Authors
- Jhiro FaranUniversitas Nasional, Jakarta, Indonesia
- Agung TriayudiUniversitas Nasional, Jakarta, Indonesia
DOI:
https://doi.org/10.30865/klik.v4i4.1728Keywords:
Performance; Employee; Clustering; Data Mining; K-Means algorithmAbstract
Employees are people who are the main element in every organization/company. An employee is someone who can carry out work and provide the results of their work to the employer or agency where the employee works, where the results of their work are in accordance with the profession or work based on their expertise. The role of employees in cooperatives is the same as the role of employees in general in every other organization/company. Giving rewards to employees is a form of company appreciation for its employees. Reward or recognition is a form of gratitude from the company for the dedication and performance of employees, namely those who have good quality work and have met the criteria for employees with good performance. The problem faced is that currently there is no process that has been carried out to group employee performance. Grouping employee performance is a fairly important problem and must be resolved immediately by the company. The solution to this problem can be solved by paying attention to patterns based on processes or data that occurred in the past. Data mining is the right way to solve this problem. Data mining is a process of processing data and extracting data to get information back from a collection of data. Clustering is a process of grouping data contained in a dataset. Grouping data in a dataset using clustering is done based on the similarity values or characteristics of each data. The K-Means algorithm is part of clustering data mining, where the K-Means algorithm can be used to form new groups of data. The results obtained from the research are that the formation of new groups/clusters is based on a total of 15 data, so there are 2 (two) clusters where in cluster 1 there is 7 data and cluster 2 there is 8 data
Downloads
Download data is not yet available.
References
S. Regina, E. Sutinah, and N. Agustina, “Clustering Kualitas Kinerja Karyawan Pada Perusahaan Bahan Kimia Menggunakan Algoritma K-Means,” J. Media Inform. Budidarma, vol. 5, no. 2, p. 573, 2021, doi: 10.30865/mib.v5i2.2909.
A. E. Pramitasari and Y. Nataliani, “Perbandingan Clustering Karyawan Berdasarkan Nilai Kinerja Dengan Algoritma K-Means Dan Fuzzy C-Means,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 8, no. 3, pp. 1119–1132, 2021, doi: 10.35957/jatisi.v8i3.957.
G. B. Kaligis and S. Yulianto, “Analisa Perbandingan Algoritma K-Means, K-Medoids, Dan X-Means Untuk Pengelompokkan Kinerja Pegawai,” IT-Explore J. Penerapan Teknol. Inf. dan Komun., vol. 1, no. 3, pp. 179–193, 2022, doi: 10.24246/itexplore.v1i3.2022.pp179-193.
D. Aulia, M. Safii, and D. Suhendro, “Penerapan Algoritma K-Means dalam Proses Clustering Penilaian Kinerja Aparatur Sipil Negera di Sekretariat DPRD Pematangsiantar,” Jurasik (Jurnal Ris. Sist. Inf. dan Tek. Inform., vol. 6, no. 1, p. 47, 2021, doi: 10.30645/jurasik.v6i1.270.
C. P. Hematang and R. Somya, “Clustering Kualitas Kinerja Pegawai Pada Naruna Cafe & Resto Menggunakan Algoritma K-Means,” J. Pendidik. Teknol. Inf., vol. 6, no. 2, pp. 188–197, 2023, doi: 10.37792/jukanti.v6i2.960.
H. Profiliana and J. S. Wibowo, “EVALUASI KINERJA PEGAWAI NEGERI SIPIL PEMERINTAH DAERAH KABUPATEN GROBOGAN MENGGUNAKAN ALGORITMA K-MEANS,” DINAMIK, vol. 27, no. 2, pp. 45–54, 2022.
M. Veronica, H. Effendi, and A. Oktafian Saleh, “Clustering Tingkat Kedisiplinan Pegawai Pada Pengadilan Tinggi Palembang Menggunakan Algoritma K-Means,” in SEMINAR NASIONAL CORISINDO, 2023, pp. 261–266.
W. N. Purba, M. Kosasih, D. Kallamas, and ..., “Penggunaan Algoritma K–Means Clustering Untuk Menentukan Penilaian Kedisiplinan Karyawan Rumah Sakit Royal Prima,” … (Teknik Inf. dan …, vol. 6, pp. 188–195, 2023, doi: 10.37600/tekinkom.v6i1.856.
R. T. Alinse, V. N. Sari, and A. F. Sallaby, “Menentukan Pola Pegawai Honorer Di Dinas Perumahan Kawasan Pemukiman Dan Pertanahan Dalam Penerapan Metode K-Means,” J. Media Infotama, vol. 17, no. 1, pp. 47–53, 2021, doi: 10.37676/jmi.v17i1.1316.
M. A. K-means, T. Kristianda, and F. Putrawansyah, “Klasterisasi Pola Kehadiran Pegawai Institut Teknologi Pagar Alam,” in Seminar Riset Mahasiswa – Computer & Electrical (SERIMA-CE), 2023, vol. 1, no. 1.
W. Ishak Marzuki, H. Latipa Sari, and Yupianti, “Clustering Kualitas Kinerja Pegawai Pemerintah Dengan Perjanjian Kerja Pada Dinas Pekerjaan,” J. Media Infotama, vol. 19, no. 2, p. 401, 2023.
E. Arda, A. Aulia, O. Saputra, and J. Heikal, “Employee Performance Segmentation In The Public Housing Service And Payakumbuh City Residential Area With Using The K-Means Clustering Model,” J. Ekon. Dan Bisnis Digit., vol. 01, no. 03, pp. 385–389, 2024.
U. R. Amanda and D. P. Utomo, “Penerapan Data Mining Algoritma Hash Based Pada Data Pemesanan Buah Impor Cv. Green Uni Fruit,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 5, no. 1, 2021.
D. P. Indini, S. R. Siburian, and D. P. Utomo, “Implementasi Algoritma DBSCAN untuk Clustering Seleksi Penentuan Mahasiswa yang Berhak Menerima Beasiswa Yayasan,” in ESCAF, 2022, pp. 325–331.
D. P. Indini, Mesran, and Dito Putro Utomo, “Penerapan Data Mining Dalam Pengelompokan Data Reseller di Telkomsel Authorized Partner (TAP) Deli Tua Dengan Algoritma K-Means,” J. Ilm. Media Sisfo, vol. 17, no. 2, pp. 189–202, 2023, doi: 10.33998/mediasisfo.2023.17.2.1391.
I. Reisandi, D. Daryana, F. Sri Mulyati, and M. Fauzi, “Implementasi Clustering K-Means Terhadap Penilaian Kinerja Karyawan PT. XYZ,” J. Sos. Teknol., vol. 1, no. 8, pp. 757–767, 2021, doi: 10.36418/jurnalsostech.v1i8.162.
D. Murni, B. Efendi, N. Rahmadani, S. Informasi, and S. Tinggi Manajemen Informatika dan Komputer Royal Kisaran, “Implementation of Employee Discipline Clustering At Gotting Sidodadi Village Office Bandar Pasir Mandoge Using K-Means Algorithm,” J. Tek. Inform., vol. 3, no. 2, pp. 295–304, 2022, [Online]. Available: https://doi.org/10.20884/1.jutif.2022.3.2.236.
N. Rakhmawaty, N. Y. Nasution, and F. D. T. Amijaya, “Perbandingan Metode K-Means Dan Metode Fuzzy C-Means (FCM) Pada Analisis Kinerja Pegawai PT. Cemara Khatulistiwa Persada Bontang,” J. EKSPONENSIAL, vol. 13, no. 1, pp. 63–71, 2022.
A. upi Fitriyadi, “Analisis Algoritma K-Means dan K-Medoids Untuk Clustering Data Kinerja Karyawan Pada Perusahaan Perumahan Nasional,” Kilat, vol. 10, no. 1, pp. 157–168, 2021, doi: 10.33322/kilat.v10i1.1174.
P. Marpaung, I. Pebrian, and W. Putri, “Penerapan Data Mining Untuk Pengelompokan Kepadatan Penduduk Kabupaten Deli Serdang Menggunakan Algoritma K-Means,” J. Ilmu Komput. dan Sist. Inf., vol. 6, no. 2, pp. 64–70, 2023.
A. Budiono, H. Manurung, and S. Syahputra, “Penilaian Kinerja Pegawai Desa Menggunakan Algoritms K-Means Berdasarkan Index Kepuasan Masyarakat (Kantor Desa Padang Brahrang),” Semin. Nas. Inform. – 3, vol. 6, no. 3, 2022, [Online]. Available: http://www.jurnal.kaputama.ac.id/index.php/SENATIKA/article/view/1060.
A. Natalis and Y. Nataliani, “Pemanfaatan k-Means Clustering dan Analytic Hierarchy Process terhadap Penilaian Prestasi Kerja Pegawai,” J. Tek. Inform. dan Sist. Inf., vol. 8, no. 1, pp. 88–99, 2022, doi: 10.28932/jutisi.v8i1.4243.
D. Anggarwati, O. Nurdiawan, I. Ali, and D. A. Kurnia, “Penerapan Algoritma K-Means Dalam Prediksi Penjualan Karoseri,” J. Data Sci. Inform., vol. 1, no. 2, pp. 58–62, 2021.
D. F. Pasaribu, I. S. Damanik, E. Irawan, Suhada, and H. S. Tambunan, “Memanfaatkan Algoritma K-Means Dalam Memetakan Potensi Hasil Produksi Kelapa Sawit PTPN IV Marihat,” BIOS J. Teknol. Inf. dan Rekayasa Komput., vol. 2, no. 1, pp. 11–20, 2021, doi: 10.37148/bios.v2i1.17.
M. R. Alhapizi, M. Nasir, and I. Effendy, “Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Untuk Menentukan Strategi Promosi Mahasiswa Baru Universitas Bina Darma Palembang,” J. Softw. Eng. Ampera, vol. 1, no. 1, pp. 1–14, 2020, doi: 10.51519/journalsea.v1i1.10.
A. Kurniawan and U. Enri, “Clustering K-Means Kinerja Assistant Sales Representative pada PT Pupuk Kujang Cikampek, Departemen Pemasaran dan Penjualan Ritel,” J. Teknol. Inform. dan Komput., vol. 7, no. 2, pp. 13–23, 2021, doi: 10.37012/jtik.v7i2.546.
I. S. Mangku Negara, P. Purwono, and I. A. Ashari, “Analisa Cluster Data Transaksi Penjualan Minimarket Selama Pandemi Covid-19 dengan Algoritma K-means,” JOINTECS (Journal Inf. Technol. Comput. Sci., vol. 6, no. 3, p. 153, 2021, doi: 10.31328/jointecs.v6i3.2693.
S. Sahibu, R. Bambang, I. Taufik, and Agusriandi, “Penerapan Data Mining Dalam Analisis Penilaian Kinerja Pegawai Menerapkan Metode K-Means,” J. Media Inform. Budidarma, vol. 7, no. 1, pp. 22–29, 2023, doi: 10.30865/mib.v7i1.5100.
P. A. Ariawan, N. P. Sastra, and I. M. Sudarma, “Clustering Data Remunerasi PNS Menggunakan Metode K-Means Clustering Dan Local Outlier Factor,” Maj. Ilm. Teknol. Elektro, vol. 19, no. 1, p. 33, 2020, doi: 10.24843/mite.2020.v19i01.p05.
Fajar Teguh Wicaksono, Resty Wulaningrum, and Ardi Sanjaya, “Penerapan Metode K-Mean untuk Menentukan Sanksi Karyawan yang Datang Terlambat,” Nusant. Eng., vol. 4, no. 1, p. 89, 2021, doi: 10.29407/noe.v4i1.15915.
F. Marisa et al., “Digitasi Produktivitas Panen Padi Berbasis K-Means Clustering,” SMARTICS J., vol. 7, no. 1, pp. 21–26, 2021.
D. Alfatah, “Application of the K-Means Clustering Algorithm in Mapping the Regional Voter Strategy for the Legislative Candidates for the DPR RI,” J. Kom., vol. 1, no. 2, pp. 435–443, 2021, [Online]. Available: https://doi.org/10.53697/jkomitek.v1i2.
Z. N. Syarif, “Penerapan Information Gain Dan Algoritma K-Means Untuk Klasterisasi Kedisiplinan Pegawai Menggunakan Rapidminer,” TeknoIS J. Ilm. Teknol. Inf. dan Sains, vol. 13, no. 1, pp. 1–12, 2023, doi: 10.36350/jbs.v13i1.165.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan Algoritma K-Means Data Mining untuk Clustering Kinerja Karyawan Koperasi