DATA MINING ANALYSIS TO DETERMINE EMPLOYEE SALARIES ACCORDING TO NEEDS BASED ON THE K-MEDOIDS CLUSTERING ALGORITHM

Alia Ahadi Argasah; Dudih  Gustian

doi:10.20884/1.jutif.2022.3.1.122

Alia Ahadi Argasah Sistem informasi , Fakultas Teknologi Informasi dan Komputer, Universitas Nusa Putra, Indonesia
Dudih Gustian Sistem informasi , Fakultas Teknologi Informasi dan Komputer, Universitas Nusa Putra, Indonesia

DOI: https://doi.org/10.20884/1.jutif.2022.3.1.122

Keywords: Clustering, Data Mining, K-Medoids, Employee, Salary

Abstract

A company to achieve its goals, one of the factors is the performance of employees according to company standards, employees will provide performance with company standards with the company's reciprocity on employees for example in the payroll aspect. The purpose of this research is to help companies with appropriate payroll so that it has a good impact on productivity, garment companies that produce various types of clothing require employees with sewing skills which are one of the most important aspects of production. The problem is in the production process that is hampered, one of the factors for decreasing employee performance is the incompatibility of salary with the abilities of employees. The k-medoids clustering method can help companies cluster employees according to the employee's ability value as a benchmark for wages, from 50 samples of employees and an assessment of the various skills possessed by employees so that the calculation results in the first cluster of 24 employees with a salary received Rp 100,000 per day, the second cluster the number of 16 employees with a salary received Rp 90,000 per day, the third cluster of 10 employees with a salary received Rp 80,000 per day. So it can be concluded that the clustering method can help companies with the right targets for grouping employee salaries according to the employee's abilities so that company productivity is not disturbed by declining employee performance or employee complaints over the incompatibility of payroll with employee abilities

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References

M. Gandung and Suwanto, “Analisis Pengaruh Kompensasi Dan Gaya Kepemimpinan Terhadap Kinerja Karyawan Pada PT. Surya Rasa Loka Jaya Di Jakarta Barat,” J. Ilmiah, Manaj. Sumber Daya Mns., vol. 3, no. 3, pp. 236–245, 2020, doi: http://www.openjournal.unpam.ac.id/index.php/JJSDM/article/viewFile/4861/3530.

R. Dewi, B. Givan, and H. Wiinarno, “Pelaksanaan Rekrutmen, Seleksi dan Penempatan Kerja Karyawan (Studi pada Karyawan PT Gemala Kempa Daya),” J. Adm. Bisnis, vol. 1, no. 1, pp. 49–55, 2021, [Online]. Available: www.igpgroup.co.id/lamaran.

S. Septiana and O. H. Widjaja, “Faktor-Faktor yang Mempengaruhi Kinerja Karyawan pada PT. Jocelyn Anugrah Jaya,” J. Manajerial Dan Kewirausahaan, vol. 2, no. 3, p. 643, 2020, doi: 10.24912/jmk.v2i3.9576.

S. Anggun and E. D. Sikumbang, “No Title K-Means Clustering dalam penerimaan karyawan baru,” Data Min., vol. 2, no. 1, pp. 103–112, 2020.

N. Y. S. Munti, G. W. Nurcahyo, and J. Santony, “Analisis Dan Penerapan Data Mining Untuk Menentukan Gaji Karyawan Tetap Dan Karyawan Kontrak Menggunakan Algoritma K-Means Clustering ( Studi Kasus Di Pt Indomex Dwijaya Lestari ),” JITI, Vol. 1, No. 1, Maret 2018, 2018.

Y. Intishar and . M., “Analisis Penerapan Sistem Informasi Akuntansi Penggajian Dalam Menunjang Efektivitas Pengendalian Internal Penggajian,” J. Ilm. Akunt. Kesatuan, vol. 6, no. 2, pp. 094–103, 2018, doi: 10.37641/jiakes.v6i2.136.

Y. Asriningtias and R. Mardhiyah, “Aplikasi Data Mining Untuk Menampilkan Informasi,” Informatika, vol. 8, no. 1, pp. 837–848, 2014.

S. Haryati, A. Sudarsono, and E. Suryana, “Implementasi Data Mining Untuk Memprediksi Masa Studi Mahasiswa Menggunakan Algoritma C4.5 (Studi Kasus: Universitas Dehasen Bengkulu),” J. Media Infotama, vol. 11, no. 2, pp. 130–138, 2015.

T. Syahputra, J. Halim, and E. P. Sintho, “Penerapan Data Mining Dalam Menentukan Pilihan Jurusan Bidang Studi SMA Menggunakan Metode,” Penerapan Data Min. dalam Menentukan Pilihan Jur. di Bid. Stud. SMA menggunakan Metod. Clust. Dengan Tek. Single Link. JURTEKSI, vol. IV, no. 2, pp. 1–4, 2018.

S. Sindi, W. R. O. Ningse, I. A. Sihombing, F. Ilmi R.H.Zer, and D. Hartama, “Analisis algoritma K-Medoids clustering dalam pengelompokan penyebaran Covid-19 di Indonesia,” Jti (Jurnal Teknol. Informasi), vol. 4, no. 1, pp. 166–173, 2020, [Online]. Available: http://www.jurnal.una.ac.id/index.php/jurti/article/view/1296.

V. A. P. Sangga, “Perbandingan Algoritma K-Means dan Algoritma K-Medoids dalam Pengelompokan Komoditas Peternakan di Provinsi Jawa Tengah Tahun 2015,” Tugas Akhir Jur. Stat. Fak. Mat. dan Ilmu Pengetah. Alam Univ. Islam Inndonesia Yogyakarta, vol. 53, no. 9, pp. 1689–1699, 2018.

A. D. A. N. Pembahasan, “PENGKLASTERAN GAJI KARYAWAN PADA PT . ERBA PRIMAS BOGOR,” vol. 4, pp. 395–402, 2020, doi: 10.30865/komik.v4i1.2852.

I. Kamila, U. Khairunnisa, and M. Mustakim, “Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Data Transaksi Bongkar Muat di Provinsi Riau,” J. Ilm. Rekayasa dan Manaj. Sist. Inf., vol. 5, no. 1, p. 119, 2019, doi: 10.24014/rmsi.v5i1.7381.

D. Marlina, N. Lina, A. Fernando, and A. Ramadhan, “Implementasi Algoritma K-Medoids dan K-Means untuk Pengelompokkan Wilayah Sebaran Cacat pada Anak,” J. CoreIT J. Has. Penelit. Ilmu Komput. dan Teknol. Inf., vol. 4, no. 2, p. 64, 2018, doi: 10.24014/coreit.v4i2.4498.

A. D. Andini and T. Arifin, “Implementasi Algoritma K-Medoids Untuk Klasterisasi Data Penyakit Pasien Di Rsud Kota Bandung,” J. RESPONSIF Ris. Sains …, vol. 2, no. 2, pp. 128–138, 2020, [Online]. Available: http://ejurnal.ars.ac.id/index.php/jti/article/view/247.

N. I. Febianto and N. Palasara, “Analisa Clustering K-Means Pada Data Informasi Kemiskinan Di Jawa Barat Tahun 2018,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 8, no. 2, pp. 130–140, 2019, doi: 10.32736/sisfokom.v8i2.653.