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All Journal SMATIKA INTEGER: Journal of Information Technology Network Engineering Research Operation [NERO] JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Prosiding Seminar Nasional Sains dan Teknologi Terapan Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Nasional Teknologi Terapan Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Djtechno: Jurnal Teknologi Informasi Indonesian Journal of Islamic Studies SAGA: Journal of Technology and Information Systems Indonesian Journal of Applied Technology Smatika Jurnal : STIKI Informatika Jurnal
Azmuri Wahyu Azinar
Universitas Muhammadiyah Sidoarjo
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Other

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Classification of Ceramic Roof Tiles Using the CNN Method Zakaria, Achmad Danu; Eviyanti, Ade; Mauliana, Metatia Intan; Azinar, Azmuri Wahyu
SAGA: Journal of Technology and Information System Vol. 3 No. 2 (2025): May 2025
Publisher : CV. Media Digital Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58905/saga.v3i2.511

Abstract

The research on tile classification using Convolutional Neural Network (CNN) aims to improve and address issues in the sorting process within the tile manufacturing industry. The accuracy level in manual sorting processes is very low due to errors caused by visual limitations and physical fatigue. By leveraging the capabilities of Convolutional Neural Network (CNN), a model was developed to classify tiles. This research involved several processes, including literature review, dataset collection, dataset splitting, preprocessing, Convolutional Neural Network (CNN) design, training, testing, and result evaluation. The study used 69 tile images divided into three classes: KW 1, KW 2, and KW 3. The results of testing the Convolutional Neural Network (CNN) on tile classification using 100 epochs with a data split of 90% training and 10% validation yielded an accuracy rate of 100%.
Co-Authors Ade Eviyanti Adi, Ragil Sapta Afidah, Dewi Nur Anjar Firmansya annisaa sri indrawanti annisaa sri indrawanti Astutik, Ika Ratna Indra Bachtiar, Ilham Wisnu Busono, Suhendro Dian Khoirul Hudalloh Dian Khoirul Hudalloh, Dian Khoirul Eriec Priyono Firdiansyah, M. Anang Firmansya, Anjar Hendro Nugroho Hendro Nugroho, Hendro Hermawan, Rafel Ardinata Hindarto Indrawanti, Annisaa Sri Irwan Alnarus Kautsar Kautsar, Irwan A. M. Anang Firdiansyah Maftahatul Hakimah Maftahatul Hakimah, Maftahatul Maretha Ruswiansari Maretha Ruswiansari, Maretha Mauliana, Metatia Intan Mufti, Adam Laxmana Muhammad Ilham Ikhwandi Muhammad Qosdy Jauharul Arzaq Ragil Sapta Adi Rinci Kembang Hapsari Rohman Dijaya Saputra, Cahnur Sugiyanto, Sugiyanto Sumarno Sumarno Taurusta, Cindy Uce Indahyanti Utami, Gati Sri Yunianita Rahmawati, Yunianita Zakaria, Achmad Danu