Implementasi SOM Dalam Clustering Hasil Ikan Laut Kabupaten Pekalongan

  • Bagus Nur Bakti Aji Program Studi Teknik Informatika, Universitas Islam Lamongan
  • Nur Nafiiyah Universitas Islam Lamongan
  • Miftahus Sholihin

Abstract

Data from sea fish in Pekalongan Regency can be processed, one of which is clustered. Clusters are grouping data based on the same criteria. The purpose of doing clustering is to be able to help in sorting and dividing a situation based on the same criteria. Clustering of marine fish products in Pekalongan Regency will be grouped into three groups, namely: a small group of marine fish products, a medium group of marine fish products, and a large group of marine fish products. The clustering process uses the SOM algorithm, and the data is taken from the website data.go.id/dataset. Data is processed in order to show which fish yields are small, medium and large. The processing process uses variable types of fish, years and results of sea fish that are stored in Excel files and then processed using Matlab. The results show that there are fish species that are classified as low and moderate clusters, namely shrimp, squid, serimping, grouper, turmeric, and ray species. The types of fish that enter the cluster and many are Tigawaja. The types of fish that enter the medium cluster are Beloso, Pihi, Pepetek, and those who enter the low cluster are 18 fish species, while those who enter the low, medium and many clusters are Petek.

References

[1] Farizi Rachman, R.A. Norrromadani Yuniati, "Analisis Cluster Sektor Perikanan Laut dengan Menggunakan Fuzzy K-Means," in Seminar MASTER, Surabaya, 2017.
[2] Tb. Ai Munandar, Wahyu Oktri Widyarto, Harsiti, "Clustering Data Nilai Mahasiswa untuk Pengelompokan Konsentrasi Jurusan Menggunakan Fuzzy Cluster Means," in Seminar Nasional Aplikasi Teknologi Informasi (SNATI), Yogyakarta, 2013.
[3] Cary Lineker Simbolon, Nilamsari Kusumastuti, Beni Irawan, "CLUSTERING LULUSAN MAHASISWA MATEMATIKA FMIPA UNTAN PONTIANAK MENGGUNAKAN ALGORITMA FUZZY C-MEANS," Bimaster, pp. 21-26, 2013.
[4] Charu C Aggarwal, Chandan K Reddy, Data Clustering: Algorithms and Applications, Boca Raton: Chapman and Hall/CRC, 2014.
[5] Budi Santoso, Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis Teori dan Aplikasi, Yogyakarta: Graha Ilmu, 2007.
[6] Arief Hermawan, Jaringan Saraf Tiruan Teori dan Aplikasi, Yogyakarta: Andi, 2006.
[7] Sutojo, Edy Mulyanto, Vincent Suhartono, Kecerdasan Buatan, Yogyakarta: Andi, 2011.
[8] Dhini Nadia, Suning, "STUDI PENATAAN SARANA PRASARANA TEMPAT PELELANGAN IKAN (TPI) JUANDA BERBASIS CLUSTER," Jurnal Teknik Waktu, pp. 1-11, 2014.
[9] A. N. Khomarudin, "Teknik Data Mining: Algoritma K-Means Clustering," Ilmu Komputer, 2016.
[10] N. Nafi'iyah, "Clustering Keahlian Mahasiswa dengan SOM (Studi Khusus: Teknik Informatika Unisla)," in SNATIKA, Malang, 2015.
[11] A. P. Windarto, "Penerapan Data Mining Pada Ekspor Buah-Buahan Menurut Negara Tujuan Menggunakan K-Means Clustering," Jurnal Teknologi Informasi, pp. 348-357, 2017.
Published
2020-07-03
How to Cite
AJI, Bagus Nur Bakti; NAFIIYAH, Nur; SHOLIHIN, Miftahus. Implementasi SOM Dalam Clustering Hasil Ikan Laut Kabupaten Pekalongan. Jurnal Elektronika Listrik dan Teknologi Informasi Terapan, [S.l.], v. 2, n. 1, p. 1-7, july 2020. ISSN 2685-7014. Available at: <http://ojs.politeknikjambi.ac.id/elti/article/view/114>. Date accessed: 04 aug. 2020. doi: https://doi.org/10.37338/e.v2i1.114.
Section
Article ELTI 2

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