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All Journal EDUNOMIC : JURNAL ILMIAH PENDIDIKAN EKONOMI FAKULTAS KEGURUAN DAN ILMU PENDIDIKAN SMATIKA InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Information System for Educators and Professionals : Journal of Information System Information Management For Educators And Professionals (IMBI) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informasi dan Komputer Jurnal Manajemen Informatika dan Sistem Informasi JURSIMA (Jurnal Sistem Informasi dan Manajemen) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System JURSIMA Jurnal Nasional Teknik Industri, Sistem Informasi dan Teknik Informatika EDUNOMIC : JURNAL ILMIAH PENDIDIKAN EKONOMI FAKULTAS KEGURUAN DAN ILMU PENDIDIKAN
Odi Nurdiawan
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Social Sciences Other

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Convolutional Neural Networks for Classification Motives and the Effect of Image Dimensions Siti Aisyah; Rini Astuti; Fadhil M Basysyar; Odi Nurdiawan; Irfan Ali
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5623

Abstract

Although Indonesian batik patterns vary by location, they usually depict local customs and cultures. Each batik has a unique quality and, to correctly identify the batik designs, you need to understand the design patterns. However, many people struggle to identify and categorize these kinds of motivation because they don't have the requisite knowledge, understanding, or access to sufficient information. This study used photo data to classify batik patterns into 15 different groups. Batik Kawung, Megamendung, Lasem, Pole, Machete, Gills, Nutmeg, Karaswasih, Cendrawasih, Geblek Renteng, Bali, Betawi, and Dayak are all included in this category. 1,350 images were used in the research. Google supports the collection of data. To provide the highest level of precision and to evaluate how image dimensions affect the classification of batik designs, this study employs convolutional neural networks (CNNs). The results of this study show that Multi-Layer Perceptron (MLP) is a well-liked deep learning method for data classification, especially in domains where picture classification is involved. The size of the images utilized affects the accuracy of computational neural network (CNN) algorithms. The results showed that the test using training data comparisons of 60%, 30% and 10% resulted in a 01.89% loss of 1.18% and a 100% improvement in accuracy.
Co-Authors Ade Irma Purnamasari Ade Rizki Rinaldi agus bahtiar Ahmad Bahrum Maula Rahman Ananda Rafly Aria Pratama Arif Nur Hakim Arif Rinaldi Dikananda Budi Saputra Chaerudin, Chaerudin Cindyk Irawanto Dadang Sudrajat Dicky Miftakhul Rizki Dwi Teguh Afandi Dyana Fitri Ega Salsa Nugraha Eka Wisnu Ramadhona Fadhil M Basysyar Fadhil M.Basysyar Fidya Arie Pratama Gifthera Dwilestari Iman Nurrohmat Irfan Ali Irvan Himawan Jayawarsa, A.A. Ketut Liyanda Pangestu Melawati Melawati Muhamad Sulaeman Mulyawan Mulyawan nur syarief abdullah Rini Astuti Ruli Herdiana Saeful Anwar Shofian Yunus Siti Aisyah Syafi'i Syafi'i Syarif Hidayat, Deden Teguh Abdi Mangun Tio Prasetiya Tio Prasetya