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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) Sinergi ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Reviewer TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science International Journal of Electrical, Energy and Power System Engineering (IJEEPSE) Indonesian Journal for Social Responsibility Journal of Integrated and Advanced Engineering (JIAE) TECHNOLOGIC
Andi Adriansyah
Universitas Mercu Buana
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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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.
Development of a machine learning model for the classification of healthy and diabetic subjects using electromyography signal Zulkifli, Muhammad Fathi Yakan; Mohamed Nasir, Noorhamizah; Ab Ghani, Muhammad Amin; Adriansyah, Andi; Selomah, Mohammad Suhaimi; Tay, Tay Gaik; Md Nor, Danial
SINERGI Vol 29, No 3 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.3.009

Abstract

Diabetes can lead to complications like Diabetic Peripheral Neuropathy (DPN), which impacts muscle and nerve function. Electromyography (EMG) is a standard diagnostic tool for detecting DPN, but its complex signals make analysis time-consuming, delaying detection and treatment. This study aims to develop and compare machine learning models for classifying healthy and diabetic individuals using EMG data collected during dorsiflexion movement. The Muscle Sensor V3 recorded EMG signals, which were then transformed into time-domain features—Root Mean Square (RMS), Mean Absolute Value (MAV), Standard Deviation (SD), and Variance (VAR)—for classification purposes. Machine learning models, including K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were optimized using Particle Swarm Optimization (PSO). The analysis revealed that healthy individuals exhibited higher EMG amplitudes than those with diabetes. Among the models, ANN achieved the highest classification accuracy (94.44%) compared to SVM (88.89%) and KNN (77.78%). These results demonstrate the effectiveness of ANN as a reliable classifier for distinguishing between healthy and diabetic individuals, offering a more efficient and accurate approach to EMG data analysis for potential clinical applications.
PERANCANGAN ROBOT TANGAN SEDERHANA Adriansyah, Andi
Technologic Vol 6 No 2 (2015): Technologic
Publisher : LPPM Politeknik Astra

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian mengenai robot tangan (hand robot) adalah penelitian yang telah cukup lama dikembangkan namun berjalan amat lamban. Faktor utama yang memperlambat perkembangannya adalah harga yang tinggi dan kerumitan teknis pada proses pembuatan. Tulisan ini menawaran proses perancangan robot tangan sederhana. Bagian robot dicetak dengan pencetak tiga deimensi. Pengendali menggunakan system mikroprosesor berbasis Arduino dan digerakkan oleh lima buah motor servo melalui keyboard. Hasil pengujian menunjukkan bahwa hasil perancangan dapat bekerja dengan performa yang dapat diandalkan.
Peningkatan Daya Saing Industri Rumah Tangga dan Usaha Mikro Kuliner melalui Rebranding dan Tata Kelola Setiany, Erna; Briandana, Rizki; Andika, Julpri; Putra, Yananto Mihadi; Ramadhan, Kurnia; Adriansyah, Andi; Feriyanto, Dafit; Rahayu, Muthia; Zamzami, Annisa Hakim; Yuliawati, Elly; Pratiwi, Riri
Indonesian Journal for Social Responsibility Vol. 7 No. 02 (2025): December 2025
Publisher : LPkM Universitas Bakrie

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36782/ijsr.v7i02.449

Abstract

Culinary micro businesses and home industries have a strategic role in local economic development, especially in the Tangerang area. However, obstacles such as low brand awareness, less than optimal business management, and minimal understanding of business regulations hinder the competitiveness of this sector. This study aims to examine how product re-branding and improving business governance can increase the competitiveness of culinary micro businesses. Using the Community-Based Research (CBR) methodology, this community engagement initiative involved 10 business owners selected through purposive sampling, utilizing in-depth interviews and field observations. The results showed that the rebranding strategy, including improving packaging, improving product quality, and strengthening marketing messages, succeeded in increasing sales by up to 30%. In addition, training in financial management, marketing, and operational management improved the skills of business actors. Administrative support in managing permits such as NIB, PIRT, and halal certification also provided more trust to consumers. In conclusion, the combination of an effective rebranding strategy and good business governance can increase the competitiveness of this industry.
Co-Authors Ab Ghani, Muhammad Amin Abdi Wahab Amrullah , Ahmad Ghozali Annisa Hakim Zamzami Dafit Feriyanto Elly Yuliawati Erna Setiany Fahrizal, Diki Ferdana, Nanda Firdaus, Ade Hadi Pranoto Haekal, Jakfat Hitimana, Sabin Jaja Kustija, Jaja Julpri Andika KOERNIAWAN, SETYA DWI Malik, Mochamad Irlan Md Nor, Danial Mirzanu Rizki GM Mohamed Nasir, Noorhamizah Muhamad Nasir, Muhamad Muhammad Hafizd Ibnu Hajar Muttaqin, Muhammad Husni Pratiwi, Riri Rahayu, Muthia Ramadhan, Kurnia Rino Ferdian Surakusumah Rizal Bahaweres Rizki Briandana Selomah, Mohammad Suhaimi Shamsudin, Abu Ubaidah Shamsudin, Abu Ubaidillah Suhartina, Rahmalisa Suwoyo, Heru Tay, Tay Gaik Thong, Zhou Tian, Yingzhong Tjetjep Rony Budiman Yananto Mihadi Putra Yanti Yanti Yuliza . Zakaria, Supaat Zendi Iklima ZULHAMIDI, ZULHAMIDI Zulkifli, Muhammad Fathi Yakan