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All Journal Jurnal Teknologi Rekayasa Informasi dan Komputer
Fajira, Hilda
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Computer Science & IT

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Klasifikasi Citra Batik Aceh Menggunakan Metode KNearest Neighbor (K-NN) Berbasis Androi Fajira, Hilda; Indrawati, Indrawati; Aswandi, Aswandi
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 6, No 1 (2023): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v6i1.4700

Abstract

Batik is a pictorial cloth that is specially made by writing or applying wax to the cloth, then processing it in a certain way that has its own characteristics. In Indonesia, there are so many different batik motifs from each region. One of the problems with batik is that batik has very diverse motifs and colors, so it is very difficult to classify batik into certain classes. This study was conducted to classify Acehnese natik into classes or regional origins based on batik motifs and characteristics and understanding of batik. The method used is the K-Nearest Neighbor method which is used to determine the closeness between the test image and the training image based on the motif features of the Aceh batik image obtained. This application system recognizes the type of Aceh batik, which reaches 80% Keywords: K-Nearest Neighbor, K-NN, Batik Aceh
Klasifikasi Citra Batik Aceh Menggunakan Metode K-Nearest Neighbor (K-Nn) Berbasis Android Fajira, Hilda; Indrawati, Indrawati; Aswandi, Aswandi
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 6, No 1 (2023): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v6i1.4802

Abstract

Batik is a pictorial cloth that is specially made by writing or applying wax to the cloth, then processing it in a certain way that has its own characteristics. In Indonesia, there are so many different batik motifs from each region. One of the problems with batik is that batik has very diverse motifs and colors, so it is very difficult to classify batik into certain classes. This study was conducted to classify Acehnese natik into classes or regional origins based on batik motifs and characteristics and understanding of batik. The method used is the K-Nearest Neighbor method which is used to determine the closeness between the test image and the training image based on the motif features of the Aceh batik image obtained. This application system recognizes the type of Aceh batik, which reaches 80% Keywords: K-Nearest Neighbor, K-NN, Batik Aceh.
Co-Authors Aswandi Aswandi, Aswandi Indrawati Indrawati