HYBRID PSO K-MEANS AND ROBUST SPARSE K-MEANS FOR EMPLOYEE STUDY DECISIONS
DOI:
https://doi.org/10.33480/jitk.v11i3.7101Keywords:
Clustering, Data Mining, Distance Metric, K-Means, OptimizationAbstract
Human Resources (HR) are a strategic asset in institutional advancement, so employee performance evaluation must be conducted objectively and based on data. This study aims to cluster employee performance data at XYZ University for determining further studies, using the K-Means, PSO K-Means, and Robust Sparse K-Means methods, as well as three types of distance measurements: Euclidean, Manhattan, and Mahalanobis Distance. The dataset consists of 17 attributes. The evaluation was conducted using the Silhouette Score, Davies-Bouldin Index, and visualization using PCA. The results indicate that the combination of PSO K-Means with Euclidean Distance provides the best balance between quantitative performance (Silhouette Score 0.1253 and DBI 2.0521) and a more visually representative distribution of cluster members. The interpretation of the clustering results yielded three clusters: Cluster 0 (no further study) consisting of 8 employees, Cluster 1 (further study) consisting of 97 employees, and Cluster 2 (awaiting study decision) consisting of 58 employees. These findings can be utilized by institutions to design more targeted and data-driven human resource development strategies.
Downloads
References
[1] Z. M. Fadhil, “Hybrid of K-means clustering and naive Bayes classifier for predicting performance of an employee,” Period. Eng. Nat. Sci., vol. 9, no. 2, pp. 799–807, 2021, doi: 10.21533/pen.v9i2.1898.
[2] Y. C. Fadilah, A. Sani, A. Andrianingsih, and U. Nasional, “APPLYING K-MEANS CLUSTERING FOR GROUPING PAPUA ’ S DISTRICTS,” vol. 10, no. 3, pp. 543–553, 2025, doi: 10.33480/jitk.v10i3.5865.APPLYING.
[3] M. I. C. Rachmatullah, “Proposed Modification of K-Means Clustering Algorithm with Distance Calculation Based on Correlation,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 8, no. 1, p. 136, 2022, doi: 10.26555/jiteki.v8i1.23696.
[4] A. N. Afifah, V. Nurcahyawati, and V. R. Hananto, “Analisis Clustering dan Pemetaan Sebaran Pelanggan Perusahaan Properti di Sidoarjo,” J. Edukasi dan Penelit. Inform., vol. 9, no. 3, p. 502, 2023, doi: 10.26418/jp.v9i3.67935.
[5] G. W. Hamaali, K. A. Abduljabbar, and D. R. Sulaiman, “K-means Clustering and PSO Algorithm for Wireless Sensor Networks Optimization,” Univ. Thi-Qar J. Eng. Sci., vol. 13, no. 1, pp. 40–50, 2023, doi: 10.31663/tqujes13.1.457(2023).
[6] S. Juanita and R. D. Cahyono, “K-Means Clustering With Comparison of Elbow and Silhouette Methods for Medicines Clustering Based on User Reviews,” J. Tek. Inform., vol. 5, no. 1, pp. 283–289, 2024, [Online]. Available: https://doi.org/10.52436/1.jutif.2024.5.1.1349
[7] C. Fan, “Evaluating Employee Performance with an Improved Clustering Algorithm,” Inform., vol. 46, no. 5, pp. 123–128, 2022, doi: 10.31449/inf.v46i5.4079.
[8] H. Amdouni, G. Manita, D. Oliva, E. H. Houssein, O. Korbaa, and S. Zapotecas-Martínez, “Dynamic Social Particle Swarm Optimization For Automatic Clustering,” Procedia Comput. Sci., vol. 246, no. C, pp. 1409–1418, 2024, doi: 10.1016/j.procs.2024.09.583.
[9] I. M. S. Bimantara and I. M. Widiartha, “Optimization of K-Means Clustering Using Particle Swarm Optimization Algorithm for Grouping Traveler Reviews Data on Tripadvisor Sites,” J. Ilm. Kursor, vol. 12, no. 1, pp. 1–10, 2023, doi: 10.21107/kursor.v12i01.269.
[10] M. Qi, J. Q. Zhao, and Y. Feng, “An optimized public opinion communication system in social media networks based on K-means cluster analysis,” Heliyon, vol. 10, no. 24, p. e40033, 2024, doi: 10.1016/j.heliyon.2024.e40033.
[11] Y. S. Waode, A. Kurnia, and Y. Angraini, “K-Means Optimization Algorithm to Improve Cluster Quality on Sparse Data,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 23, no. 3, pp. 641–652, 2024, doi: 10.30812/matrik.v23i3.3936.
[12] F. Akhmatshin, P. Egarmin, M. Gerasimova, I. Petrova, and S. Mikitchak, “Clustering of k-means based on Euclidean distance metric and Mahalanobis metric,” E3S Web Conf., vol. 531, pp. 0–5, 2024, doi: 10.1051/e3sconf/202453103002.
[13] P. Kumari and S. Gupta, “Comparative analysis between Euclidean distance metric and Mahalanobis Distance Metric,” vol. 12, no. 2, 2024.
[14] R. P. Permata, “Optimizing K-Means Clustering through Distance Metric Simulation for Strategic Enrollment Segmentation in Private Universities,” vol. 10, no. 2, pp. 616–629, 2025.
[15] R. Buaton et al., “COMPARATIVE EVALUATING NUMERICAL MEASURE VARIATIONS IN K- MEDOIDS CLUSTERING FOR EFFECTIVE DATA GROUPING,” vol. 10, no. 2, pp. 394–403, 2024, doi: 10.33480/jitk.v10i2.5545.INTRODUCTION.
[16] A. M. P. Swarm and O. Approach, “A Multi-Objective Particle Swarm Optimization Approach,” vol. 9, no. 3, pp. 542–550, 2025.
[17] H. Yue, H. Zhang, and Y. Dai, “Application of PSO-integrated K-means algorithm in resident digital portrait classification,” pp. 1–15, 2025, doi: 10.1371/journal.pone.0329123.
[18] M. Mahajan, S. Kumar, and B. Pant, “Prediction of Environmental Pollution Using Hybrid PSO-K-Means Approach,” vol. 12, no. 2, pp. 65–76, 2021, doi: 10.4018/IJEHMC.2021030104.
[19] A. Penerapan, F. Slovin, D. A. N. Kesalahan, and D. Perspektif, “Jurnal multidisiplin sosisal humaniora,” vol. 1, pp. 53–63, 2024.
[20] T. Third, I. Conference, L. Ganasih, R. Rama, and R. Aswin, “The Influence Of Motivation , Perceptions And Attitudes On Student Decisions In Choosing A Major ( Study Of Management Students , Faculty Of Economics And Business , University Of Riau ),” 2023.
[21] N. A. Maori and E. Evanita, “Metode Elbow dalam Optimasi Jumlah Cluster pada K-Means Clustering,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 14, no. 2, pp. 277–288, 2023, doi: 10.24176/simet.v14i2.9630.
[22] Baiq Nikum Yulisasih, H. Herman, and S. Sunardi, “K-Means Clustering Method For Customer Segmentation Based On Potential Purchases,” J. ELTIKOM, vol. 8, no. 1, pp. 83–90, 2024, doi: 10.31961/eltikom.v8i1.1137.
[23] N. L. Ratniasih and R. A. N. Diaz, “Comparison of Clustering Algorithm in Employee Training Management Recommendations,” 3rd Int. Conf. Cybern. Intell. Syst. ICORIS 2021, pp. 5–8, 2021, doi: 10.1109/ICORIS52787.2021.9649503.
[24] N. Rahmadani, E. Rahayu, and A. Lestari, “K-Means Clustering Areas Prone To Traffic Accidents in Asahan Regency,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 6, no. 2, pp. 181–186, 2021, doi: 10.33480/jitk.v6i2.1519.K-MEANS.
[25] A. B. Madjoukeng and B. Edith, “Robust Sparse k-means Clustering Against STOF Observations Robust Sparse k -means Clustering Against STOF Observations,” no. March, 2025, doi: 10.36227/techrxiv.174114537.78354861/v1.
[26] M. Muhajir and D. Rosadi, “Robust clustering using sparse K means on text analitycs of PPKM policy in Indonesia,” AIP Conf. Proc., vol. 2720, no. December, 2023, doi: 10.1063/5.0137220.
[27] T. M. Ghazal et al., “Performances of k-means clustering algorithm with different distance metrics,” Intell. Autom. Soft Comput., vol. 30, no. 2, pp. 735–742, 2021, doi: 10.32604/iasc.2021.019067.
[28] W. A. Prastyabudi, A. N. Alifah, and A. Nurdin, “Segmenting the Higher Education Market: An Analysis of Admissions Data Using K-Means Clustering,” Procedia Comput. Sci., vol. 234, no. 2023, pp. 96–105, 2024, doi: 10.1016/j.procs.2024.02.156.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Luh Dwi Ari Sudawati, Roy Rudolf Huizen, Dandy Pramana Hostiadi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
