K-MEDOIDS ALGORITHM ANALYSIS IN PERMANENT WORKER GROUPING OF INDONESIAN CONSTRUCTION COMPANIES

Authors

  • Sonia Tarigan STIKOM Tunas Bangsa Pematangsiantar, North Sumatra
  • Harly Okprana STIKOM Tunas Bangsa Pematangsiantar, North Sumatra
  • Ilham Syahputra Saragih STIKOM Tunas Bangsa Pematangsiantar, North Sumatra

Keywords:

Construction Company, Analysis, K Medoids, Cluster

Abstract

The construction companies are both those who run the construction work, both construction administrators and construction consultants who need the manpower for their operations. There is no way to determine the existence of a policy of the workers who have a work agreement with the business owner for a period of time. The company's long-term rating of construction workers in Indonesia from 2010-2018 is based on the need to provide information and input to the local government center at the construction site in Indonesia. One of the grouping methods that can be used is k - Medoids. The advantage of this method is to overcome sensitive to outlier. This method in its horn is represented by objects close to the center and thus capable of sterilizing a more precise value. Analysis of the data grouping shows that two cluster data produced one in the low and 33 in high cluster with total cost of 2.7557.

References

Yuli Mardi, “Data Mining : Klasifikasi Menggunakan Algoritma C4 . 5 Data mining merupakan bagian dari tahapan proses Knowledge Discovery in Database ( KDD ) . Jurnal Edik Informatika,” J. Edik Inform., vol. 2, 2019.

D. Firdaus, “Penggunaan Data Mining dalam Kegiatan Sistem Pembelajaran Berbantuan Komputer,” J. Format, vol. 6, no. 2, pp. 91–97, 2017.

M. Y. Helmy, Kushartantya, and N. Bahtiar, “Implementasi Data Mining Untuk Memprediksi Kelayakan Permintaan Pinjaman Nasabah Di Lembaga Keuangan,” J. Informatics Technol., vol. 2, no. 1, pp. 33–42, 2013.

N. Putu, E. Merliana, and A. J. Santoso, “Analisa Penentuan Jumlah Cluster Terbaik pada Metode K-Means,” pp. 978–979.

W. M. S. Karsito, “Karsito, Winda Monika Sari,” vol. 9, no. September, pp. 67–78, 2018.

A. Maddeppungeng, “Alasan Utama Perusahaan Jasa Kontruksi Melakukan Investasi Teknologi Informasi,” Poli-Teknologi, vol. 9, no. 1, p. 160282, 2010.

E. Widajanti, “PERENCANAAN SUMBERDAYA MANUSIA YANG EFEKTIF: STRATEGI MENCAPAI KEUNGGULAN KOMPETITIF Erni Widajanti Fakultas Ekonomi Universitas Slamet Riyadi Surakarta,” J. Ekon. dan Kewirausahaan, vol. 7, no. 2, pp. 105–114, 2007.

Augustinus & Eric, “Pengelolaan SDM Pada PT. Aneka Sejahtera Engineering,” Pengelolaan SDM Pada PT. Aneka Sejah. Eng., vol. 1, no. 2, 2013.

S. Sugriyono and M. U. Siregar, “Preprocessing kNN algorithm classification using K-means and distance matrix with students’ academic performance dataset,” J. Teknol. dan Sist. Komput., vol. 8, no. 4, pp. 311–316, 2020, doi: 10.14710/jtsiskom.2020.13874.

R. Franita, “Implementasi Sinergitas Lembaga Pemerintah Untuk Mendukung Budaya Sadar Bencana di Kota Balikpapan,” Nusant. J. Ilmu Pengetah. Sos., vol. 7, no. 2, pp. 408–420, 2020.

D. Rudianto, “Analisis Perbandingan Kinerja Keuangan PT. Telkom, Tbk dengan PT. Indosat, Tbk Periode 2005-2010,” J. Nas. Univ. Bakrie Jakarta, ISSN, pp. 2089–3590, 2012.

A. U. Fitriyadi and A. Kurniawati, “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.

Z. Mustofa and I. S. Suasana, “Algoritma Clustering K-Medoids Pada E-Government Bidang Information And Communication,” J. Teknol. dan Komun., vol. 9, pp. 1–10, 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.

P. Kumar and D. Sirohi, “Comparative analysis of FCM and HCM algorithm on Iris data set,” Int. J. Comput. Appl., vol. 5, no. 2, pp. 33–37, 2010, doi: 10.5120/888-1261.

Downloads

Published

2021-10-14

How to Cite

Tarigan, S., Okprana, H., & Saragih, I. S. (2021). K-MEDOIDS ALGORITHM ANALYSIS IN PERMANENT WORKER GROUPING OF INDONESIAN CONSTRUCTION COMPANIES. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 1(1), 84–91. Retrieved from https://ioinformatic.org/index.php/JAIEA/article/view/58