Article Per Year (5 Year)
p-Index From 2021 - 2026
2.679
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Mechatronics, Electrical Power, and Vehicular Technology Jurnal Simetris Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Teknik Mesin Prosiding Seminar Nasional Teknoka Journal of Robotics and Control (JRC) Jurnal Elektra BERNAS: Jurnal Pengabdian Kepada Masyarakat Ramatekno Jurnal Polimesin
Emmanuel Agung Nugroho
Unknown Affiliation
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Veterinary Other

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 24 Documents
Search

 1   2   3 
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.
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.
Yarn inspection and sorting system using robotic vision and machine learning Emmanuel Agung Nugroho; Joga Dharma Setiawan; Deni Kurnia; Nanang Roni Wibowo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2325-2336

Abstract

The increasing demand for automation in the textile industry, particularly in quality inspection processes, underscores the need for intelligent and cost effective solutions. Conventional methods of yarn classification and sorting remain labor-intensive, time-consuming, and susceptible to human error, resulting in inconsistent quality control. This study introduces an automated system for yarn inspection and sorting that integrates robotic vision, machine learning, and position-based visual servoing (PBVS) for real-time motion control. The proposed system combines Raspberry Pi-based machine learning with computer vision utilizing a 4-degree-of-freedom (4-DOF) robotic manipulator and a webcam, enabling precise pick-and-place operations based on yarn classification into four categories: good, striped, moldy, and dirty. Experimental results validate the system’s effectiveness, achieving an average deviation of 0.375 mm along the x-axis, 0.69 mm along the y-axis, and 0.675 mm along the z-axis, resulting in an overall position error of 0.58 mm. These results demonstrate the system’s robustness and reliability in dynamic industrial environments. The novelty of this research lies in leveraging a low-cost embedded architecture with advanced visual servoing for textile automation, reducing operational errors, improving efficiency, and supporting industry 4.0 adoption.
RANCANG BANGUN SISTEM PEMBANGKIT LISTRIK TENAGA SURYA (PLTS) UNTUK RUMAH TINGGAL Sigit Purnomo; Emmanuel Agung Nugroho
JURNAL RAMATEKNO Vol 6 No 1 (2026): Ramatekno_6_1_2026
Publisher : LPPM Politeknik Enjinering Indorama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61713/jrt.v6i1.321

Abstract

Ketergantungan terhadap energi fosil dan juga kebutuhan energi listrik rumah tangga yang terus meningkat mendorong pemanfaatan energi terbarukan. Salah satu solusi yang potensial adalah Pembangkit Listrik Tenaga Surya (PLTS) skala rumah tangga. Penelitian ini bertujuan untuk merancang dan merealisasikan sistem PLTS berkapasitas 1200 Wp yang diaplikasikan pada rumah tinggal dengan beban utama berupa penerangan DC dan AC. Sistem dirancang menggunakan 12 modul panel surya monocrystalline masing-masing berdaya 100 Wp, baterai LiFePO4 12 V 400 Ah, serta solar charge controller tipe MPPT 60 A. Metode penelitian meliputi analisis kebutuhan beban, perancangan konfigurasi panel surya, perhitungan kapasitas baterai, serta analisis kinerja sistem berdasarkan potensi energi surya harian. Hasil perancangan menunjukkan bahwa sistem PLTS 1200 Wp mampu menghasilkan energi rata-rata sekitar 3,4 kWh per hari, sehingga cukup untuk memenuhi kebutuhan beban penerangan 12 jam per hari dan televisi 5 jam operasi sebesar 2,55 kWh. Dengan demikian, sistem PLTS yang dirancang layak diterapkan sebagai solusi energi alternatif ramah lingkungan untuk rumah tangga.
Co-Authors Afzeri Afzeri Tamsir Alfiyan, Yogi Aris Suryadi Asmoro, Purwandito Tulus Deni Kurnia Deni Kurnia Diki, Diki Diki, M. Iwan Setiawan Janizal Joga Dharma Setiawan Joga Dharma Setiawan La Ode Muh Munadi La Ode Muhamad Munadi M, Munadi Marwan Ruswanda Munadi Munadi Nanang Roni Wibowo Riqqi Rizalludin Rosmawati, Nelis Rustiyanti, Alifa Sigit Purnomo Slamet Riyadi Supriyanto, Adolf Asih Tiawan Wibowo, Nanang Roni