Optimalisasi Kinerja Karyawan Berbasis HR Analytics dengan K-Means Clustering dan Analisis Faktor Demografi

Authors

  • Anandita Nabilla Ramadhani Program Studi Sistem Informasi, Fakultas Ilmu Komputer, Universitas Sriwijaya
  • Ghita Athalina Program Studi Sistem Informasi, Fakultas Ilmu Komputer, Universitas Sriwijaya

DOI:

https://doi.org/10.33020/saintekom.v15i1.779

Keywords:

data-driven HR management, k-means clustering, employee performance, demographic factors

Abstract

Data-driven Human Resources (HR) management is an important aspect in improving organizational productivity and efficiency in the digital era. This research aims to cluster employees based on performance using the K-Means Clustering algorithm and evaluate the influence of demographic factors on job performance. The dataset used is a public dataset from Kaggle, including employee performance information such as Key Performance Indicators (KPIs), training scores, multiple trainings, performance appraisals, awards, as well as demographic attributes such as gender, age, education level, and recruitment channels. Using the six-stage CRISP-DM framework, the data was processed using StandardScaler, and the optimal number of clusters was determined through the Elbow Method, Davies-Bouldin Index, and Silhouette Score, resulting in two main clusters. Cluster 0 includes high-performing employees with KPIs above 80%, good performance ratings, and good training scores, while Cluster 1 consists of low-performing employees, with lower KPIs, poor performance ratings, and training scores. Analysis showed demographic factors did not significantly affect employee performance. This research recommends focused training for low-performing employees and rewards for high-performing employees, so that each employee can reach their full potential and support organizational productivity.

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References

Alga Vredizon, P., Firmansyah, H., Shafira Salsabila, N., & Eko Nugroho, W. (2024). Penerapan Algoritma K-Means Untuk Mengelompokkan Makanan Berdasarkan Nilai Nutrisi. Journal of Technology and Informatics (JoTI), 5(2), 108–115. https://doi.org/10.37802/joti.v5i2.577

Ari Sulistiyawati, & Eko Supriyanto. (2021). Implementasi Algoritma K-means Clustring dalam Penetuan Siswa Kelas Unggulan. 15(2). https://doi.org/https://doi.org/10.33365/jtk.v15i2.1162

Dhewayani, F. N., Amelia, D., Alifah, D. N., Sari, B. N., & Jajuli, M. (2022). Implementasi K-Means Clustering untuk Pengelompokkan Daerah Rawan Bencana Kebakaran Menggunakan Model CRISP-DM. Jurnal Teknologi Dan Informasi. https://doi.org/10.34010/jati.v12i1

Dini Fadilla, & Mike Triani. (2024). Analisis Determinan Produktivitas Tenaga Kerja Di Indonesia. https://doi.org/10.24036/ecosains.13122857.00

Faran, J., & Triayudi, A. (2024). KLIK: Kajian Ilmiah Informatika dan Komputer Penerapan Algoritma K-Means Data Mining untuk Clustering Kinerja Karyawan Koperasi. Media Online, 4(4). https://doi.org/10.30865/klik.v4i4.1728

Gideon Bartolomeus Kaligis, & Sri Yulianto. (2022). Analisa Perbandingan Algoritma K-Means, K-Medoids, dan X-Means untuk Pengelompokkan Kinerja Pegawai (Studi Kasus: Sekretariat DPRD Provinsi Sulawesi Utara). https://doi.org/10.24246/itexplore.v1i3.2022.pp179-193

Hendrastuty, N. (2024). Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa. Jurnal Ilmiah Informatika Dan Ilmu Komputer (JIMA-ILKOM), 3(1), 46–56. https://doi.org/10.58602/jima-ilkom.v3i1.26

Hidayat, R. S., Muttaqin, M. R., & Irmayanti, D. (2024). PENGELOMPOKAN DAERAH RAWAN BENCANA DI JAWA TENGAH MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING. In Jurnal Mahasiswa Teknik Informatika (Vol. 8, Issue 5). https://doi.org/https://doi.org/10.36040/jati.v8i5.10880

Indhitya Padiku, & Agus Lahinta. (2022). Penerapan Clustering K-Means Untuk Mendukung Pengelolaan Koleksi Pada Perpustakaan Fakultas Teknik Universitas Negeri Gorontalo. Jurnal Teknik, 20(1), 54–62. https://doi.org/https://doi.org/10.37031/jt.v20i1.206

Indra Reisandi, Daryana, Fatimah Sri Mulyati, & Muchammad Fauzi. (2021). IMPLEMENTASI CLUSTERINGK-MEANSTERHADAP PENILAIAN KINERJA KARYAWAN PT.XYZ. https://doi.org/10.59188/jurnalsostech.v1i8.162

International Labour Organization. (2020). Program Pekerjaan Layak Nasional (DWCP) untuk Indonesia 2020-2025. https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@asia/@ro-bangkok/@ilo-jakarta/documents/publication/wcms_757814.pdf

Ni Putu Ayu Widiari, I I Made Agus Dwi Suarjaya, & Dwi Putra Githa. (2020). Teknik Data Cleaning Menggunakan Snowflake untuk Studi Kasus Objek Pariwisata di Bali. https://doi.org/10.24843/JIM.2020.v08.i02.p07

Sudaryanto. (2024). Peran KPI Dalam Mendorong Kinerja Karyawan Dan Produktivitas Organisasi. Bisnis Dan Digital (JIMaKeBiDi), 1(2). https://doi.org/https://doi.org/10.61132/jimakebidi.v1i2.67

Teddy Dirgantara, & Rokhedi Priyo Santoso. (2024). Sosiodemografis dan rata-rata produktivitas tenaga kerja di Indonesia. Jurnal Kebijakan Ekonomi Dan Keuangan, 97–108. https://doi.org/10.20885/jkek.vol3.iss1.art13

Tresi Aprilia. (2024). Klasifikasi Kanker Payudara Menggunakan Algoritma K-Nearest Neighbor dan Metode Naive Bayes. 4(2), 156–163. https://doi.org/10.54259/satesi.v4i2.3167

Umagapi, I. T., Umaternate, B., Komputer, S., Pasca Sarjana Universitas Handayani, P., Kepegawaian Daerah Kabupaten Pulau Morotai, B., & Riset dan Inovasi, B. (2023). Uji Kinerja K-Means Clustering Menggunakan Davies-Bouldin Index Pada Pengelompokan Data Prestasi Siswa. https://doi.org/https://doi.org/10.36040/jati.v8i2.9005

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Published

31-03-2025

How to Cite

Ramadhani, Anandita Nabilla, and Ghita Athalina. 2025. “Optimalisasi Kinerja Karyawan Berbasis HR Analytics Dengan K-Means Clustering Dan Analisis Faktor Demografi”. Jurnal Saintekom : Sains, Teknologi, Komputer Dan Manajemen 15 (1):1-14. https://doi.org/10.33020/saintekom.v15i1.779.

Issue

Vol. 15 No. 1 (2025): Maret 2025

Section

Articles

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Copyright (c) 2025 Anandita Nabilla Ramadhani, Ghita Athalina

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