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All Journal Elkom: Jurnal Elektronika dan Komputer Progresif: Jurnal Ilmiah Komputer
I Gusti Agung Made Yoga Mahaputra
Politeknik Negeri Bali
Computer Science & IT Control & Systems Engineering Education

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Analisis Performa Siswa dalam E-Learning I Made Surya Kumara; Kannan Nataraj; Ni Putu Widya Yuniari; Jauzaa Maylia Suhendro; I Gusti Agung Made Yoga Mahaputra; Depandi Enda
Progresif: Jurnal Ilmiah Komputer Vol 21, No 1 (2025): Februari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i1.2569

Abstract

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Sistem Inspeksi Visual Berbasis Web-camera Menggunakan YOLO untuk Deteksi Real-time Spaghetti defect   pada 3D Printing FDM I Gusti Agung Made Yoga Mahaputra; Putri Alit Widyastuti Santiary; I Ketut Swardika
Elkom: Jurnal Elektronika dan Komputer Vol. 19 No. 1 (2026): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v19i1.3976

Abstract

Print failure remains a major challenge in fused deposition modeling (FDM) 3D printing because it may lead to material waste, production delays, and potential damage to printer components. One frequent failure mode is the spaghetti defect  , in which extruded filament forms irregular strands due to poor bed adhesion, object displacement, or extrusion outside the intended printing path. This study develops a low-cost visual inspection system based on a web-camera and Raspberry Pi for real-time detection of spaghetti defect   using a YOLO object detection model. The proposed system captures visual data from the printing area, processes image frames locally on the edge device, and displays the detected defect  location using a bounding box, class label, and confidence score. The dataset consisted of 100 images, including 50 normal printing images and 50 spaghetti defect   images, divided into training, validation, and testing sets using a 70:20:10 ratio. Defect  images were annotated using bounding boxes to enable location-based defect  detection. The evaluation results show that the model achieved a precision of 91.30%, recall of 88.24%, F1-score of 89.74%, mAP@0.5 of 92.10%, and mAP@0.5:0.95 of 87.30%. Real-time testing also demonstrated that the system could detect spaghetti defect   with confidence scores ranging from 0.87 to 0.90. These results indicate that the integration of a web-camera, Raspberry Pi, and YOLO has potential as an economical and portable early monitoring system for FDM 3D printing failures. 
Co-Authors Depandi Enda I Ketut Swardika I Made Surya Kumara Jauzaa Maylia Suhendro Kannan Nataraj Ni Putu Widya Yuniari Putri Alit Widyastuti Santiary