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All Journal Rotasi Mechatronics, Electrical Power, and Vehicular Technology JURNAL TEKNIK MESIN Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Rekayasa Mesin Jurnal Teknologi Informasi dan Ilmu Komputer Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Teknik Mesin Indonesia Journal of Robotics and Control (JRC) Jurnal Polimesin
Joga Dharma Setiawan
Departemen Teknik Mesin, Fakultas Teknik, Universitas Diponegoro, Jl. Prof. Soedarto, SH, Tembalang, Semarang, Jawa Tengah, 50275
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Journal : Jurnal Polimesin

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Enhancing textile quality control with the application of teachable machine and Raspberry Pi as machine learning-based image processing Nugroho, Emmanuel Agung; Setiawan, Joga Dharma; Munadi, M.; Diki, M.
Jurnal Polimesin Vol 22, No 5 (2024): October
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v22i5.5308

Abstract

The adoption of image processing-based technologies in the textile sector is rising. This technology is commonly utilized to replace traditional sensor systems that are limited to a single function while also improving product quality control functions. Defects during the manufacturing process are a common problem in the textile business, particularly with fabric products. This study created a fabric quality control system that detects fabric problems using machine learning-based picture classification techniques. A D320p web camera detects rare and slap flaws, which are classified using open-source Google teaching machine software and processed on a Raspberry Pi 3B device. The laboratory-scale measurement was carried out on a prototype cloth rolling machine using the confusion matrix method. The test results reveal an average inference speed of 143.5 milliseconds, a frame rate of 6.45 fps, and a 98.56% accuracy rate. These results demonstrate that the proposed system is effective and efficient for detecting fabric defects, offering a promising solution for enhancing quality control in the textile industry. Future research could focus on scaling the system for industrial use and enhancing real-time performance.
Co-Authors AA Sudharmawan, AA Achmad Widodo Agus Mukhtar Anggi Arivian Arifin, Zainal Ariyanto, Mochammad Aulia Syukri Aulia, Dito Rahmat Baskoro, Catur Hilman Adritya Haryo Bhakti Budi Setiyana D. Michael Hendra Damar Yusuf Sutrisno Diki, Diki Diki, M. Dimas Bimo Nugroho Dwiyanto, Yahya Fikri Dyah Kusuma Dewi Eden Wawi Putra Eflita Yohana Emmanuel Agung Nugroho Gunawan Dwi Haryadi Hanif Muhammad Rakha Hujjatul Anam I Made Wicaksana Ekaputra Ilham F, Muhammad Ario Ismoyo Haryanto J. Jamari Khoiri Rozi Kurnianto, Andhieka Rizky La Ode Muhamad Munadi M, Munadi M.Pd S.T. S.Pd. I Gde Wawan Sudatha . Mochammad Ariyanto Mochammad Ariyanto Mochammad Ariyanto Moh. Arozi Muhammad Ritzky Novhar Munadi Munadi Munadi Munadi Munadi, M. Nana Supriyana Nugraha, Faryan Nurminda Eka Indra Pramana Paryanto Paryanto Paryanto Petrus Londa Pratama, Rakha Rahmadani Purwiantoro, Arman Rifka Hana Indraswari Rifky Ismail Riza Muhida Roni Permana Saputra RR. Ella Evrita Hestiandari Rusnaldy Rusnaldy Rustiyanti, Alifa Setiawan, Muhammad Farouk Toni Prahasto Wahyu Caesarendra Yuwana, Rizkia Wira Zainal Arifin