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All Journal International Journal of Electrical and Computer Engineering Jurnal Dedikasi Bulletin of Electrical Engineering and Informatics Jurnal Inovasi Teknologi Pendidikan International Journal of Advances in Intelligent Informatics Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control SINTECH (Science and Information Technology) Journal Jurnal Repositor Indonesian Journal of Electrical Engineering and Computer Science JHCLS
Yuda Munarko
Universitas Muhammadiyah Malang
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health

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Journal : Bulletin of Electrical Engineering and Informatics

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Classification of batik patterns using K-Nearest neighbor and support vector machine Agus Eko Minarno; Fauzi Dwi Setiawan Sumadi; Hardianto Wibowo; Yuda Munarko
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (664.555 KB) | DOI: 10.11591/eei.v9i3.1971

Abstract

This study is proposed to compare which are the better method to classify Batik image between K-Nearest neighbor and support vector machine using minimum features of GLCM. The proposed steps are started by converting image to grayscale and extracting colour feature using four features of GLCM. The features include energy, entropy, contras, correlation and 0o, 45o, 90o, and 135o. The classifier features consist of 16 features in total. In the experimental result, there exist comparison of previous works regarding the classification KNN and SVM using multi texton histogram (MTH). The experiments are carried out in the form of calculation of accuracy with data sharing and cross-validation scenario. From the test results, the average accuracy for KNN is 78.3% and 92.3% for SVM in the cross-validation scenario. The scenario for the highest accuracy of data sharing is at 70% for KNN and at 100% for SVM. Thus, it is apparent that the application of the GLCM and SVM method for extracting and classifying batik motifs has been effective and better than previous work.
Co-Authors A.A. Ketut Agung Cahyawan W Abims Fardiansa Aditya Dwi Maryanto Agus Eko Minarno Ahmad Faiz, Ahmad Akbi, Denar Regata Aminuddin Aminuddin Anisah Rahmatul Laily Annisa Annisa Aripa, Laofin Arrie Kurniawardhani Basuki, Setio Chandranegara, Didih Rizki Christian Sri kusuma Aditya, Christian Sri kusuma Dyah Anitia Dyah Hestiningtyas Fachry Abda El Rahman Hanung Adi Nugroho Hardianto Wibowo Hariyady Hariyady Hazmi Cokro Mandiri, Mochammad Ibrahim, Zaidah Izzah, Tsabita Nurul Lailatul Husniah Maskur Maskur Maulina Balqis Muhammad Athaillah Noor Aini Mohd Roslan Norizan Mat Diah Nur Hayatin Nur Putri Hidayah Rahma Ningsih Rendiyono Wahyu Saputro Ruhaila Maskat Sasongko Yoni Bagas Sumadi, Fauzi Dwi Setiawan Susi Ekawati Vicky Novreza Wahyu Andhyka Kusuma Wicaksono, Galih Wasis Widya Rizka Ulul Fadilah Wildan Suharso Yufis Azhar Zaidah Ibrahim Zaidah Ibrahim