Klasterisasi udang berdasarkan ukuran berbasis pemrosesan citra digital menggunakan metode CCA dan DBSCAN

Shrimps clusterization by size using digital image processing with CCA and DBSCAN

*Adri Priadana orcid scopus  -  Universitas Jenderal Achmad Yani Yogyakarta, Indonesia
Aris Wahyu Murdiyanto  -  Universitas Jenderal Achmad Yani Yogyakarta, Indonesia
Received: 11 Aug 2019; Revised: 11 Feb 2020; Accepted: 14 Feb 2020; Published: 30 Apr 2020; Available online: 15 Feb 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Section: Original Research Articles
Language: ID
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Abstract
The quality of farmed shrimps has several criteria, one of which is shrimp size. The shrimp selection was carried out by the contractor at the harvest time by grouping the shrimp based on their size. This study aims to apply digital image processing for shrimp clustering based on size using the connected component analysis (CCA) and density-based spatial clustering of applications with noise (DBSCAN) methods. Shrimp group images were taken with a digital camera at a light intensity of 1200-3200 lux. The clustering results were compared with clustering from direct observation by two experts, each of which obtained an accuracy of 79.81 % and 72.99 % so that the average accuracy of the method was 76.4 %.
Keywords: size clustering; vaname shrimp; image processing; connected component analysis; DBSCAN

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