DOI: https://doi.org/10.14710/j.gauss.13.1.111-120

Clustering Kabupaten/Kota di Provinsi Papua Berdasarkan Produk Domestik Regional Bruto Menurut Lapangan Usaha Menggunakan Single Linkage dan K-Medoids

*Caecilia Bintang Girik Allo  -  Department of Mathematics, Universitas Cenderawasih, Jl. Kamp Wolker Waena Jayapura, Papua 99351, Indonesia
Open Access Copyright 2024 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
Gross Regional Domestic Product (GRDP) using the production approach represents the total value added from goods and services produced by different sectors within a specific region over a defined timeframe. There are 17 business sectors used to obtain the GRDP. The growth rate of GRDP in Papua is decrease in 2023. The growth rate is only 3,44%,  whereas the previous year it reached 4,11%. An analysis is needed to assist the government to enhance the GRDP in Papua. Clustering method can group districts/cities that have similar characteristics. The aims of this article is to determine the best method for clustering districts/cities in Papua using GRDP data. Single Method and K-Medoids is used in this article. Based on silhouette coefficients, Single Methods is better than K-Medoids to clustering districts/cities in Papua. Based on criteria of silhouette coefficients, number of clusters formed is three.
Fulltext View|Download
Keywords: Multivariate Analysis
Funding: Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Cenderawasih

Article Metrics:

Article Info
Section: Articles
Language : ID
Recent articles
Clustering Kabupaten/Kota di Provinsi Papua Berdasarkan Produk Domestik Regional Bruto Menurut Lapangan Usaha Menggunakan Single Linkage dan K-Medoids METODE MEAN-SEMIVARIANCE DALAM PEMBENTUKAN PORTOFOLIO REKSA DANA SAHAM TERBAIK BAROMETER BAREKSA PERAMALAN HARGA GULA PASIR MENGGUNAKAN VARIASI KALENDER REGARIMA DENGAN MOVING HOLIDAY EFFECT (PERIODE JANUARI 2018 SAMPAI DENGAN DESEMBER 2022 DI PASAR KOTA SEMARANG) More recent articles
  1. Asmiatun, S., Wakhidah, N., dan Putri, A. N. 2020. Penerapan Metode K-Medoids untuk Pengelompokkan Kondisi Jalan di Kota Semarang. Jurnal Teknik Infomatika dan Sistem Informasi Vol. 6 No. 2 DOI: https://doi.org/10.35957/jatisi.v6i2.193
  2. Badan Pusat Statistika (BPS) Provinsi Papua. 2024. Produk Domestik Regional Bruto Provinsi Papua Menurut Lapangan Usaha 2019 – 2023. Jayapura: Badan Pusat Statistika (BPS) Provinsi Papua
  3. Fathia, A. N., Rahmawati, R. dan Tarno. 2016. Analisis Klaster Kecamatan di Kabuapten Semarang Berdasarkan Potensi Desa Menggunakan Metode Ward dan Single Linkage. Jurnal Gaussian Vol. 5 No. 4 DOI: 10.14710/j.gauss.5.4.801-810
  4. Gujarati, D. N. 2004. Basic Econometrics (4th Edition). Singapore: McGraw Hill Inc
  5. Hair, J. F., Black, W. C., Babin, B. J., dan Anderson, R. E. 2010. Multivariate Data Analysis (7th Edition). New York: Pearson Prentice Hall
  6. Han, J. dan Kamber, M. 2006. Data Mining: Concept and Techniques. San Fransisco. Morgan Kauffman Publisher
  7. Ibrahim, R. N., Hayati, M. N., dan Amijaya, F. D. T. 2020. Penerapan Algoritma K-Medoids Pada Pengelompokkan Wilayah Desa atau Kelurahan di Kabupaten Kutai Kartanegara (Studi Kasus: Data Hasil Pendataan Potensi Desa (PODES) Tahun 2018). Jurnal Eksponensial Vol. 11 No. 2 DOI: 10.30872/eksponensial.v11i2.658
  8. Johnson, R. A., dan Wichern, D. W. 2007. Applied Multivariate Statistical Analysis (6th Edition). New Jersey: Prentice Hall International Inc
  9. Kaufman, L., dan Rousseeuw, P. J. 1990. Finding Groups in Data. New York. John Willey & Sons
  10. Kementerian Perencanaan Pembangunan Sosial. 2024. Rancangan Akhir Rencana Pembangunan Jangka Panjang Nasional 2025 – 2045. Jakarta: Kementerian Perencanaan Pembangunan Sosial
  11. Nahdliyah, M. A., Widiharih, T. dan Prahutama, A. 2019. Metode K-Medoids Clustering Dengan Validasi Silhouette Index dan C-Index (Studi Kasus Jumlah Kriminalitas Kabupaten/Kota di Jawa Tengah Tahun 2018). Jurnal Gaussian Vol. 8 No. 2 DOI: 10.14710/j.gauss.8.2.161-170
  12. Sapriyanti dan Rianto, Y. 2020. Komparasi Metode Clustering K-Means dan Single Linkage untuk Penentuan Kelompok Agent Pada Call Center. JISAMAR: Journal of Information System, Applied, Management, Accounting and Research Vol. 4 No. 3 https://journal.stmikjayakarta.ac.id/index.php/jisamar/article/view/244
  13. Syafiyah, U., Asrafi, I., Wicaksono, B., Puspitasari, D. P., dkk. 2022. Analisis Perbandingan Hierarchical dan Non-Hierarchical Clustering Pada Data Indikator Ketenagakerjaan di Jawa Barat Tahun 2020. Seminar Nasional Official Statistics Vol. 1 DOI: 10.34123/semnasoffstat.v2022i1.1221
  14. Wang, X., dan Xu, Y. 2019. An Improved Index for Clustering Validation Based On Silhouette Index and Calinski-Harabasz Index. IOP Conference Series: Materials Science and Engineering Vol 569 No. 5 DOI: 10.1088/1757-899X/569/5/052024
  15. Wororomi, J. K., Allo, C. B. G., Paranoan, N. R., Gusthvi, W. 2023. Performance of K-Means and DBSCAN Algorithm in Clustering Gross Regional Domestic Product. JICP: Journal of International Conference Proceedings Vol. 5 No. 5 DOI: 10.32535/jicp.v6i5.2710

Last update:

No citation recorded.

Last update:

No citation recorded.