. Sci. Eng., vol. 10, no. 11, 2022, doi: 10.3390/jmse10111608.
B. Firmansyah and U. Chotijah, “Implementation of The K-Means Clustering for Teacher Performance Assessment Grouping (PKG) at MI Bani Hasyim Cerme,” J. Ris. Inform., vol. 5, no. 1, pp. 499–506, 2022, doi: 10.34288/jri.v5i1.475.
D. A. Tarigan, “Optimization of the K-Means Clustering Algorithm Using Davies Bouldin Index in Iris Data Classification,” Media Online), vol. 4, no. 1, pp. 545–552, 2023, doi: 10.30865/klik.v4i1.964.
A. Idrus, N. Tarihoran, U. Supriatna, A. Tohir, S. Suwarni, and R. Rahim, “Distance Analysis Measuring for Clustering using K-Means and Davies Bouldin Index Algorithm,” TEM J., vol. 11, no. 4, pp. 1871–1876, 2022, doi: 10.18421/TEM114-55.
R. Oktarina and Junita, “Determine the clustering of cities in Indonesia for disaster management using K-Means by excel and RapidMiner,” IOP Conf. Ser. Earth Environ. Sci., vol. 794, no. 1, 2021, doi: 10.1088/1755-1315/794/1/012094.
