Article Sidebar

Download
PDF
Statistic
Read Counter : 82 Download : 50

Main Article Content

Abstract

West Java was the province with the highest unemployed rate during the COVID-19 pandemic. Significant increase of open ‎unemployment rate in West Java negatively impacts the national income. This study aims to apply the ‎clustering method using the k-means algorithm to determine priority clusters in West Java ‎Province by looking at the number of clusters in West Java’s city and the main characteristic of ‎each cluster. The clustering was conducted utilizing a k-means clustering algorithm which is grouping data based on similar ‎characteristics. The clustering results were evaluated using silhouette method. The results indicated that ‎two clusters were optimal. The clustering process using the k-means method showed that there were three clusters distinguishing the open unemployment rate during the pandemic in West Java Province in 2020. Cluster 1 ‎had a fairly low open unemployment rate due to the stalled service sector and low minimum city wage. ‎Cluster 2 had a high open unemployment rate due to the service sector and high minimum city wage. ‎Cluster 3 had medium open unemployment rate due to the service sector and also medium minimum city ‎wage. It suggests that cluster 2 is a priority cluster in dealing with the open unemployment rate.‎

Keywords

k-means clustering open unemployment West Java Province

Article Details

How to Cite
Ardiansyah, M. F. H., Amany, N., Anugrah, C. I., & Syafitri, U. D. (2024). K-Means Clustering Application of Open ‎Unemployment in 2020 Caused by COVID-19 in West Java Province. Enthusiastic : International Journal of Applied Statistics and Data Science, 4(1), 1–12. https://doi.org/10.20885/enthusiastic.vol4.iss1.art1
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. “Menaker Ida: Jawa Barat, Provinsi Paling Banyak Pekerjanya Yang Terdampak COVID-19” BBPVP Bekasi. Accessed: June 16, 2022. [Online]. Available: https://blkbekasi.kemnaker.go.id/Berita/detail/
  2. Menaker-Ida-Jawa-Barat-Provinsi-Paling-Banyak-Pekerjanya-Yang-Terdampak-COVID-19-31ZHQ
  3. Kemnaker Data, Accessed: June. 16, 2022. [Online]. Available: https://satudata.kemnaker.go.id/data/kumpulan-data/292
  4. A.N. Ramadani, D. Sartika, and H. Herawaty, “Increase in Unemployment Rates During the COVID-19 Pandemic,” Jurnal Ilmiah Ilmu Administrasi dan Manajemen, Vol. 15, No. 3, pp. 111–120, 2022.
  5. UNICEF, UNDP, PROSPERA, and The SMERU Research Institute, Socioeconomic Impact of the COVID-19 Pandemic on Households in Indonesia: Three Rounds of Monitoring Surveys. 2022. [Online]. Available: https://www.unicef.org/indonesia/media/13106/file/Socio-Economic%20Impact%20of%20COVID-19%20on%20Households%20in%20Indonesia.pdf
  6. “Produk Domestik Regional Bruto Provinsi-Provinsi di Indonesia menurtu Lapangan Usaha 2016-2020,” Statistics Indonesia, 2021.
  7. K. Ishak, “Faktor-Faktor yang Mempengaruhi Pengangguran dan Inflikasinya terhadap Indeks Pembangunan di Indonesia,” Iqtishaduna, Vol. 7, No. 1, pp. 22–38, 2018.
  8. T. Alfina, B. Santosa, and A.R. Barakbah, “Analisa Perbandingan Metode Hierarchical Clustering, K-Means, dan Gabungan Keduanya dalam Cluster Data (Studi Kasus: Problem Kerja Praktek Teknik Industri ITS),” Jurnal Teknik ITS, Vol. 1, No. 1, pp. 521–525, 2012.
  9. R.A. Johnson and D.W. Winchern, Applied Multivariate Statistical Analysis. New Jersey, USA: Prentice Hall, 2007.
  10. A.A. Matjik and I.M. Sumertajaya, Sidik Peubah Ganda dengan Menggunakan SAS. Bogor, Indonesia: IPB Press, 2011.
  11. C. Suhaeni, A. Kurnia, and Ristiyanti, “Perbandingan Hasil Pengelompokan Menggunakan Analisis Cluster Berhirarki, K-Means Cluster, dan Cluster Ensemble (Studi Kasus Data Indikator Pelayanan Kesehatan Ibu Hamil),” Jurnal Media Infotama, Vol. 14, No. 1, pp. 31–38, 2018.
  12. T. Khotimah, “Pengelompokan Surat dalam Al Quran Menggunakan Algoritma K-Means,” Jurnal Teknik Mesin Elektro dan Ilmu Komputer, Vol. 5, No. 1, pp. 83–88, 2014.
  13. A.U. 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.
  14. F.L. Sibuea and A. Sapta, “Pemetaan Siswa Berprestasi Menggunakan Metode K-Means Clustering,” Jurnal Teknologi dan Sistem Informasi, Vol. 4, No. 1, pp. 85–92, 2017, doi: 10.33330/jurteksi.v4i1.28.
  15. F. Nhita, “Comparative Study Between Parallel K-Means and Parallel K-Medoids with Message Passing Interface (MPI),” International Journal on Information and Communication Technology, Vol. 2, No. 2, pp. 27–36, 2016, doi: 10.21108/IJOICT.2016.22.86.
  16. R.D. Ramadhani and D.A.K. Januarita, “Evaluasi K-Means Dan K-Medoids Pada Dataset Kecil,” in Prosiding Seminar Nasional Informatika dan Aplikasinya, Fakultas Matematika dan Ilmu Pengetahuan Universitas Jenderal Achmad Yani, 2019, 20–24.
  17. A. Widarjono, “Analisis Statistika Multivariat Terapan. Sleman, Indonesia: UPP STIM YKPN, 2010.
  18. Suparto, “Analisis Korelasi Variabel-Variabel yang Mempengaruhi Siswa dalam Memilih Perguruan Tinggi,” in Seminar Nasional Sains dan Teknologi Terapan II, Institut Teknologi Adhi Tama Surabaya, 2014, 469–478.
  19. D.S. Hamermesh. “Do Labor Costs Affect Companies’ Demand for Labor?” IZA World of Labor. Accessed: June. 18, 2022 [Online]. Available: https://wol.iza.org/articles/do-labor-costs-affect-companies-demand-for-labor/long