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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) Jurnal Edukasi Elektro Jurnal Ilmu Komputer dan Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Sinergi Seminar Nasional Informatika (SEMNASIF) ICON-CSE Jurnal Teknologi Elektro ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Proceeding of the Electrical Engineering Computer Science and Informatics 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 International Journal of Advanced Technology in Mechanical, Mechatronics and Material (IJATEC) Technologic Journal of Integrated and Advanced Engineering (JIAE) Journal FORTEI-JEERI International Journal of Engineering Continuity Buletin Pos dan Telekomunikasi 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 Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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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.
Utilizing Inverse Kinematics for Precise Guidance in Planning 6-DoF Robot End-Effector Movements Suwoyo, Heru; Adriansyah, Andi; Andika, Julpri; Ibnu Hajar, Muhammad Hafizd; Dinata, Rizky; Triwidya Mochtar, Thathit Gumilar; Yusuf, Muhammad; Hutomo, Fajri Rezki
International Journal of Engineering Continuity Vol. 3 No. 1 (2024): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v3i1.148

Abstract

The solution of the kinematic inverse determines a substantial part of the robotic arm's control accuracy. Researchers frequently employ standard problem-solving techniques such as numerical, algebraic, iterative, and geometric methods. Although geometric like trigonometrical method has been widely studied, and their application is strongly dependent on the shape and dimensions of the robot. The complexity of the steps makes this approach difficult for researchers. In order to give a clearance and easiness, the step-by-step features of inverse kinematics are described in this research. The study begins with forward kinematics and refers to the DH-parameter in Homogeneous Matrix Transformations calculation. The existence of specific elements applied to mathematical derivation constituted the basis of forward kinematic discussions. And based on geometrical analysis, the inverse kinematic is then derived. Furthermore, simulations are performed to demonstrate the actual implementation of IK and the solution is then used to initiate the path planning process.
Adaptive bidirectional heuristic rapidly exploring random tree* for efficient path planning Suwoyo, Heru; Faudzi, Ahmad 'Athif Mohd; Adriansyah, Andi; Gunardi, Yudhi; Andika, Julpri; Tian, Yinzhong
Bulletin of Electrical Engineering and Informatics 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/eei.v15i3.11859

Abstract

Sampling-based path planning algorithms such as rapidly exploring random tree* (RRT*) are widely used for autonomous navigation in complex environments. However, many RRT variants suffer from slow initial exploration, suboptimal convergence, and search inefficiency in dense spaces. Based on this, adaptive bidirectional heuristic-RRT* (ABH-RRT*) is proposed. It is a novel method introduced as a unified path planner. ABHRRT* integrates bidirectional tree growth, heuristic-based parent selection, fast-informed hybrid sampling, and adaptive reordering to improve exploration efficiency and path optimality. The algorithm speeds up the initial path recovery caused by the presence of dual tree expansion and fast sampling. In addition, the algorithm also refines the solution using informed sampling and adaptive reordering to improve convergence toward near-optimal paths. The performance of ABH-RRT* is evaluated in four environments with different complexity levels and compared with RRT, RRT*, Fast-RRT*, Smart-RRT*, and Informed-RRT*. Experimental results show that ABH-RRT* consistently produces shorter paths and faster convergence, reduces path cost by 2–24% and increases convergence speed by 40–58% in dense and constrained environments. These results show that ABH-RRT* is a better and adaptive solution for path planning in complex scenarios.
An adaptive decreasing sigmoid convergence factor for enhancing Grey Wolf Optimizer performance in high-dimensional optimization problems Andi Adriansyah; Yudhi Gunardi; Heru Suwoyo; Fina Supegina; Isack Farady; Ahmad 'Athif Mohd Faudzi
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.025

Abstract

Optimization algorithms require an effective balance between exploration and exploitation to achieve fast convergence and high solution quality. The Grey Wolf Optimizer (GWO) has demonstrated promising performance in various engineering applications; however, its conventional linear convergence factor often leads to premature convergence or insufficient exploitation, particularly in high-dimensional search spaces. To address this limitation, this study proposes an adaptive decreasing sigmoid convergence factor that dynamically regulates the transition between exploration and exploitation throughout the optimization process. Unlike the standard linear reduction scheme, the proposed sigmoid-based mechanism maintains stronger exploration during the early search stages and accelerates exploitation in later iterations through a controlled nonlinear decline. The proposed approach was evaluated using four widely adopted benchmark functions, namely Sphere, Rosenbrock, Rastrigin, and Griewank, under different dimensionalities, population sizes, and iteration limits. Experimental results demonstrate that the proposed method improves performance in most benchmark scenarios compared with the standard GWO. The best performance was obtained with a sigmoid parameter n = 0.75, which yielded near-optimal solutions for the Sphere and Griewank functions while maintaining stable convergence for the Rosenbrock function. The results further indicate that the proposed strategy scales effectively across medium- and high-dimensional optimization problems. These findings confirm that the adaptive decreasing sigmoid convergence factor provides a simple yet effective enhancement to GWO, offering improved convergence behavior and optimization accuracy across benchmark optimization problems.
Co-Authors Ab Ghani, Muhammad Amin Abdi Wahab Abdul Mutalib Leman Abdurohman Abdurohman ABU UBAIDAH SHAMSUDIN Abu Ubaidah Shamsudin Adikrisna Nugraha Ahmad 'Athif Mohd Faudzi Akhmad Wahyu Dani Amrullah , Ahmad Ghozali Annisa Hakim Zamzami Arif Basuki Badaruddin . Badaruddin Badaruddin Cak Fangky Poernomo Chenwei Deng Dafit Feriyanto Deng, Chenwei Dodi Hermawan Eko Ihsanto Elly Yuliawati Erna Setiany Fahrizal, Diki Faudzi, Ahmad 'Athif Mohd Fengfeng Xi Ferdana, Nanda Fina Supegina Firdaus, Ade Furqan Furqan Guangjie Yuan Hadi Pranoto Hadi Pranoto Haekal, Jakfat Heri Hermawan Hitimana, Sabin Hutomo, Fajri Rezki Isack Farady Jaja Kustija, Jaja Julpri Andika Kasmad Ariansyah KOERNIAWAN, SETYA DWI Long Li Md Nor, Danial Mirzanu Rizki GM Mirzanu Rizki GM Mochamad Irlan Malik Mohamed Nasir, Noorhamizah Muchamad Rizky Nugraha Mudrik Alaydrus Muhamad Nasir, Muhamad Muhammad Hafizd Ibnu Hajar Muhammad Hanif Budiutomo Muhammad Yusuf Muthia Rahayu Muttaqin, Muhammad Husni Oka Hidyatama Pratiwi, Riri Putri Wulandari RAHAYU, FAJAR Rama Sulistyawan Ramadhan, Kurnia Reni Ika Andriani Ri zally Priatmadja Rino Ferdian Surakusumah Rio Mubarak Rizal Bahaweres Rizal Bahaweres Rizki Briandana Rizky Dinata Robi Yusuf Habibie Sabin Hitimana Said Attamimi Selomah, Mohammad Suhaimi Setiyo Budiyanto Setya Dwi Koerniawan Shamsudin H. Mohd. Amin Shamsudin, Abu Ubaidah Shamsudin, Abu Ubaidillah Suhartina, Rahmalisa Supaat Zakaria Suwoyo, Heru Tay, Tay Gaik Thong, Zhou Tian, Yingzhong Tian, Yingzhong Tian, Yinzhong Tjetjep Rony Budiman Tjetjep Rony Budiman Tong Zhou Triwidya Mochtar, Thathit Gumilar Wenbin Wang Wijaya Wijaya Yananto Mihadi Putra Yanti Yanti Yifan Li Yingzhong Tian Yingzhong Tian Yudhi Gunardi Yudhi Gunardi Yuliza . Yuliza Yuliza Zakaria, Supaat Zendi Iklima Zhou Thong ZULHAMIDI, ZULHAMIDI Zulkifli, Muhammad Fathi Yakan