cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Andi Adriansyah
Contact Email
andi@mercubuana.ac.id
Phone
+628111884220
Journal Mail Official
sinergi@mercubuana.ac.id
Editorial Address
Fakultas Teknik Universitas Mercu Buana Jl. Raya Meruya Selatan, Kembangan, Jakarta 11650 Tlp./Fax: +62215871335
Location
Kota adm. jakarta barat,
Dki jakarta
INDONESIA
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 588 Documents
 55   56   57   58   59 
Risk-based predictive maintenance of medium voltage network switching equipment using analytical hierarchy process as an analytical tool Erick Satriyo Gumilang; Chairul Hudaya; Budi Sudiarto; Faiz Husnayain
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.
Shear strength enhancement of fine sand soil using Guar Gum biopolymer under varying curing conditions Riza Suwondo; Maya Devina Kurniawan; I Gede Mahardika Susila; Andryan Suhendra
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. 
Dynamic modeling of lithium-ion battery degradation using data-driven and physics-informed method Daniel Santoso; Muhamad Dzaky Ashidqi
SINERGI Vol. 30 No. 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.013

Abstract

Accurate real‑time prediction of lithium‑ion battery (LIB) capacity degradation is essential for embedded battery‑management systems. Equivalent circuit models (ECMs) run quickly but lose accuracy over time, whereas purely data-driven networks achieve high precision at a high computational cost. This study introduces a physics‑informed neural network (PINN) that embeds the differential equations of a first‑order Thevenin ECM directly into the loss function. Using only terminal voltage and current as inputs, the network simultaneously estimates internal resistance, polarization resistance, polarization capacitance, open‑circuit voltage, and capacity loss. The model was trained and evaluated over 300 charge–discharge cycles of a 18650 lithium-ferrous phosphate (LFP) cell. The resulting capacity degradation estimation achieved a root mean squared error (RMSE) of 0.012 and a mean absolute percentage error (MAPE) of 0.974 %, surpassing a neural ordinary differential equation baseline with RMSE of 0.215. The trained network contains 261 parameters, requires 0.6 ms per sample for inference, and consumes 49 MB of memory. This computation cost is far lower than that of a long short‑term memory (LSTM) benchmark with comparable accuracy. In addition, the proposed model maintains its accuracy under limited dataset conditions. With a fourfold smaller training set, the PINN maintained an RMSE of 0.023, whereas the LSTM error increased to 0.72. The results demonstrate that lightweight neural networks guided by physics-based constraints can provide reliable, real-time health estimation on resource‑limited hardware.
A predictive safety and maintenance framework for railway locomotives: integrating HAZOP, FMEA, and IoT-based risk mitigation Rifki Muhendra; Zain Fadhlurrohman; Farhan Indra Pratama Adi; Zulkani Sinaga; Andi Turseno
SINERGI Vol. 30 No. 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.014

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.
The impact of the inclination angle of perforated screen facade on daylight performance in the tropics Feny Elsiana; Danny Santoso Mintorogo
SINERGI Vol. 30 No. 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.015

Abstract

Daylighting is one of the fundamental aspects of green building principles. Utilizing daylighting in a building offers numerous benefits, including energy efficiency, enhanced comfort, improved workplace productivity, better health, and increased economic value. However, buildings with glazed facades can experience excessive illuminance, uneven daylight distribution, and glare without proper shading devices. Perforated screen facade (PSF) is one of the shading devices widely used in buildings with glass facades. PSF minimizes direct solar radiation and enhances daylighting performance while preserving outdoor views. This study focused on one design variable of PSF, the inclination angle, which had not been widely explored in previous research within the context of a tropical climate. The research aimed to evaluate the impact of the PSF inclination angle on daylight performance. The research method was experimental, using radiance-based simulation as a tool. The daylight availability and visual comfort of office buildings with vertical PSF were compared with inclined PSF. The daylight performance metrics analyzed included mean illuminance, useful daylight illuminance, and spatial disturbing glare. The results indicated that implementing an inclined PSF resulted in mean illuminance ranging from 1065 to 1105 lx, useful daylight illuminance between 95.08% and 95.55%, and spatial disturbing glare between 5.1% and 6.5%. Increasing the PSF inclination angle raises the mean illuminance and spatial disturbing glare and reduces the useful daylight illuminance. PSF can be applied with an inclination angle to buildings in the tropics, providing broader possibilities for facade design exploration.
Towards enhanced acoustic fan booster damage detection: a comparative study of feature-based and machine learning approaches Rikko Putra Youlia; Dedik Romahadi; Aberham Genetu Feleke; Irfan Evi Nugroho; Alina Alina
SINERGI Vol. 30 No. 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.016

Abstract

Machine failure detection frequently uses non-destructive monitoring techniques such as vibration analysis. Although vibration analysis can identify machine degradation, the apparatus is often costly and necessitates specialist knowledge. Additionally, many existing methods in audio classification rely on characteristics represented as pictures or vectors, which increases computational complexity. In contrast, this research introduces a novel method that substitutes vibration data with a singular numerical feature derived from audio signals, addressing both cost and complexity issues. Our objective is to develop a rapid and precise audio-based method for detecting machine damage. The acoustic signals from the machine apparatus were classified into three categories: normal, belt damage, and combined belt and bearing defect. The data processing technique involved lowering the sample rate and segmenting the data to improve computational efficiency and classification performance. We use the Welch method and appropriate statistical techniques to analyze Power Spectral Density (PSD). The performance of seven classifier models, KNN, LDA, SVM, NB, ANN, RF, and DT, was evaluated using accuracy, precision, sensitivity, specificity, and F-score. LDA achieved the highest accuracy at 92.83%, followed by ANN (92.75%), NB (92.74%), and DT (92.34%). These models outperformed KNN (89.90%) and RF (89.40%), with SVM recording the lowest accuracy at 85.40%. LDA was highly effective, achieving the highest accuracy with a single average PSD-type feature, showcasing its robustness in machine defect diagnosis. Compared to previous methods, this approach simplifies feature extraction, reduces computational demands, and maintains high diagnostic performance, providing notable benefits in terms of effectiveness and precision. 
An effective and efficient vehicle detection using ER-EMA-YOLOv10n Imanuel Kutika; Vicky Nolant Setyanto Lahimade; Tomi Heri Julius Todingan; Hebron Prasetya; Steven Ray Sentinuwo; Muhamad Dwisnanto Putro
SINERGI Vol. 30 No. 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.017

Abstract

Vehicle detection plays a key role in automating traffic analysis, a field that continues to advance rapidly. Vision-based systems identify vehicle types and sizes, but achieving high accuracy and efficiency remains a challenge. Reliable real-world deployment requires optimized models that balance performance and computational cost. YOLOv10n, the most efficient version of the YOLO family, offers a solid foundation for lightweight feature extraction. To improve its detection performance, this study proposes an enhanced version of YOLOv10n by incorporating a scale-aware attention mechanism. We proposed the Expanded Refinement Efficient Multi-Scale Attention (ER-EMA) module, which enhances feature encoding by capturing vehicle characteristics across multiple receptive fields. ER-EMA consists of two core components: the Expanded Converted Inverted Block (ECIB) and the Convolutional Refinement Block (CRB). These components use diverse convolutional kernels to extract and refine multi-frequency spatial features. Integrating ER-EMA into the YOLOv10n framework produces a more compact and accurate detection model. Experimental results show that the proposed model increases mAP@50 by 1%, while reducing the number of parameters by 0.1M and computation by 0.1 GFLOPS on the Vehicle-COCO dataset. On the UA-DETRAC benchmark, it achieves a 4% improvement in mAP@50:95, with a reduction of 0.2M in parameters and 0.4 GFLOPS in computational efficiency—outperforming the original YOLOv10n and prior methods in both performance and computational efficiency.
Development and performance evaluation of an automatic size-sorting system for catfish seeds using photodiode sensors Irmansyah Irmansyah; Rifqi Eka Saputra; Mahfuddin Zuhri; Heriyanto Syafutra
SINERGI Vol. 30 No. 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.018

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.
Real-time deep neural network-based waste detection and classification using a camera sensor Arsyad Ramadhan Darlis; Lita Lidyawati; Lisa Kristiana; Etih Hartati; Faradilla Rizqi Trisani
SINERGI Vol. 30 No. 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.019

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.
Enhanced Classification of Multi Abnormal Brain Tumors Detection Using Customized Inception V3 Nagaraju Arumalla; Veerraju Gampala
SINERGI Vol. 30 No. 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.020

Abstract

A brain tumor (BT) is considered to be one of the most fatal diseases in the world, which also demands a very precise and early detection to be successfully addressed. The irregularities in the brain can be detected with the help of a magnetic resonance image, or MRI. Menigoma, glioma, pituitary tumours, and no-tumor are four categories of BT to be classified in this work according to an enhanced transfer learning (TL) approach, generated by the pretrained Inception V3 model. The preprocessing pipeline is new and includes data augmentation to reduce overfitting, a bilateral filter to remove noise, background cropping, and image scaling. The proposed method achieves training accuracy of 94.9% and validation accuracy of 93.8%. With a change in the hyperparameter (k-value), the validation and training accuracies improve to 95.3% and 96.8%, respectively. Furthermore, the model has a high level of generalization, where sensitivity is 92.8 percent, and specificity is 93.5 percent. The combination of transfer learning with the high-level enhancement and strengthening of pictures is novel. Nevertheless, among the factors that can affect generalizability, the variety and size of datasets are important. This model should be confirmed through further research using larger, more diverse datasets and explored in the context of clinical interpretability.

Page 58 of 59 | Total Record : 588

 55   56   57   58   59 

Filter by Year

2013 2026


Reset
Filter By Issues
All Issue Vol. 30 No. 2 (2026) Vol 30, No 1 (2026) Vol. 30 No. 1 (2026) Vol 29, No 3 (2025) Vol 29, No 2 (2025) Vol 29, No 1 (2025) Vol 28, No 3 (2024) Vol 28, No 2 (2024) Vol. 28 No. 1 (2024) Vol 28, No 1 (2024) Vol 27, No 3 (2023) Vol 27, No 2 (2023) Vol 27, No 1 (2023) Vol 26, No 3 (2022) Vol 26, No 2 (2022) Vol 26, No 1 (2022) Vol 25, No 3 (2021) Vol 25, No 2 (2021) Vol 25, No 1 (2021) Vol 24, No 3 (2020) Vol 24, No 2 (2020) Vol 24, No 1 (2020) Vol 23, No 3 (2019) Vol 23, No 2 (2019) Vol 23, No 1 (2019) Vol 22, No 3 (2018) Vol 22, No 2 (2018) Vol 22, No 1 (2018) Vol 21, No 3 (2017) Vol 21, No 2 (2017) Vol 21, No 1 (2017) Vol 20, No 3 (2016) Vol 20, No 2 (2016) Vol 20, No 1 (2016) Vol 19, No 3 (2015) Vol 19, No 2 (2015) Vol 19, No 1 (2015) Vol 18, No 3 (2014) Vol 18, No 2 (2014) Vol 18, No 1 (2014) Vol 17, No 3 (2013) More Issue