PENERAPAN METODE K-MEANS UNTUK PENGELOMPOKAN POPULASI TERNAK DI KABUPATEN SUMBAWA PROVINSI NTB

Penulis

  • Siti Mutiara
  • Karina Auliasari
  • Hani Zulfia Zahro

DOI:

https://doi.org/10.31000/jika.v9i1.13102

Abstrak

Kabupaten Sumbawa Provinsi Nusa Tenggara Barat (NTB),mempunyai sektor pertenakan berkembang pesat dengan potensi hewan ternak yang mencakup kuda, sapi, kerbau, dan kambing yang menjadi andalan ekonomi masyarakat.Meski demikian,pertumbuhan populasi hewan ternak yang pesat menghadirkan tantangan dalam pengelolaan dan pengelompokan.Data yang digunakan mencakup populasi hewan ternak per kecematan dari tahun 2016 - 2020 yang ditemukan melalui halaman resmi Badan Pusat Statistik (BPS) Kabupaten Sumbawa..Menunjukan adanya ketimpangan di beberapa kecematan,dengan beberapa wilayah mengalami kepadatan ternak tinggi sementara wilayah lain kurang optimal dalam pemanfaatan potensi pertenakan.Penelitian ini bertujuan mengelompokan populasi hewan ternak di kabupaten sumbawa berdasarkan kecematan. Metode K-Means Clustering dengan metode yang sesuai merupakan kumpulan aturan yang dirancang untuk mengorganisasikan data ke dalam klaster yang memiliki karakteristik serupa, yang membedakannya dari klaster lain yang memiliki atribut berbeda.Dengan Menganalisis data populasi hewan ternak berdasarkan kecematan, Penelitian ini bertujuan untuk memberikan wawasan baru terkait pola distribusi ternak yang dapat membantu pemerintah daerah dalam pengambilan keputusan strategis, seperti alokasi sumber daya dan pengembangan infrastruktur pertenakan dan membangun aplikasi berbasis website untuk pengelompokan populasi hewan ternak untuk memudahkan pemerintah daerah pertenakan di kabupaten sumbawa dalam mengelompokan hewan ternak .hasil dari penelitian ini menampilkan pengelompokan hewan ternak berdasarkan kategori cluster.

Referensi

F. Fitratunnisa, D. Dahlanuddin, and H. Hermansyah, (2022). “Analysis of Potential feed Ingredients to Support the Cattle Feed Industry in West Nusa Tenggara,†Jurnal Ilmu dan Teknologi Peternakan Indonesia (JITPI) Indonesian Journal of Animal Science and Technology), vol. 8, no. 1, pp. 9–20, Jun. doi: 10.29303/jitpi.v8i1.104.

I. P. C. P. Adnyana, N. Hilmiati, and L. G. S. Astiti, (2021). “The growth of cattle population in West Nusa Tenggara (NTB) and its development prospects,†IOP Conf Ser Earth Environ Sci, vol. 667, no. 1, p. 012054, Feb. doi: 10.1088/1755-1315/667/1/012054.

V. Yu. Sidorova, E. B. Petrov, and N. N. Novikov, (2022). “Application of digital models for improvement of beef cattle feeding graphs,†Proceedings of the National Academy of Sciences of Belarus. Agrarian Series, vol. 60, no. 4, pp. 380–393, Nov. doi: 10.29235/1817-7204-2022-60-4-380-393.

P. F. Sari, (2021). “Pengelompokan Populasi Hewan Ternak Menggunakan Metode Clustering ( Studi Kasus : Dinas Pertanian dan Ketahanan Pangan Kabupaten Langkat ),†Seminar Nasional Informatika (Senatika) pp. 166–175.

N. M. Kopelman, J. Mayzel, M. Jakobsson, N. A. Rosenberg, and I. Mayrose, (2015). “Clumpak : a program for identifying clustering modes and packaging population structure inferences across K,†Mol Ecol Resour, vol. 15, no. 5, pp. 1179–1191, Sep. doi: 10.1111/1755-0998.12387.

H. Gao, Y. Li, P. Kabalyants, H. Xu, and R. Martinez-Bejar, (2020). “An Original Hybrid PSO-K-Means Clustering Method Based on Gaussian Distribution and Lévy Flight,†IEEE Access, vol. 8, pp. 122848–122863, doi: 10.1109/ACCESS.2020.3007498.

D. Rohman, R. Annisa, D. Indriyana Efendi, and D. Solahudin, (2024). “Clustering Bencana Alam Menggunakan K-Means Pada Wilayah Jawa Barat,†JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 1, pp. 493–500, doi: 10.36040/jati.v8i1.8409.

A. M. Ikotun, M. S. Almutari, and A. E. Ezugwu, (2021). “Nature-Inspired Metaheuristic Approaches Based on K-Means for Automated Data Grouping: Recent Developments and Future Prospects,†Applied Sciences, vol. 11, no. 23, p. 11246, Nov. doi: 10.3390/app112311246.

T. S. Xu, H.-D. Chiang, G.-Y. Liu, and C.-W. Tan, (2017). “Hierarchical K-means Method for Clustering Large-Scale Advanced Metering Infrastructure Data,†IEEE Transactions on Power Delivery, vol. 32, no. 2, pp. 609–616, Apr. doi: 10.1109/TPWRD.2015.2479941.

S. Kasus, W. Jawa, W. P. Priyadi, J. D. Irawan, and A. Faisol, (2024). “PENERAPAN DATA MINING UNTUK CLUSTERING WILAYAH PRODUKSI PADI MENGGUNAKAN METODE K-MEANS,†vol. 8, no. 5, pp. 8381–8388.

M. Nikhar* and L. Thakre, (2020). “Intelligent Agricultural Farm Improvement using K-Means Learning,†International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 8, pp. 166–170, Jun. doi: 10.35940/ijitee.H6222.069820.

F. A. Ulya, A. N. Abdullah, T. A. Hanan, and I. M. Nur, (2023). “Penyusunan Kategori Pengangguran Terbuka di Jawa Tengah dengan Metode K-Means,†vol. 1, no. 2, pp. 71–80.

M. G. Sadewo, A. P. Windarto, and D. Hartama, (2017). “Penerapan Datamining Pada Populasi Daging Ayam Ras Pedaging Di Indonesia Berdasarkan Provinsi Menggunakan K-Means Clustering,†InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan), vol. 2, no. 1, pp. 60–67, doi: 10.30743/infotekjar.v2i1.164.

V. Miralda, M. Zarlis, and E. Irawan, (2020). “Penerapan Metode K-Means Clustering Untuk Daging Ayam Buras,†vol. 2, no. 2, pp. 91–98.

S. N. Saragih, M. Safii, and D. Suhendro, (2021). “Implementasi Metode K-Means pada Hasil Produksi Daging Jenis Ternak,†Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika), vol. 6, no. 1, p. 235, doi: 10.30645/jurasik.v6i1.288.

Unduhan

Diterbitkan

2025-01-31

Terbitan

Vol 9 No 1 (2025): JIKA (Jurnal Informatika)

Bagian

Articles

Lisensi

License and Copyright Agreement

In submitting the manuscript to the journal, the authors certify that:

  • They are authorized by their co-authors to enter into these arrangements.
  • That it is not under consideration for publication elsewhere,
  • That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
  • They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
  • They agree to the following license and copyright agreement.

Copyright

Authors who publish with International Journal of Advances in Intelligent Informatics agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal. 
  2. 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 acknowledgment of its initial publication in this journal.
  3. 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.

Licensing for Data Publication

International Journal of Advances in Intelligent Informatics use a variety of waivers and licenses, that are specifically designed for and appropriate for the treatment of data:

Other data publishing licenses may be allowed as exceptions (subject to approval by the editor on a case-by-case basis) and should be justified with a written statement from the author, which will be published with the article.

Open Data and Software Publishing and Sharing

The journal strives to maximize the replicability of the research published in it. Authors are thus required to share all data, code or protocols underlying the research reported in their articles. Exceptions are permitted but have to be justified in a written public statement accompanying the article.

Datasets and software should be deposited and permanently archived inappropriate, trusted, general, or domain-specific repositories (please consult http://service.re3data.org and/or software repositories such as GitHub, GitLab, Bioinformatics.org, or equivalent). The associated persistent identifiers (e.g. DOI, or others) of the dataset(s) must be included in the data or software resources section of the article. Reference(s) to datasets and software should also be included in the reference list of the article with DOIs (where available). Where no domain-specific data repository exists, authors should deposit their datasets in a general repository such as ZENODO, Dryad, Dataverse, or others.

Small data may also be published as data files or packages supplementary to a research article, however, the authors should prefer in all cases a deposition in data repositories.