A Classification of Batik Lasem using Texture Feature Ecxtraction Based on K-Nearest Neighbor

Cahaya Jatmoko, Daurat Sinaga

Abstract


In this study, batik has been modeled using the GLCM method which will produce features of energy, contrast, correlation, homogenity and entropy. Then these features are used as input for the classification process of training data and data testing using the K-NN method by using ecludean distance search. The next classification uses 5 features that provide information on energy values, contrast, correlation, homogeneity, and entropy. Of the two classifications, which comparison will produce the best accuracy. Training data and data testing were tested using the Recognition Rate calculation for system evaluation. The results of the study produced 66% recognition rate in 50 pieces of test data and 100 pieces of training data.

Full Text:

PDF

References


D. Pradito, Merak Ngibing, PT Gramedia Pustaka Utama, 2010.

A. Syaefudin, “https://www.detik.com/search/searchall?query=batik+lasem,†2 Oktober 2017. [Online]. Available: https://news.detik.com/berita-jawa-tengah/d-3667571/motif-yang-rumit-membuat-batik-lasem-diburu-kolektor. [Diakses 15 Desember 2017].

A. Malagina, “http://nationalgeographic.co.id/berita/budaya,†8 Desember 2016. [Online]. Available: http://nationalgeographic.co.id/berita/2016/12/menyibak-kisah-dan-filosofi-di-balik-motif-batik-lasem. [Diakses 14 Desember 8].

Redaksi, “http://rembangkab.go.id/topik/berita-pemerintah/,†26 Februari 2015. [Online]. Available: http://rembangkab.go.id/desa-wisata-kampoeng-batik-lasem-dilaunching/. [Diakses 15 Desember 2017].

T. Sutojo, E. Mulyanto, V. Suhartono dan O. D. Nurhayati, Teori Pengolahan Citra Digital, Semarang: Andi Offset Yogyakarta dan UDINUS Semarang, 2009.

O. R. Indriani, E. J. Kusuma, C. A. Sari, E. H. Rachmawanto dan D. Setiadi, “Tomatoes Classification Using K-NN Based on,†dalam Icitech, Indonesia, 2017.

P. S. Tirajani, T. Sutojo, D. Setiadi, E. H. Rachmawanto dan C. A. Sari, “CBIR for Classification of Cow Types using GLCM,†dalam Icitech Conference, Indonesia, 2017.

M. Lestari, “Penerapan Algoritma Klasifikasi Nearest Neighbor (K-NN) untuk Mendeteksi Penyakit Jantung,†Faktor Exacta, pp. 366-371, 2014.

S. Sugiyanto dan F. Wibowo, “Klasifikasi Tingkat Kematangan Buah Pepaya (Carica Papaya L.) California (Callina-IPB9) Dalam Ruang Warna HSV dan Algoritma K-Nearest Neighboor,†dalam Prosiding SENATEK 2015 Fakultas Teknik, Purwokerto, 2015.

E. Budianita, Jasril dan L. Handayani, “Implementasi Pengolahan Citra dan Klasifikasi K-Nearest Neighbour Untuk Membangun Aplikasi Pembeda Daging Sapi dan Babi,†Jurnal Sains, Teknologi dan Industri, vol. 12, no. 2, pp. 242-247, 2015.

U. Sudibyo, D. P. Kusumaningrum, E. H. Rachmawanto dan C. A. Sari, “OPTIMASI ALGORITMA LEARNING VECTOR QUANTIZATION (LVQ) DALAM PENGKLASIFIKASIAN CITRA DAGING SAPI DAN DAGING BABI BERBASIS GLCM DAN HSV,†Jurnal SIMETRIS, vol. 9, no. 1, pp. 1-10, 2018.




DOI: https://doi.org/10.33633/jais.v3i2.2151

Article Metrics

Abstract view : 371 times
PDF - 342 times

Refbacks

  • There are currently no refbacks.


Flag Counter

 

 

 

 

Journal of Applied Intelligent System (e-ISSN : 2502-9401, p-ISSN : 2503-0493) is published by Department of Informatics Universitas Dian Nuswantoro Semarang and IndoCEISS.

  

 

Journal of Applied Intelligent System indexed by :


This journal is under licensed of Creative Commons Attribution 4.0 International License.

Visitor Stats