Clustering Coastal Areas Based on Aquaculture Productivity in North Aceh Regency Using K-Means Algorithm
DOI:
https://doi.org/10.30871/jaic.v9i5.10094Keywords:
K-Means, Clustering, Aquaculture, Coastal AreasAbstract
This study aims to cluster coastal subdistricts in North Aceh Regency based on the productivity of seven key aquaculture commodities milkfish, vannamei shrimp, tiger shrimp, tilapia, mojarra, grouper, and crab using the K-Means algorithm. The dataset, sourced from 15 coastal subdistricts, was normalized using the Z-Score method. The optimal number of clusters was determined using the Elbow Method, and clustering performance was evaluated with the Silhouette Score, yielding a value of 0.5293, indicating a moderately well-defined structure. The resulting clusters reflect distinct productivity levels: Cluster 0 (low), Cluster 1 (moderate), and Cluster 2 (high). A two-dimensional PCA plot was used to visualize the clusters, showing clear separations among them. These findings offer valuable insights for regional planners and policymakers in developing targeted aquaculture strategies and optimizing resource allocation, particularly for underperforming areas.
Downloads
References
[1] P. H. Merdeka, “Manajemen Peningkatan Kesejahteraan Masyarakat Pesisir Melalui Pemberdayaan Usaha Lokal Masyarakat: a Review,” J. Accounting, Manag. Econ. Bus., vol. 1, no. 1, pp. 1–9, 2023, doi: 10.56855/analysis.v1i1.180.
[2] Nabila Afifah Azuga, “Kajian Kerentanan Kawasan Pesisir Terhadap Bencana Kenaikan Muka Air Laut (Sea Level Rise) Di Indonesia,” J. Ris. Kelaut. Trop. (Journal Trop. Mar. Res., vol. 3, no. 2, pp. 65–76, 2021, doi: 10.30649/jrkt.v3i2.41.
[3] Estu Nugroho and T. H. dan M. N. , Raden Roro Sri Pudji Sinarni Dewi , Aisyah, “Pemanfaatan Sumberdaya Kelautan Dan Perikanan Melalui Budidaya Perikanan Berkelanjutan Menuju Masyarakat Pembudidaya 5.0 Utilization,” J. Kebijak. Perikan. Indones., vol. 14, no. 2, pp. 111–119, 2022, [Online]. Available: http://ejournal-balitbang.kkp.go.id/index.php/jkpi
[4] Y. Yunarty, A. Kurniaji, and K. Kasmatang, “Pemeriksaan Ektoparasit pada Berbagai Komoditas Budidaya Perikanan Payau,” Biosci. J. Ilm. Biol., vol. 11, no. 1, p. 579, 2023, doi: 10.33394/bioscientist.v11i1.7699.
[5] E. Sulistiyawan, A. Hapsery, and L. J. A. Arifahanum, “Perbandingan Metode Optimasi Untuk Pengelompokan Provinsi Berdasarkan Sektor Perikanan Di Indonesia (Studi Kasus Dinas Kelautan dan Perikanan Indonesia),” J. Gaussian, vol. 10, no. 1, pp. 76–84, 2021, doi: 10.14710/j.gauss.v10i1.30936.
[6] F. A. Maresti, W. I. Rahayu, M. B. C. Lustin, and T. H. Pakpahan, “Implementasi K-Means untuk Melakukan Segmnetasi Produk Berdasarkan Data Transaksi Retail,” J. Ilm. Sains dan Teknol., vol. 9, no. 1, pp. 20–32, 2025, doi: 10.47080/saintek.v9i1.3856.
[7] A. Rahmadani, “Implementasi Algoritma K-Means Untuk Clustering Data Inventori,” Indones. J. Inform. Res. Softw. Eng., vol. 5, no. 1, pp. 1–11, 2025, [Online]. Available: https://journal.irpi.or.id/index.php/ijirse/article/view/1832
[8] H. Hamsiah, “Implementasi Data Mining Dalam Penerapan Clustering Algoritma K-Medoid Sebaran Mahasiwa Baru Pada STIE-SAK,” J. Ilm. Ilk. - Ilmu Komput. Inform., vol. 8, no. 1, pp. 16–24, 2025, doi: 10.47324/ilkominfo.v8i1.311.
[9] S. B. H. Sakur, M. Silangen, and D. Tuwohingide, “Penerapan Algoritma K-Means untuk Klasterisasi Produksi Budidaya Perikanan Provinsi Sulawesi Utara,” J. SAINTEKOM, vol. 14, no. 1, pp. 38–47, 2024, doi: 10.33020/saintekom.v14i1.528.
[10] S. Tahalea, R. Fadhilah, M. Y. Matdoan, and D. A. Safira, “Clustering Shrimp Distribution in Indonesia Using the X-Means Clustering Algorithm,” Var. J. Stat. Its Appl., vol. 6, no. 1, pp. 49–54, 2024, doi: 10.30598/variancevol6iss1page49-54.
[11] S. Sulastri, B. Susetyo, and I. M. Sumertajaya, “The Clustering of the Aquaculture Fisheries Companies in Indonesia Using the K-Prototypes and Two Step Cluster (TSC) Algorithm,” Int. J. Sci. Basic Appl. Res., vol. 38, no. 1, pp. 171–186, 2021.
[12] N. Hafni, Y. M. Rangkuti, and I. M. K. Karo, “Distance Function Analysis on Fuzzy C-Means for Clustering Aquaculture Production in North Sumatra,” J. Math. Comput. Stat., vol. 8, no. 1, pp. 154–162, 2025, doi: 10.35580/jmathcos.v8i1.6500.
[13] O. Nadia, W. Nasution, N. Ilmi, and J. E. Candra, “Implementasi Algoritma K-Means Clustering Dalam Pengelompokan Hasil Tangkap Ikan Di Kepulauan Riau,” Cetak) J. Innov. Res. Knowl., vol. 5, no. 1, pp. 693–704, 2025.
[14] M. Marclyna Nau, V. Nurul Fathya, and O. Pratama Martadireja, “Implementasi Data Mining Pada Analisis Karakteristik Pelanggan,” JATI (Jurnal Mhs. Tek. Inform., vol. 9, no. 3, pp. 5209–5215, 2025, doi: 10.36040/jati.v9i3.13725.
[15] M. Alta Melfia, K. Athaya Ramadhini, M. Ajmal Maulana, A. Laura Dasiva, A. Aria Briantoro, and B. Oloan Lubis, “Penerapan Data Mining Untuk Klasifikasi Data Penjualan Sembako Terlaris Dengan Algoritma C4.5,” JATI (Jurnal Mhs. Tek. Inform., vol. 9, no. 3, pp. 4616–4621, 2025, doi: 10.36040/jati.v9i3.13450.
[16] S. A. Wardani, N. Bahri, A. Varuq, and H. T. Santoso, “Implementasi Data Minning Clustering Dalam Mengelompokan Kasus Perceraian di Provinsi Jawa Timur Menggunakan Algoritma K-Means,” vol. 6, no. June, pp. 68–83, 2025.
[17] T. Hartini et al., “Implementasi Algoritma K-Means Clustering,” vol. 9, no. 1, pp. 76–83, 2025.
[18] R. Pajri, N. Suarna, I. Ali, and D. Indriya Efendi, “Penerapan Algoritma K-Means Untuk Optimalisasi Klasterisasi Penjualan Obat Di Apotek Perjuagan,” JATI (Jurnal Mhs. Tek. Inform., vol. 9, no. 1, pp. 1594–1599, 2025, doi: 10.36040/jati.v9i1.12472.
[19] M. Sholeh and K. Aeni, “Perbandingan Evaluasi Metode Davies Bouldin, Elbow dan Silhouette pada Model Clustering dengan Menggunakan Algoritma K-Means,” STRING (Satuan Tulisan Ris. dan Inov. Teknol., vol. 8, no. 1, p. 56, 2023, doi: 10.30998/string.v8i1.16388.
[20] M. Qusyairi, Zul Hidayatullah, and Arnila Sandi, “Penerapan K-Means Clustering Dalam Pengelompokan Prestasi Siswa Dengan Optimasi Metode Elbow,” Infotek J. Inform. dan Teknol., vol. 7, no. 2, pp. 500–510, 2024, doi: 10.29408/jit.v7i2.26375.
[21] P. P. Allorerung, A. Erna, M. Bagussahrir, and S. Alam, “Analisis Performa Normalisasi Data untuk Klasifikasi K-Nearest Neighbor pada Dataset Penyakit,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 9, no. 3, pp. 178–191, 2024, doi: 10.14421/jiska.2024.9.3.178-191.
[22] R. Saputra and A. Ramadhanu, “Implementasi Hybrid Intelligent System untuk KlasifikasiJenis Bola Berbasis Computer Vision,” J. Inform. Teknol. dan Sains, vol. 7, no. 1, pp. 318–325, 2025.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Septia Mulya Ulfa, Rozzi Kesuma Dinata, Risawandi Risawandi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
