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All Journal Majalah Ilmiah MOMENTUM Jurnal Polimesin
Joga Dharma Setiawan
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Automotive Engineering Control & Systems Engineering Education Engineering Materials Science & Nanotechnology Mechanical Engineering

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Analisis Propulsi Statik dari Electric Ducted Fan dengan Metode Eksperimental Muhammad Dzulfikar; Tabah Priangkoso; Joga Dharma Setiawan; Candra Wahyu Sportyawan
JURNAL ILMIAH MOMENTUM Vol 18, No 2 (2022)
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jim.v18i2.7281

Abstract

Dalam pengujian ini, fungsi dari electric ducted fan diinvestigasi dengan metode eksperimental. Sebuah electric ducted fan dengan diameter rotor 80 mm digunakan untuk pembelajaran. Eksperimen menggunakan alat uji gaya dorong statik. Hasil uji statik menunjukkan bahwa gaya dorong maksimum didapatkan di sekitar 30 N. Selain nilai gaya dorong, parameter data lain yang didapatkan yaitu arus, tegangan, daya, kecepatan putar baling-baling, kecepatan udara, torsi, suhu, dan pulse width modulation (PWM).
Enhancing textile quality control with the application of teachable machine and Raspberry Pi as machine learning-based image processing Emmanuel Agung Nugroho; Joga Dharma Setiawan; M. Munadi; M. Diki
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 Candra Wahyu Sportyawan Emmanuel Agung Nugroho M. Diki M. Munadi Muhammad Dzulfikar Tabah Priangkoso