Employee Performance Classification Using K-Nearest Neighbor and Random Forest with Work Behavior Scenario Simulation
DOI:
https://doi.org/10.65230/jitcos.v2i1.83Keywords:
Employee Performance, Machine Learning, KNN, Random Forest, HR AnalyticsAbstract
Employee performance evaluation is a critical aspect of human resource management, directly influencing productivity, promotion decisions, and career development planning. Traditional approaches often fail to capture hidden patterns in complex employee data, making machine learning (ML) a promising solution for more accurate and objective classification. This study aims to model and compare two ML algorithms, K-Nearest Neighbor (KNN) and Random Forest, for employee performance classification, followed by scenario-based simulation using the best-performing model. The research employed a quantitative computational approach with a dataset of 100,000 employee records and 20 features. Preprocessing steps included feature selection, binarization of performance scores, handling class imbalance using Synthetic Minority Over-sampling Technique (SMOTE), and feature engineering to enrich data representation. The dataset was split into 80% training and 20% testing. KNN was first built as a baseline model, then optimized through hyperparameter tuning using GridSearchCV with cross-validation. Random Forest was implemented with 100 decision trees and bootstrap sampling to enhance accuracy and stability. Results show that the tuned KNN model achieved an accuracy of 70.88%, improving from 63.85% baseline. However, Random Forest outperformed KNN significantly, reaching 97.17% accuracy with lower error rates. Scenario simulations using Random Forest demonstrated practical applicability in predicting employee performance based on work behavior profiles. In conclusion, Random Forest provides a more robust and reliable model for employee performance classification compared to KNN. The study contributes by integrating algorithm comparison with simulation design, offering actionable insights for human resource decision-making. Future research may explore additional ensemble methods and real-time evaluation frameworks.
References
Akbar, Fahmi Malik Namus. (2024). “Metode knn ( k-nearest neighbor ) untuk menentukan kualitas air.” Jurnal tekno kompak 18(1):28-40. doi: 10.33365/jtk.v18i1.3241.
Azmi, Azel Fabian, and Apriade Voutama. (2024). “Prediksi churn nasabah bank menggunakan klasifikasi random forest dan decision tree dengan evaluasi confusion matrix.” KOMPUTA : Jurnal ilmiah komputer dan informatika 13(1):111–19. doi: https://doi.org/10.34010/komputa.v13i1.12639.
Azmi, F., Gibran, M. K., & Saleh, A. (2025). Herbal Plant Image Retrieval Using HSV Color Histogram and Random Forest Algorithm. Journal of Computer Science, Information Technology and Telecommunication Engineering, 6(2), 1019-1027. https://doi.org/10.30596/jcositte.v6i2.26495
Cahyani, Gita Indah. (2023). “Pengaruh manajemen evaluasi kinerja karyawan terhadap produktivitas perusahaan.” Karimah tauhid 2(5):1708–13. doi: https://doi.org/10.30997/karimahtauhid.v2i5.9441
Diana, Rosita, Heri Warni, and Tata Sutabri. (2023). “Penggunaan teknologi machine learning untuk pelayanan monitoring kegiatan belajar mengajar pada smk bina sriwijaya palembang.” Jurnal teknik informatika (JUTEKIN) 11(1):203–10. doi: https://dx.doi.org/10.51530/jutekin.v11i1.709.
Diansyah, Surya. (2022). “Klasifikasi tingkat kepuasan pengguna dengan menggunakan metode k-nearest neighbour ( KNN ).” Jurnal sistim informasi dan teknologi 4(1):7–12. doi: 10.37034/jsisfotek.v4i1.114.
Fadhlurrahman, M. Nabil, and Mutiara Yudina Fitrah. (2025). “Optimizing amazon reviews using principal component analysis , feature selection on random forest classifier.” Media journal of general computer science 2(1):12–17. doi: 10.62205/ mjgcs.v2i1.35.
Fauzi, Ade Rifal E. R. R., and Ika Nur LailyFitriana. (2025). “Klasifikasi sekuensial untuk pengelompokan data absensi karyawan menggunakan algoritma decision tree dan feature engineering.” Prosiding seminar nasional sains dan teknologi seri iii 2(1):457–70. doi: https://conference.ut.ac.id/index.php/saintek/article/view/5004/1895.
Hermawan, Fani Fadillah, and Yuni Yamasari. (2022). “Implementasi k-nearest neighbor dengan pemilihan fitur pada aplikasi prediksi kelayakan pengajuan pinjaman.” JINACS 3(4):411–24. doi: https://doi.org/10.26740/jinacs.v3n04.p411-424
Husen, Dede, Dede Sandi, Sepriadi Bumbungan, Kusrini, and Kusnawi. (2022). “Analisis prediksi kebakaran hutan dengan menggunakan algoritma random forest classifier.” Jurnal nuansa informatika 16(1):150–55. doi: https://doi.org/10.25134/nuansa.v16i1.5392.
Kumari, Saloni, Deepika Kumar, and Mamta Mittal. 2021. “An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier.” International journal of cognitive computing in engineering 2(January):40–46. doi: 10.1016/j.ijcce.2021.01.001.
Kusuma, Satya Tegar, and Theopilus Bayu Sasongko. (2023). “Optimasi k-nearest neighbor dengan grid search cv pada prediksi kanker paru-paru.” Indonesian journal of computer science 12(4):2162–71. doi: https://doi.org/10.33022/ijcs.v12i4.3267.
Mansur, Mohammad Ripan Saiful. (2023). “Restorasi wajah pada kamera cctv dengan pendekatan metode gan untuk memperbaiki piksel pada wajah.” Universitas Komputer Indonesia. doi: http://elibrary.unikom.ac.id/id/eprint/9006
Nalatissifa, Hiya, Windu Gata, Sri Diantika, and Khoirun Nisa. (2021). “Perbandingan kinerja algoritma klasifikasi naive bayes , support vector machine ( SVM ), dan random forest untuk prediksi ketidakhadiran di tempat kerja.” Jurnal informatika universitas pamulang 5(4):578–84. doi: 10.32493/informatika.v5i4.7575.
Nuraeni, Fitri, Dede Kurniadi, and Moch Haiqal Diazki. (2024). “Algoritma k-nearest neighbor pada kasus dataset imbalanced untuk klasifikasi kinerja karyawan perusahaan.” Jurnal teknologi informasi dan ilmu komputer (JTIIK) 11(3):557–68. doi: 10.25126/jtiik.938144.
Patil, J. K., and P. M. Jadhav. (2023). “Predicting hr churn with python and machine learning.” Journal of advanced zoology 44(8):164–72. doi: https://doi.org/10.53555/jaz.v44iS8.3526.
Pulungan, Mutiara Persada, Andi Purnomo, and Aliyah Kurniasih. (2024). “Penerapan smote untuk mengatasi imbalance class dalam klasifikasi kepribadian mbti menggunakan naive bayes classifier.” Jurnal teknologi informasi dan ilmu komputer (JTIIK) 11(5):1033–42. doi: 10.25126/jtiik.2024117989.
Risky, T. T. A., Faiza, N., Hasibuan, M. F. H., & Nasution, M. S. R. (2025). Modeling and Simulation of Indoor Temperature Dynamics Using Random Forest and Multi-Layer Perceptron Methods. JITCoS : Journal of Information Technology and Computer System, 1(2), 64-73. https://doi.org/10.65230/jitcos.v1i2.39
Rahmadeyan, Akhas, and Mustakim. (2023). “Seleksi fitur pada supervised learning : klasifikasi prestasi belajar mahasiswa saat dan pasca pandemi COVID-19.” Jurnal nasional teknologi dan sistem informasi 9(1):21–32. doi: https://doi.org/10.25077/TEKNOSI.v9i1.2023.21-32.
Said, Hardiana, Nurhafifah Matondang, and Helena Nurramdhani Irmanda. (2022). “Penerapan algoritma k-nearest neighbor untuk memprediksi kualitas air yang dapat dikonsumsi.” Techno.COM 21(2):256–67. doi: https://doi.org/10.33633/tc.v21i2.5901.
Sudrajat, Dadang, Ade Irma Purnamasari, Arif Rinaldi, Dian Ade Kurnia, and Agus Bahtiar. (2022). “Klasifikasi mutu pembelajaran hybrid berdasarkan algoritma c . 45 , random forest dan naïve bayes dengan optimasi bootsrap areggating ( bagging ) pada masa covid-19.” JURIKOM (Jurnal riset komputer) 9(6):2227–33. doi: 10.30865/jurikom.v9i6.5179.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Hafiz Aryanda, Alwi Syahputra, Muhammad Farhan, Ade Aulia Dharma (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Focus and Scope 
Editorial Team 
Reviewer 
Peer Review Process 
Publication Ethics 
Author Guidelines 
Copyright Notice 
Open Access Policy 
Plagiarism Check 
Ref. Management 
Author Fees
Copyright Transfer Agreement
