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All Journal CESS (Journal of Computer Engineering, System and Science) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pilar Nusa Mandiri JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Al-Madrasah: Jurnal Ilmiah Pendidikan Madrasah Ibtidaiyah JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Mantik Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Teknik Informatika C.I.T. Medicom TIN: TERAPAN INFORMATIKA NUSANTARA Journal Social Science And Technology For Community Service KLIK: Kajian Ilmiah Informatika dan Komputer Insearch: Information System Research Journal Jurnal Sisfotek Global Satya Informatika Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT
Rini Nuraini
Universitas Nasional
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Journal : KLIK: Kajian Ilmiah Informatika dan Komputer

 1 
Implementasi Euclidean Distance dan Segmentasi K-Means Clustering Pada Identifikasi Citra Jenis Ikan Nila Rini Nuraini
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 1 (2022): Agustus 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i1.551

Abstract

Tilapia is one of the favorite fish for consumption because it contains high nutrition at a relatively low price. This is what makes fish cultivators in Indonesia choose tilapia for cultivation. Tilapia has several varieties that have different characteristics, thus affecting the way of handling and cultivating these fish. For this reason, tilapia cultivators need to have knowledge about the types of tilapias so they can cultivate based on the characteristics of these fish species. This study aims to implement the Euclidean Distance algorithm and image segmentation with K-Mean Clustering on image identification of tilapia species based on their shape and texture characteristics. The K-Mean Clustering algorithm is used to separate the foreground and background in the image. Furthermore, the object's characteristics will be extracted based on its shape and texture characteristics. Furthermore, the identification process is carried out using the Euclidean Distance algorithm which will look for similarity values between two or more by calculating the value of the distance from Euclidean, to determine whether the object is included in which class based on the closeness of the values obtained. Based on the test results, the accuracy value reached 84.3%. These results show that the developed model can identify tilapia species well
Co-Authors ., Rusliyawati Adi Wibowo Akhmad Jayadi Alexander, Allan D Alfry Aristo Jansen Sinlae Anisah Ulfah Arisantoso Arisantoso Bagas Apriandadwi Putra Bayu Setyo Prayogi Budi Warsito Dedy Alamsyah Denis Taslim Desi nurnaningsih Destriana, Rachmat Fadllurrohman Fadllurrohman Fauzan Muhammad Fryda Fatmayati Hengki Rusdianto Ilham Wahyudi Siadi Indra Jaya Iskandar Fitri Liesnaningsih Liesnaningsih M Iwan Wahyuddin M. Hadi Prayitno Mayatopani, Hendra Muhammad Faiz Syafii Murien Nugraheni Mursalim Tonggiroh Nofitri Heriyani Norfaizah Norfaizah Nunik Yudaningsih Nurdiana Handayani Nurhasan Nugroho Petrus Jacob Pattiasina Ramadhan Lukman Ri Sabti Septarini Siti Nurhayati Susana Dwi Yulianti Syam Ramadhan Wahyul Amien Syafei Yeni Daniarti Yusra Fernando