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All Journal International Journal of Electrical and Computer Engineering Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT)
Muttaqin, Muhammad Husni
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Object detection in printed circuit board quality control: comparing algorithms faster region-based convolutional neural networks and YOLOv8 Kustija, Jaja; Fahrizal, Diki; Nasir, Muhamad; Adriansyah, Andi; Muttaqin, Muhammad Husni
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2796-2808

Abstract

Along with the development of electronic technology, the integration of numerous components on printed circuit board (PCB) boards has resulted in increasingly complex and intricate layouts. Small defects in traces can lead to failures in electronic functions, making the inspection of PCB surface layouts a critical process in quality control. Given the limitations of manual inspection, which struggles to detect such defects due to their size and complexity, there is a growing need for a PCB inspection system that utilizes automated optical inspection (AOI) based on deep learning detection. This research develops and compares two deep learning algorithms, faster region-based convolutional neural networks (R-CNN) and YOLOv8, to identify the most effective algorithm for detecting defects on PCB layouts. The findings of this study indicate that the YOLOv8 algorithm outperforms faster R-CNN, with the YOLOv8x variant emerging as the best model for defect detection. The YOLOv8x model achieved performance scores of 0.962 (mAP@50), 0.503 (mAP@50:95), 0.953 (Precision), 0.945 (Recall), and 0.949 (F1-score). These results provide a strong foundation for further research into the application of AOI for PCB defect detection and other quality control processes in manufacturing, using optimized deep learning models.
Egg Incubator Control System: A Review Zakaria, Diky; Hamzah, Muhammad Bilal; Nazhif, Dany Syauqi; Prayudha, Rezka Bunaiya; Wahid, Muhammad Rizalul; Ramelan, Agus; Muttaqin, Muhammad Husni; Nugraha, Adi
Journal of Electrical, Electronic, Information, and Communication Technology Vol 5, No 1 (2023): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.5.1.72718

Abstract

Chicken or duck farming is one of the businesses that has good prospects. Conventional hatching of chicken or duck eggs has its own risks with a hatching success percentage of <81%. The hen or duck also needs time for further breeding because they have to incubate the eggs first. Egg incubators on the market usually use on-off controls to regulate incandescent lights which can cause the temperature to fluctuate. Air humidity settings are also manually set by the user. Researchers have conducted studies related to temperature and humidity settings. This article reviews articles from the Scopus database related to control systems in egg incubator with research questions: controlled parameters, sensors used, control theory, methods, and research results that have been carried out. The result of this article can provide an overview of the research development related to egg incubator control systems.
Co-Authors Adi Nugraha Andi Adriansyah Diky Zakaria Fahrizal, Diki Hamzah, Muhammad Bilal Jaja Kustija, Jaja Muhamad Nasir, Muhamad Muhammad Rizalul Wahid Nazhif, Dany Syauqi Prayudha, Rezka Bunaiya Ramelan, Agus