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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) International Journal of Engineering Continuity 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 Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Journal : Sinergi

 1 
PROPOSE SAFETY ENGINEERING CONCEPT SPEED LIMITER AND FATIGUE CONTROL USING SLIFA FOR TRUCK AND BUS Pranoto, Hadi; Adriansyah, Andi; Feriyanto, Dafit; Wahab, Abdi; Zakaria, Supaat
SINERGI Vol 24, No 3 (2020)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2020.3.009

Abstract

In 2015, there were 55 deaths from 6,231 accident cases that occurred in Jakarta. A severe problem in Indonesia is the absence of a unique safety device in both commercial transport or personal vehicles and the very high complexity problem of human highways. Consequently, there are many traffic accidents caused by the negligence of the driver, such as driving a vehicle in a drunken, tired, drowsy, or over-limit speed. Therefore, it needs to be innovative using devices to increase speed but able to detect the level of tired or sleepy drivers. This paper tries to propose a concept of improving safety engineering by developing devices that can control the speed and level of safety of trucks and buses, named SLIFA. The proposed device captures the driver's condition by looking at the eyes, size of mouth evaporating, and heart rate conditions.  Theses condition will be measured with a particular scale to determine the fatigue level of the driver. Some performance tests have been carried out on truck and bus with 122 Nm and 112 Nm torque wheels and 339 HP and 329 HP power values, respectively, and the minimum speed is 62 km/h. At a top speed of 70 km / h, the torque and power of the truck are 135Nm and 370HP, with average fuel consumption of 3.43 liters/km before SLIFA installation and average fuel consumption of 4.2 liters/km after SLIFA installation. SLIFA can be said to have functional eligibility and can cut fuel consumption by 81 percent.
Design of path planning robot simulator by applying sampling based method Suwoyo, Heru; Andika, Julpri; Adriansyah, Andi
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.016

Abstract

This research aims to create a simulator for solving the global path planning of mobile robots. Various sampling-based methods such as Rapidly-exploring Random Tree (RRT), RRT*, and Fast-RRT, along with other derivative algorithms, have been widely used to solve path-planning problems in mobile robots. The level of computational efficiency, path optimality, and the ability to adapt to variant environments are some of the issues that still arise, although these techniques have shown good results in many cases. Although the existing solutions are innovative, comparison between the existing methods is still difficult due to significant differences in convergence speed, implementation complexity, and quality of the resulting paths. This makes choosing the most suitable method for a particular application difficult. The simulator uses sampling-based path planning algorithms such as RRT*, Fast RRT*, RRT*-Smart, informed-RRT*, and Honey Bee Mating Optimization-based Fast-RRT*. With this simulator, users can easily compare the performance of each algorithm and see the characteristics and efficiency of each algorithm in various situations. By running all methods through this simulator, the user can easily compare the methods based on convergence speed and optimality. Therefore, it will effectively help users understand robot navigation, improve the quality of learning, and promote the development of path-planning technology for mobile robots.
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.
Co-Authors Ab Ghani, Muhammad Amin Abdi Wahab Amrullah , Ahmad Ghozali Annisa Hakim Zamzami Dafit Feriyanto Dinata, Rizky Elly Yuliawati Erna Setiany Fahrizal, Diki Ferdana, Nanda Firdaus, Ade Hadi Pranoto Haekal, Jakfat Hitimana, Sabin Hutomo, Fajri Rezki 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 Muhammad Yusuf 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 Triwidya Mochtar, Thathit Gumilar Yananto Mihadi Putra Yanti Yanti Yuliza . Zakaria, Supaat Zendi Iklima ZULHAMIDI, ZULHAMIDI Zulkifli, Muhammad Fathi Yakan