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Andi Adriansyah
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Sinergi
Published by Universitas Mercu Buana
ISSN : 14102331     EISSN : 24601217     DOI : https://dx.doi.org/10.22441/sinergi
Core Subject : Engineering,
Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering
SINERGI is a peer-reviewed international journal published three times a year in February, June, and October. The journal is published by Faculty of Engineering, Universitas Mercu Buana. Each publication contains articles comprising high quality theoretical and empirical original research papers, review papers, and literature reviews that are closely related to the fields of Engineering (Mechanical, Electrical, Industrial, Civil, and Architecture). The theme of the paper is focused on new industrial applications and energy development that synergize with global, green and sustainable technologies. The journal registered in the CrossRef system with Digital Object Identifier (DOI). The journal has been indexed by Google Scholar, DOAJ, BASE, and EBSCO.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 24 Documents
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Search results for , issue "Vol 30, No 1 (2026)" : 24 Documents clear
Risk-based predictive maintenance of medium voltage network switching equipment using analytical hierarchy process as an analytical tool Gumilang, Erick Satriyo; Hudaya, Chairul; Sudiarto, Budi; Husnayain, Faiz
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.011

Abstract

Predictive maintenance has become crucial for enhancing the reliability and efficiency of electrical systems, especially for Medium Voltage Network (MVN) switching equipment, which plays a key role in electricity distribution. This study aimed to develop a risk-based predictive maintenance model for MVN switching equipment using the Analytical Hierarchy Process (AHP) for maintenance prioritization, along with Z-score and Monte Carlo simulation methods to evaluate risk likelihood and impact. The Z-score method assessed the probability of risks occurring, revealing a probability exceeding 90% for specific equipment, such as UP2D.2025.C4, at 93.12%. The Monte Carlo simulation assessed the potential impact of these risks, showing severe consequences for various types of equipment. For example, UP2D.2025.C1 had a mean of 28.51 and a standard deviation of 3.50, while UP2D.2025.C8 had a standard deviation of 33.17, with an impact of over 61.53%. AHP was used to assign priority weights to components based on criteria such as equipment age, operational condition, and failure history. The analysis indicated that the Lightning Arrester had the highest maintenance priority at 26.04%, followed by the Fuse Cutout at 20.62% and the Pole-Mounted Circuit Breaker at 11.15%. This research was expected to significantly contribute to the development of more efficient and effective maintenance strategies for electrical systems, particularly in the electricity distribution sector.
Real-time Unmanned Surface Robot (USR) for river quality monitoring systemm Mohd Aras, Mohd Shahrieel; Ponusamy, Pavitrah; Md Nawawi, Mohamad Riduwan; Zohedi, Fauzal Naim; Bahar, Mohd Bazli; Abdullah, Lokman; Khamis, Alias; Rizman, Zairi Ismael
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.001

Abstract

A real-time Unmanned Surface Robot (USR) for river water quality monitoring system is a technology that employs a small autonomous boat outfitted with sensors and other monitoring equipment to gather and transmit data on various water quality parameters like pH, temperature and total dissolved solids sensors in rivers and other bodies of water. The USR can traverse the river, gather information or data at specific points or designated locations, as well as continuously monitor a specific stretch of river at all times. The data or information was sent in real time to a central monitoring station, where it was analyzed and used to identify potential water quality problems. Initially, the USR was designed using SolidWorks software, and its structural performance was the main focus of the investigation and examination of the design.  This USR was then created and manufactured.  The entire USR system could help detect and mitigate pollution and other environmental problems, as well as offer useful information for managing water resources. Next, to determine the overall performance of the USR, five experiments and autopilot accuracy tests were performed. Finally, this study also verified and validated the accuracy of water quality monitoring sensors. 
3D numerical investigation of roadway bridge response under hydrodynamic forces and local scour in stiff clay and sand foundations Saiful Nizam, Nurul Shafrina Atika; Ahmad, Nordila; Suif, Zuliziana; Jelani, Jestin
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

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Abstract

Bridges are critical components of transportation networks but are highly vulnerable to failure during extreme flood events, particularly due to hydrodynamic forces and local scour. This study quantitatively evaluates the effects of flood velocity and scour depth on bridge pier displacement for two representative soil conditions: very stiff clay (Ground Type B) and medium-dense sand (Ground Type C). A 3D finite-element model incorporating non-linear p–y springs was developed in CSI Bridge to represent soil–structure interaction (SSI). A total of 192 simulations were performed across flood velocities of 2–16 m/s and scour depths ranging from 0DF to 2DF. The results show that pier displacement increases systematically with both velocity and scour, with medium-dense sand exhibiting up to 30% higher displacement than very stiff clay at severe flood conditions (0.07 m vs. 0.06 m). These findings highlight the importance of soil stiffness in governing pier response under extreme hydrodynamic loading. While the study does not address debris impact, flow directionality or additional hydraulic parameters, the outcomes provide valuable insight for improving foundation design and incorporating SSI considerations into flood-resilient bridge engineering.
An Examination of the Fe₃O₄ nanomaterial impact in conjunction with Magnetorheological Elastomer material Priyandoko, Gigih; Ubaidillah, Ubaidillah; Imaduddin, Fitrian; Suwandono, Purbo; Sasongko, Muhammad Ilman Nur
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.006

Abstract

Magnetorheological elastomer (MRE) is an advanced material class that can be used for vibration damping. This material possesses the ability to reduce vibration disturbances through adjustment of its mechanical properties in response to a magnetic field applied from an external source. The objective of this study is to ascertain the effect of incorporating Magnetite (Fe₃O₄) nanomaterials into MRE. It is expected that this new material will be more sensitive to magnetic fields in damping vibrations, which would be a significant improvement. MRE is composed of carbonyl iron powder (CIP), silicone oil, and silicone rubber, with weight proportions of 30%, 5%, and 65%, correspondingly. The addition of magnetite nanomaterials to MRE occurred at weight ratios of 0.5%, 1%, 1.5%, and 2%. Observations of this new material included elemental composition analysis and viscoelastic testing of various mixture formulations in the laboratory. From this research, it can be concluded that an MRE containing Fe₃O₄ nanomaterials has been created. For the attenuation of vibrations within the 1–100 Hz frequency range. MRE-2 (MRE with 0.5% Fe₃O₄ added) is the best choice as the primary material, as it exhibited the highest tan delta value and strong damping performance at an intermediate frequency. MRE-1 sample was used as a base material mixture without added Magnetite also an excellent choice, offering high stiffness and good damping capability at low frequencies. It is shown by the results of this experiment that the effectiveness of MRE in reducing vibration can be increased by adding Magnetite, even in the limited mid-frequency range of 0 to 100 Hz.
Development and performance evaluation of an automatic size-sorting system for catfish seeds using photodiode sensors Irmansyah, Irmansyah; Saputra, Rifqi Eka; Zuhri, Mahfuddin; Syafutra, Heriyanto
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

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Abstract

In catfish farming, uniform seed size is crucial for ensuring balanced growth and minimizing competition for feed. Generally, size sorting is performed manually through visual observation and net separation, which is labor-intensive, time-consuming, and often causes stress or injury to fish. To address these limitations, this study aimed to develop and evaluate a real-time, low-cost automatic sorting system for live catfish seeds. The proposed system utilizes photodiode sensors and an Arduino-based microcontroller to detect variations in fish body length by interrupting a laser beam. Four photodiodes were arranged at specific distances to classify fish seeds into four size categories (<7 cm, 7–8 cm, 9–10 cm, and 11–12 cm). After classification, the system automatically directed each seed into the corresponding container. The results showed that the prototype successfully classified and sorted catfish seeds with an overall accuracy of 67.5%. In contrast, tests with PVC pipes under controlled conditions achieved 100% accuracy. These findings highlight the novelty of integrating size detection and direct sorting for live fish seeds, a feature not previously reported in the literature. Beyond its current limitations, this system provides a methodological framework for sensor-based aquaculture automation, offering potential for further improvements in accuracy, robustness, and application to other aquaculture species.
Shear strength enhancement of fine sand soil using Guar Gum biopolymer under varying curing conditions Suwondo, Riza; Kurniawan, Maya Devina; Susila, I Gede Mahardika; Suhendra, Andryan
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.012

Abstract

This study investigates the effect of Guar Gum biopolymer on the shear strength behaviour of fine sand soil, with the aim of evaluating its potential as a sustainable soil stabilization agent. A series of direct shear tests, following ASTM D3080-23, was conducted on Guar Gum-treated soil samples with varying biopolymer concentrations (1%, 3%, and 5%) and water content (10%, 12%, and 15%). Curing durations of 2, 5, and 7 days were applied to assess time-dependent strength development. The shear strength parameters, cohesion (c) and internal friction angle (φ), were evaluated to quantify the improvement in soil performance. The results showed that cohesion increased with higher Guar Gum concentration and longer curing times, with the highest cohesion (0.105 kg/cm²) observed at 5% concentration after 7 days. However, the internal friction angle decreased with prolonged curing, suggesting a shift from the frictional to cohesive strength. Water content had a significant impact, with 10–12% yielding optimal results. At a water content of 12 %, the highest internal friction angle (52°) was recorded after 7 days. Overall, the findings confirm that Guar Gum can significantly enhance the shear strength of fine sand when key parameters are optimized, offering an effective, environmentally friendly alternative to conventional chemical stabilizers in geotechnical applications. 
Performance Analysis of a Micro Underwater Remotely Operated Vehicle (ROV) Zohedi, Fauzal Naim; Chuan, Chan Yeow; Mohd Aras, Mohd Shahrieel; Khamis, Alias; Rizman, Zairi Ismael
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.002

Abstract

Underwater Remote Operated Vehicle (ROV) is a tethered marine robot that are widely employed for scientific and commercial applications. Several industries are working on underwater robots to increase the productivity, monitoring and surveillance especially in the petroleum and gas industries. These operations are often performed by human divers; however, the underwater environment poses hazards and pressure-related limits, making them costly and risky. As a result, ROVs have been designed to replace divers themselves. It is a tethered underwater robot that the operator controls manually using a PS2 controller. This project is to design and develop a micro underwater ROV for monitoring applications. The ROV are designed to withstand pressure underwater by selection of suitable material for its frame and other components will be equipped including pressure/depth sensor, MPU6050 IMU sensor and waterproof endoscope camera. Standard testing procedures are employed to assess the ROV's performance in buoyancy and control efficiency tests for the propulsion system in real environment, including laboratory pool. The developed ROV prototype shows promising performance with achieved 90% negative buoyancy is crucial for the ROV to perform effective submerge and raise operations and also with stable velocity and acceleration in forward, backward, and submerging. The steering tests highlighted that the ROV is more flexible and faster in maneuvering concerning turning performance as the horizontal thrusters’ configurations are positioned at 45° at the back of the ROV. The outcomes of this project are anticipated to bring substantial advantages to industries associated with underwater applications.
A predictive safety and maintenance framework for railway locomotives: integrating HAZOP, FMEA, and IoT-based risk mitigation Muhendra, Rifki; Fadhlurrohman, Zain; Adi, Farhan Indra Pratama; Sinaga, Zulkani; Turseno, Andi
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

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Abstract

Safety and maintenance efficiency are critical challenges in the railway industry, particularly in the use of lifting jacks for locomotive maintenance. This study proposes a predictive maintenance framework that integrates the Hazard and Operability Study (HAZOP), Failure Mode and Effects Analysis (FMEA), and Internet of Things (IoT) technology to detect potential failures in real time. A case study was conducted at a locomotive maintenance depot in Indonesia, where several occupational accidents had been recorded due to lifting jack malfunctions. Based on HAZOP and FMEA analyses components such as stoppers and drive motors were identified as having high Risk Priority Numbers (RPN), each reaching 512, indicating significant failure risks. The proposed IoT system employs HCSR-04 and MPU6050 sensors to accurately monitor the height and inclination of the equipment. Evaluation results show that the system effectively detects anomalies with minimal data deviation and a low data loss rate during a 10-day testing period. The implementation of this system significantly reduces workplace accident risks, improves maintenance efficiency, and supports digital transformation within the industrial environment. These findings demonstrate that the integration of HAZOP, FMEA, and IoT is effective for risk mitigation and can be replicated in other railway components. Moreover, this research opens new avenues for developing AI-based predictive systems and implementing digital twins as part of future smart maintenance strategies.
Determination of critical factors and the best alternatives for developing biodiesel from Maggot BSF Rimantho, Dino; Pratomo, Vector Anggit; Pane, Erlanda Augupta; Noywuli, Nicolaus
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.007

Abstract

This paper explores the approach for producing biodiesel from Maggot Black Soldier Fly (BSF) as a sustainable renewable energy source in Indonesia. The SWOT and VIKOR techniques determine the most effective strategy for promoting renewable energy in Indonesia. The paper included numerous respondents to ascertain the criteria and assess each option. Environmental consciousness is an important strong component in biodiesel development, with a value of 1.52. A significant drawback in biodiesel production is the elevated investment costs, quantified at 1.48. A notable opportunity in biodiesel development is its potential as an environmentally sustainable energy alternative, scoring 1.32, while a considerable threat is inadequate financial assistance, scoring 1.24. Moreover, applying the VIKOR approach reveals that alternative 6 (Enhancing collaboration among stakeholders) is the most critical option, as expert evaluations indicate, with a value of 0.048. The outcomes of this study require enhancement since additional research is necessary to yield more precise findings that will augment our comprehension of the evolution of renewable energy in Indonesia. Future studies should focus on the ramifications of producing biodiesel from BSF maggots, particularly in terms of energy security and energy autonomy in Indonesia. 
Real-time deep neural network-based waste detection and classification using a camera sensor Darlis, Arsyad Ramadhan; Lidyawati, Lita; Kristiana, Lisa; Hartati, Etih; Trisani, Faradilla Rizqi
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

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Abstract

Waste generation is a growing environmental concern, with manual sorting methods often being inefficient and error-prone, particularly under varying lighting and environmental conditions. In Indonesia, waste is typically categorized into organic and nonorganic, yet existing automated classification systems lack real-time capabilities and robustness in dynamic settings. This study proposes a novel real-time waste detection and classification system using a deep neural network, implemented on the Jetson Nano platform with a camera sensor. The system utilizes the ResNet-18 convolutional neural network architecture and is developed using Python. It is designed to distinguish between organic and nonorganic waste in real-time. Training was conducted over 30 epochs, and the system was tested under various lighting conditions—morning, daytime, afternoon, and nighttime. Results show high accuracy: 95.24% in the morning, 95.24% during the day, 90.45% in the afternoon, and 86.90% at night, with an average accuracy of 91.96%. Performance was influenced by factors such as lighting intensity, distance, waste position, changes in organic waste, and occlusion by plastic. The proposed system offers a significant improvement over traditional and existing methods by enabling accurate, real-time waste classification under diverse conditions, contributing to more efficient and intelligent waste management.

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