Pengelompokan Kabupaten/Kota Di Jawa Barat Berdasarkan Indikator Ketenagakerjaan Menggunakan Metode K-Means Dan Fuzzy C-Means Dengan Evaluasi Rasio SW/SB Sebagai Validasi Klaster
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DOI:
https://doi.org/10.32665/statkom.v4i2.5196Keywords:
Employment, West Java, Clustering, K-Means, Fuzzy C-MeansAbstract
Background: The high dynamics of the labor market in Indonesia make employment one of the main challenges of sustainable development. Changes in economic structure, an increase in the workforce, and regional disparities in employment opportunities require adaptive strategies. This condition is particularly evident in densely populated areas with diverse economic sectors, such as West Java Province.
Objective: This study aims to group 27 districts/cities in West Java Province based on five employment indicators, namely TPAK, TPT, the number of workers in micro and small businesses, the average monthly net income of informal workers according to education, and the percentage of employment in the workforce by comparing the K-Means and Fuzzy C-Means methods.
Methods: This study uses two clustering methods, namely K-Means and Fuzzy C-Means. The best method is selected by comparing the SW and SB values.
Results: The results indicate that the K-Means method is the best method, as seen from the smaller Sw/Sb ratio of 0.076, compared to Fuzzy C-Means, which is 0.153.
Conclusion: The results of the K-Means method clustering show that the districts/cities in West Java are divided into 7 clusters, namely cluster 1 with 3 regions, cluster 2 with 8 regions, clusters 3 and 4 with 1 region, cluster 5 with 4 regions, cluster 6 with 3 regions, and cluster 7 with 7 regions. These results can serve as a basis for determining the priority of regional employment interventions.
References
Badan Pusat Statistik Provinsi Jawa Barat. (2024a). Tingkat Partisipasi Angkatan Kerja Menurut Kabupaten/Kota (Persen), 2021-2023. Retrieved on [10 April 2025]. https://jabar.bps.go.id/id/statistics-table/2/ODkjMg==/tingkat-partisipasi-angkatan-kerja-menurut-kabupaten-kota.html
Badan Pusat Statistik Provinsi Jawa Barat. (2024b). Tingkat Pengangguran Terbuka Menurut Kabupaten/Kota (%), 2021-2023. Retrieved on [10 April 2025]. https://jabar.bps.go.id/id/statistics-table/2/NzMjMg==/tingkat-pengangguran-terbuka-kabupaten-kota.html
Cebeci, Z., & Yildiz, F. (2015). Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures. Journal of Agricultural Informatics, 6(3), 13–23. https://doi.org/10.17700/jai.2015.6.3.196
Chong, B. (2021). K-means clustering algorithm: a brief review. Academic Journal of Computing & Information Science, 4(5), 37–40. https://doi.org/10.25236/ajcis.2021.040506
Disdukcapil. (2023). Buku Profil Perkembangan Kependudukan Provinsi Jawa Barat. https://disdukcapil.jabarprov.go.id/profil-kependudukan
Firdaus, H. S., Nugraha, A. L., Sasmito, B., & Awaluddin, M. (2021). Perbandingan Metode Fuzzy C-Means Dan K-Means Untuk Pemetaan Daerah Rawan Kriminalitas Di Kota Semarang. Elipsoida : Jurnal Geodesi Dan Geomatika, 4(01), 58–64. https://doi.org/10.14710/elipsoida.2021.9219
Fuadah, N. A., Nafisah, Z. A., & Wulandari, S. P. (2024). Pengelompokkan Kabupaten/Kota di Provinsi Jawa Timur berdasarkan Indikator Ketenagakerjaan. Ebisnis Manajemen, 2(4), 37–50. https://doi.org/10.59603/ebisman.v2i4.589
Hanniva, H., Kurnia, A., Rahardiantoro, S., & Mattjik, A. A. (2022). Penggerombolan Kabupaten/Kota di Indonesia Berdasarkan Indikator Indeks Pembangunan Manusia Menggunakan Metode K-Means dan Fuzzy C-Means. Xplore: Journal of Statistics, 11(1), 36–47. https://journal-stats.ipb.ac.id/index.php/xplore/article/view/855
Karnoto, K., & Suryati, T. F. (2024). Analisis Perubahan Penimbang Dalam Estimasi Indikator Ketenagakerjaan Di Sulawesi Barat : Kasus Transisi Dari Supas 2015 Ke Lf Sp2020. Jurnal Ekonomi Ichsan Sidenreng Rappang, 3(1), 300–311. https://doi.org/10.61912/jeinsa.v3i1.39
Khan, A. S. S., Fatekurohman, M., & Dewi, Y. S. (2023). Perbandingan Algoritma K-Medoids Dan K-Means Dalam Pengelompokan Kecamatan Berdasarkan Produksi Padi Dan Palawija Di Jember. Jurnal Statistika Dan Komputasi, 2(2), 67–75. https://doi.org/10.32665/statkom.v2i2.2301
Noya van Delsen, M. S., Wattimena, A. Z., & Saputri, S. (2017). Penggunaan Metode Analisis Komponen Utama Untuk Mereduksi Faktor-Faktor Inflasi Di Kota Ambon. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 11(2), 109–118. https://doi.org/10.30598/barekengvol11iss2pp109-118
Oktaviandi, B., Mukhti, T. O., Kurniawati, Y., & Martha, Z. (2024). Implementation of the Fuzzy C-Means Clustering Method in Grouping Provinces in Indonesia based on the Types of Goods Sold in E-commerce Businesses in 2022. UNP Journal of Statistics and Data Science, 2(3), 360–365. https://doi.org/10.24036/ujsds/vol2-iss3/210
Pattipeilohy, R. L., & Pakereng, M. A. I. (2023). Penerapan K-Means Clustering Pada Data Mahasiswa Fakultas Interdisiplin Program Studi D4 Destinasi Pariwisata Untuk Menentukan Strategi Promosi. Jurnal Sains Komputer & Informatika (J-SAKTI, 7(1), 320–331. https://tunasbangsa.ac.id/ejurnal/index.php/jsakti/article/view/595
Pradipta, I. M. D., Eka, A., Wahyudi, A., & Aryani, S. (2018). Fuzzy C-Means Clustering for Customer Segmentation. International Journal of Engineering and Emerging Technology, 3(1), 18–22. https://ojs.unud.ac.id/index.php/ijeet/article/view/41251/25103
Saepudin, T., & Nurfala, S. (2022). Analisis Indikator Ketenagakerjaan dan Demografi terhadap Kemiskinan di Jawa Barat. Jurnal Riset Ilmu Ekonomi, 2(2), 69–78. https://doi.org/10.23969/jrie.v2i2.30
Safitri, E. M. (2024). Clustering Study Of Hospitals In Bojonegoro Based On Health Workers With K-Means And K-Medoids Methods. Jurnal Statistika Dan Komputasi, 3(2), 92–102. https://doi.org/10.32665/statkom.v3i2.3592
Sebriana, E. I., & Hasanah, S. H. (2025). Analisis Indikator Ketenagakerjaan di Jawa Timur Tahun 2023 dengan Pendekatan Clustering. Prosiding Seminar Nasional Sains Dan Teknologi Seri III, 2(1), 1056–1068. https://conference.ut.ac.id/index.php/saintek/article/view/5158
Sudarsono, B. G., Leo, M. I., Santoso, A., & Hendrawan, F. (2021). Analisis Data Mining Data Netflix Menggunakan Aplikasi Rapid Miner. JBASE - Journal of Business and Audit Information Systems, 4(1), 13–21. https://doi.org/10.30813/jbase.v4i1.2729
Yudhistiraa, A., Aldino, A. A., & Darwis, D. (2022). Analisis Klasterisasi Penilaian Kinerja Pegawai Menggunakan Metode Fuzzy C-Means (Studi Kasus : Pengadilan Tinggi Agama bandar lampung). Jurnal Ilmiah Edutic : Pendidikan Dan Informatika, 9(1), 77–82. https://doi.org/10.21107/edutic.v9i1.17134
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