Comparison of K-Medoids and K-Means Result for Regional Clustering of Capture Fisheries in Aceh Province

Thifal Salsabila, Nurdin Nurdin, Sujacka Retno

Abstract


This research aims to develop a web-based application that can categorize areas of capture fisheries in Aceh Province. The methods used in this research are K-Means and K-Medoids. The methods used in this research are K-Means and K-Medoids, a clustering technique used to group districts/cities based on high and low catch areas. This application will use data from the Marine and Fisheries Service (KKP) of Aceh Province, covering the period 2017 to 2023. This research will analyze variables such as production (tons), number of vessels, sub-districts, villages, and fish species. The system is developed using the PHP programming language to facilitate implementation and data access by stakeholders. Stakeholders. As an evaluation tool for clustering results, the Davies-Bouldin Index (DBI) is used to measure the quality of clustering results. The results of this study are expected to provide an overview of areas with high catches and assist policymakers in designing a more strategic approach to fishing—policymakers in developing more effective strategies to increase fishing, especially in districts with low fish catch. In addition, this application also provides an interactive platform for users to analyze fisheries data quickly and efficiently.


Keywords


Clustering, K-Means, K-Medoids, Capture Fisheries, Aceh Province

Full Text:

PDF

References


M. Y. Matdoan, N. A. Purnamasari, and N. S. Laamena, “Application of the K-Means Algorithm for Clustering Production of Capture Fisheries in Maluku Province,” Pattimura Int. J. Math., vol. 2, no. 2, pp. 63–70, 2023, doi: 10.30598/pijmathvol2iss2pp63-70.

Nurdin, Bustami, R. Meiyanti, and A. Fahada, “Clustering Types of Capture Fisheries Products Using the K-Means Clustering Algorithm,” J. Theor. Appl. Inf. Technol., vol. 102, no. 17, pp. 6482–6492, 2024.

Q. Shang, Y. Yu, and T. Xie, “A Hybrid Method for Traffic State Classification Using K-Medoids Clustering and Self-Tuning Spectral Clustering,” Sustain., vol. 14, no. 17, 2022, doi: 10.3390/su141711068.

S. Mousavi, F. Z. Boroujeni, and S. Aryanmehr, “Improving customer clustering by optimal selection of cluster centroids in K-means and K-medoids algorithms,” J. Theor. Appl. Inf. Technol., vol. 8, no. 10, pp. 3807–3814, 2020.

X. Ran, X. Zhou, M. Lei, W. Tepsan, and W. Deng, “A Novel K-Means Clustering Algorithm With A Noise Algorithm For Capturing Urban Hotspots,” Appl. Sci., vol. 11, no. 23, 2021, doi: 10.3390/app112311202.

N. Nurdin, F. Fajriana, M. Maryana, and A. Zanati, “Information System for Predicting Fisheries Outcomes Using Regression Algorithm Multiple Linear,” J. Informatics Telecommun. Eng., vol. 5, no. 2, pp. 247–258, 2022, doi: 10.31289/jite.v5i2.6023.

Nurdin, M. Zarlis, Tulus, and S. Efendi, “Data Driven Optimization Approach to Fish Resources Supply Chain Planning in Aceh Province,” J. Phys. Conf. Ser., vol. 1255, no. 1, 2019, doi: 10.1088/1742-6596/1255/1/012081.

Z. A. Muchlisin, “Analisis Kebijakan Introduksi Spesies Ikan Asing Di Perairan Umum Daratan Provinsi Aceh,” vol. 1, no. 1, pp. 79–89, 2020.

M. Anwar, M. Shafira, and S. Sunarto, “Harmonisasi Kewenangan Pengelolaan Sumber Daya Perikanan Di Era Otonomi Daerah Berbasis Pancasila,” Pancasila Law Rev., vol. 1, no. 1, p. 54, 2020.

S. A. Abbas, A. Aslam, A. U. Rehman, W. A. Abbasi, S. Arif, and S. Z. H. Kazmi, “K-Means and K-Medoids: Cluster Analysis on Birth Data Collected in City Muzaffarabad, Kashmir,” IEEE Access, vol. 8, pp. 151847–151855, 2020, doi: 10.1109/ACCESS.2020.3014021.

M. Nurdin, Bustami, Rini Meiyanti, Amalia Fahada, “Application of the K-Means Method for Clustering Capture Fisheries Products in North Aceh with A Data Mining Approach,” J. Adv. Zool., vol. 44, no. 04, pp. 1770–1780, 2023.

A. Maulana and N. Nur Akbar, Khahari, “Penerapan Clustering Menggunakan Algoritma K-Means Sebagai Analisis Produksi Komoditas Perikanan Provinsi di Indonesia,” EJECTS E-Journal Comput. Technol. Informations Syst., vol. 01, no. 01, pp. 34–39, 2021.

N. Istiqomah, M. A. Ridla, and N. Azise, “Data Mining: Tingkat Penghuni Kamar Hotel Di Aceh Dari Tahun 2018-2022 Menggunakan Aplikasi Zaitun,” Gudang J. Multidisiplin Ilmu, vol. 2, no. 8, pp. 9–12, 2024, [Online]. Available: https://gudangjurnal.com/index.php/gjmi/article/view/671

Nayla Azkia, Rizky Feliana Devi, Fajar Hartanto Siswanto, and Jerry Heikal, “Application of K-Means Clustering To Analyze Insurance Data At Pt Axa Insurance Indonesia,” J. Soc. Econ. Res., vol. 6, no. 1, pp. 1136–1144, 2024, doi: 10.54783/jser.v6i1.482.

S. Darma, Y. Yusman, and J. Hendrawan, “Analisis Data Tingkat Kehadiran Pegawai dengan Menggunakan Clustering K-Means Pada Dinas Pekerjaan Umum dan Penataan Ruang Kabupaten Langkat,” J. Minfo Polgan, vol. 13, no. 1, pp. 1105–1116, 2024, doi: 10.33395/jmp.v13i1.13958.

F. Fajriana, “Analisis Algoritma K-Medoids pada Sistem Klasterisasi Produksi Perikanan Tangkap Kabupaten Aceh Utara,” J. Edukasi dan Penelit. Inform., vol. 7, no. 2, p. 263, 2021, doi: 10.26418/jp.v7i2.47795.

R. Putri, F. Riana, and B. Wulandari, “Implementasi K-Medoids Dalam Pengelompokan Fasilitas Pelayanan Kesehatan Pada Kasus Tuberculosis,” J. Inform., vol. 11, no. 1, pp. 17–24, 2024, doi: 10.31294/inf.v11i1.20044.

S. Hajar, A. A. Novany, A. P. Windarto, A. Wanto, and E. Irawan, “Penerapan K-Means Clustering Pada Ekspor Minyak Kelapa Sawit Menurut Negara Tujuan,” Semin. Nas. Teknol. Komput. Sains 2020, pp. 314–318, 2020.

N. L. Anggreini, “Teknik Clustering Dengan Algoritma K-Medoids Untuk Menangani Strategi Promosi Di Politeknik Tedc Bandung,” J. Teknol. Inf. dan Pendidik., vol. 12, no. 2, pp. 1–7, 2019, doi: 10.24036/tip.v12i2.215.

E. Sabna, U. Hang, and T. Pekanbaru, “Analysis Of K-Mean Clustering In Grouping Child Mortality,” no. x, pp. 1–5, 2024.




DOI: https://doi.org/10.52088/ijesty.v5i2.829

Article Metrics

Abstract view : 0 times
PDF - 0 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Thifal Salsabila, Nurdin, Sujacka Retno

International Journal of Engineering, Science and Information Technology (IJESTY) eISSN 2775-2674