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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 561 Documents
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Experimental study of engine performance using a blend of RON 90 gasoline and fractionated gasoline equivalent from plastic pyrolysis oil Bisrul Hapis Tambunan; Janter P. Simanjuntak; Sahala Siallagan; Bonaraja Purba; Rimbawati Rimbawati; Nurin Wahidah Mohd Zulkifli; Mohd Kamal Kamarulzaman
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.016

Abstract

This study investigated the performance and emission characteristics of a gasoline engine using a blend of commercial gasoline RON 90 and fractionated plastic pyrolysis oil (PPO). Almost all previous studies used unfractionated PPO in engine performance tests. The use of raw PPO in engine performance tests will result in poor engine performance, because the physicochemical properties of the fuel do not meet engine requirements and the ASTM D4814 standard (gasoline fuel properties standard). An innovative aspect of this study is that the raw PPO was first fractionated to separate the gasoline PPO fraction, the diesel PPO fraction, and other aromatic fractions. Gasoline-equivalent PPO was used in the engine performance test to ensure the fuel used met engine specifications. PPO is obtained from post-consumer plastic waste through a pyrolysis process, followed by fractionation to separate heavy fractions and complex aromatic compounds. Blends containing up to 40% fractionated PPO were tested to evaluate their effects on engine performance and emissions. Experimental results showed that the use of 40% PPO only reduced thermal efficiency 0.79%, which is very low compared to the results of previous studies. In terms of emissions, the use of a 40% fractionated PPO blend reduced CO emissions by 7%, reduced HC by 17%, and increased CO2 by 17%. The reduction in CO2 and HC emissions is an innovative aspect of this study. These findings differ from previous studies using raw PPO, which reported significant engine performance degradation and increased emissions due to poor combustion characteristics.
Enhanced accuracy for classification modeling with a Decision Tree on rooftop solar PV Alfin Sahrin; Erna Utami; Ali Musyafa; Aris Suryadi; Didik Notosudjono; Andi Andriansyah
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.017

Abstract

Rooftop photovoltaic (PV) systems play a crucial role in Indonesia’s decarbonization agenda; however, research on interpretable classification frameworks that integrate roof geometry and meteorological heterogeneity remains limited. Most previous studies have focused on module-level fault detection rather than assessing installation feasibility at the urban scale. Addressing this research gap, the present study proposes a transparent and data-driven approach using a Decision Tree (DT) model enhanced with Grid Search Cross-Validation (GSCV) to classify the feasibility of rooftop PV installations across 20 Indonesian capital cities. Simulation data generated by PVsyst incorporates multiple tilt and azimuth configurations, as well as local weather variables, representing one-, two-, and three-directional roof geometries. The proposed DT–GSCV model is benchmarked against k-NN, Gaussian Naïve Bayes, Logistic Regression, Random Forest, XGBoost, and CatBoost, demonstrating superior generalization and interpretability. Cross-location validation across five rotational subsets confirms stable performance, with an average accuracy of 91.0% ± 0.3 and an F1-score of 0.90, highlighting the model's robustness across diverse climatic zones. Feature importance and SHAP analyses reveal that irradiation and tilt angle are the most influential factors, while temperature and humidity negatively affect feasibility. The novelty of this work lies in developing a reproducible, interpretable machine learning framework that bridges physical PV modeling and data analytics for rooftop system design. This methodology enables rapid, transparent decision support for optimizing rooftop PV deployment across Indonesia's diverse urban and climatic settings.
Stability improvement of the Southern Sulawesi system using the Hippopotamus Optimization Algorithm (HOA) Imam Robandi; Vita Lystianingrum; Jamaaluddin Jamaaluddin; Izza Anshory; Muhammad Ruswandi Djalal; Mohamad Almas Prakasa; Akhmad Ramadhani
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.018

Abstract

This study proposes a Multi Band Power System Stabilizer (MB-PSS) optimized with the Hippopotamus Optimization Algorithm (HOA) to enhance dynamic stability in the Southern Sulawesi (Sulbagsel) electricity system integrated with wind power plants (WPPs). Unlike previous works applying HOA to distribution networks or photovoltaic optimization, this study addresses a key gap: the absence of HOA based stabilizer optimization in large scale multi machine systems, particularly in Sulbagsel. The novelty lies in positioning HOA as an alternative swarm intelligence method suited to the nonlinear characteristics of MB-PSS tuning. While the claim of being the first HOA application in Sulbagsel requires stronger justification, this study extends its relevance by bridging overlaps in related domains. Damping analysis and time domain simulations are conducted to benchmark HOA against Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). Results show that HOA achieves superior performance: overshoot in generator speed deviation decreases to 0.009923 to 0.002359 p.u., settling time reduces from 16s without stabilizer to 9.58s, and the maximum damping ratio increases to 0.7185. These outcomes confirm HOA’s capability to improve oscillation damping, though additional reporting of frequency responses and voltage stability metrics would strengthen the empirical contribution. Theoretically, this study highlights HOA’s balance between exploration and exploitation, making it suitable for multimodal cost functions in stabilizer tuning. However, broader theoretical implications, such as HOA’s advancement of stability theory beyond empirical results, remain underexplored. Future research should address this dimension to consolidate HOA’s role in advanced power system stability studies.
Pore-water pressure as the stress reference in the stress state variables for unsaturated soils, a theoretical revisit Sugeng Krisnanto; Sahlan Safar Insanul Zhafir Ruhyana; Chrysti Adi Wicaksono; Muhammad Ikhsan Muslimin
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.019

Abstract

Stress state variables refer to the state of stresses that control shear strength and volume change of unsaturated soils. When the pore-water pressure, uw, is taken as the stress reference, the stress state variables are expressed as (s - uw), (ua - uw), and uw for soils with compressible soil solids, and as (s - uw) and (ua - uw) for soils with incompressible soil solids. The existing theoretical derivation was based on the equilibrium equation of water, air, and contractile skin phases. However, several steps in the existing derivation are not clearly presented and are challenging to follow. These steps are in the initial stage of the equilibrium derivation and in the elimination of the third variable, uw, for incompressible soil solids. This paper presents revisit of the theoretical derivation to obtain the stress state variables. This revisit considers stress times area in place of force instead of stress times porosity in place of force as in the existing derivation. The proposed approach provides a clearer and more transparent procedure for eliminating uw from the governing equations when the solid particles are assumed to be incompressible. This revisit also provides sound stress state variables for unsaturated soils with both compressible and incompressible solids. Overall, the theoretical revisit in this paper offers clearer interpretation and a more comprehensible derivation. In addition, it supports the validity and correctness of the commonly adopted stress state variables used in the constitutive modelling of unsaturated soils.
A hybrid exploratory factor analysis - Grey Delphi framework for prioritization in occupational health and safety risks in the textile industry Sofian Bastuti; Roslina Mohammad; Abdul Yasser Abd Fatah; Rini Alfatiyah; Nurazean Maarop; Hayati@Habibah Abdul Talib
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.020

Abstract

The textile industry plays a vital role in supporting the national economy, but is characterized by complex and hazardous working conditions that pose serious challenges to occupational health and safety (OHS). Workers are frequently exposed to high-speed machinery, harmful chemicals, excessive dust, and physically demanding tasks, making risk identification and prioritization essential for improving workplace safety. This study aims to systematically identify and rank the most critical OHS risk factors by employing a hybrid methodology that integrates Exploratory Factor Analysis (EFA) and the Grey Delphi method. Data were collected from 390 textile workers and subsequently validated through the consensus of 12 experts. The EFA process reduced 57 initial indicators into nine underlying categories, while the Grey Delphi analysis prioritized 25 risks. Among these, the five most critical risks identified are: (1) excessive noise generated by weaving and spinning machines, (2) exposure to cotton dust containing endotoxins, (3) unprotected moving machine parts, (4) long working hours without adequate rest, and (5) improper or inconsistent use of personal protective equipment (PPE). The novelty of this study lies in integrating quantitative factor reduction with expert consensus under uncertainty, producing a replicable hybrid framework for data-driven OHS risk prioritization. This approach advances current literature by bridging statistical analysis with expert judgment, thereby improving methodological rigor. The findings provide measurable contributions for both scholars and practitioners by offering evidence-based guidance for policy formulation, resource allocation, and the design of targeted safety interventions to enhance OHS management in the textile sector.
Gradient-induced fuel consumption and CO₂ emission sensitivity: a comparative analysis of two and three-axle trucks on short uphill segments Hakzah Hakzah; Abdul Rahman; Andriyani Andriyani; Jasman Jasman; Kasmaida Kasmaida
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.021

Abstract

Road freight transport significantly contributes to global energy use and greenhouse gas emissions, yet the influence of short uphill gradients on fuel consumption and CO₂ emissions remains insufficiently studied in Indonesia. While prior research has examined slope effects on long-haul operations, limited evidence quantifies sensitivity on short ascending segments (50–250 m) and compares two- and three-axle trucks. This study addresses this gap by applying a mathematical modeling framework that integrates technical parameters collected from the Datae Motor Vehicle Weigh Station (UPPKB Datae), Sidenreng Rappang Regency, South Sulawesi Province, Indonesia, with established fuel consumption and emission formulations. Gradient variations of 0–15% and travel distances of 50–250 m were analyzed, and fuel consumption results were converted into CO₂ emissions using IPCC guidelines. The findings reveal a sharp escalation in both fuel demand and emissions, with the slope increasing. At a 15% gradient and 250 m distance, CO₂ emissions rose by approximately 692% for two-axle trucks and 625% for three-axle trucks compared with the flat baseline. Although heavier trucks recorded higher absolute values, two-axle trucks exhibited greater relative sensitivity to changes in gradient. These results provide novel evidence on slope-induced inefficiencies in short segments, offering practical insights for eco-routing, operational planning, and gradient-sensitive decarbonization strategies in freight transport.    
Benchmarking YOLOv8 and vision transformers for intelligent fish monitoring in aquaponics and controlled aquarium environments Tresna Dewi; Yurni Oktarina; Sri Rezki Artini; Gita Ayu Julianka; Jhoni Satria
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.022

Abstract

Sustainable aquaculture requires reliable and accurate fish monitoring systems capable of operating across heterogeneous environmental conditions. Conventional monitoring approaches are labor-intensive and prone to human error, while recent advances in deep learning have enabled vision-based automation for aquatic environments. Convolutional object detectors such as YOLO and emerging Vision Transformer (ViT) models have demonstrated promising performance; however, most existing studies remain limited to single-environment evaluations and rarely address energy-constrained, real-world deployment. To bridge this gap, this study presents a systematic benchmark of YOLOv8 and ViT across two complementary settings: a controlled aquarium environment and a solar-powered, off-grid aquaponics system. The proposed framework integrates 1080p CCTV video acquisition, dataset annotation and augmentation, and standardized training and evaluation using COCO metrics. Experimental results show that ViT consistently outperforms YOLOv8 in detection accuracy and prediction stability across both environments. ViT achieves 99.73% accuracy in the controlled aquarium and ≥99.6% accuracy performance (99.68–99.73%) in aquaponics, while YOLOv8 records 87.90% accuracy in the aquarium and 93.92–97.92% across aquaponics fish classes, exhibiting higher sensitivity to background clutter. Statistical validation using McNemar’s test (p < 0.001) confirms that these differences are statistically significant. Beyond accuracy, the results reveal a trade-off between robustness and computational efficiency. ViT provides superior resilience under occlusion and glare, whereas YOLOv8 offers faster inference suitable for real-time operation on resource-limited edge devices. End-to-end deployment on a solar-powered NVIDIA Jetson Xavier NX demonstrates the feasibility of continuous, off-grid aquaculture monitoring and provides practical guidance for context-aware model selection in intelligent aquaculture systems.
Design and optimization of an electric screw propeller vehicle chassis using a BBNN-GA hybrid approach Mohammad Khoirul Effendi; Muhammad Ghazy Rizqi Fahada; Harus Laksana Guntur
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.023

Abstract

Electric screw propeller vehicles represent an innovative solution for traversing difficult environments such as peat soil, particularly for fresh fruit bunch (FFB) transportation in the palm oil industry. However, the unique propulsion mechanism and demanding operational conditions impose significant structural challenges on the vehicle chassis, requiring a design that is both robust and lightweight to support electric vehicle efficiency. This study focuses on the design and optimization of a chassis for an electric screw-propelled vehicle for FFB transport operating on peat soil terrain. Advanced computational intelligence techniques, namely a Back Propagation Neural Network (BPNN) and a Genetic Algorithm (GA), are employed. The BPNN predicts key structural responses, including equivalent stress, fatigue life, safety factor, and weight, with high accuracy based on variations in material type and beam thickness. Furthermore, the GA utilizes these predictions to optimize the design. The optimized results show excellent agreement with finite element simulations, with deviations of only 3.47% in stress, 2.31% in fatigue life, 1.19% in safety factor, and 0.31% in weight, confirming the high predictive accuracy of the hybrid BPNN–GA model. The optimized chassis achieves a balanced trade-off between structural strength and light weight efficiency while remaining within allowable design limits. To the authors’ knowledge, this study represents the first application of a hybrid BPNN–GA approach for optimizing a screw-propeller vehicle chassis operating on peat soil terrain, offering a novel computational strategy for lightweight and reliable electric vehicle design in soft-terrain environments.
Bitcoin price prediction using Autoencoder-based GRU and LSTM models Nurun Nafisah; Yuni Yamasari; Ervin Yohannes
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.027

Abstract

The high volatility of Bitcoin prices presents a major challenge in forecasting, as predictive models must capture both short-term fluctuations and long-term trends. Despite extensive studies on deep learning for cryptocurrency prediction, there remains a lack of systematic comparative analysis of autoencoder-based recurrent architectures, particularly AE-LSTM and AE-GRU, across both univariate and multivariate input settings, with statistical validation. This study analyzes Bitcoin price data from 2018 to 2025 to evaluate and compare the performance of two hybrid deep learning models, Autoencoder-based Gated Recurrent Unit (AE-GRU) and Autoencoder-based Long Short-Term Memory (AE-LSTM), in Bitcoin price prediction. The experiments explore the effects of dropout, learning rate, and epochs, using both univariate and multivariate inputs (Open, High, Low, Close). Results show that AE-GRU consistently outperforms AE-LSTM across all configurations, achieving up to 16.5% higher MAPE-based accuracy. The best performance was achieved by the multivariate AE-GRU, dropout = 0.1, learning rate = 0.001, epoch = 100, with RMSE 1667.125, MAE 1145.718, MAPE 2.33%, and R² 0.995017. Moreover, AE-GRU demonstrates faster training efficiency, requiring 185–195 ms/step, while AE-LSTM takes 208–215 ms/step under the same conditions. AE-GRU's superior accuracy and efficiency are attributed to its simplified gating structure and the feature compression capability of the autoencoder, which enhances learning stability and generalization. Overall, the AE-GRU model offers robust predictive performance and computational efficiency. It is a reliable framework for real-time cryptocurrency forecasting and a promising foundation for advanced deep learning architectures in financial time-series analysis.
Comparison of Carbon Utilization Technologies for Decarbonization Strategy in the Ammonia Industry Daril Ridho Zuchrillah; Rizal Arifin; Friska Dwi Pratiwi; Niken Rani Nastiti; Achmad Dwitama Karisma; Ardista Izdhihar Kaloka; Soeprijanto Soeprijanto
SINERGI Vol. 30 No. 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.2.024

Abstract

Global climate change is caused by high greenhouse gas (GHG) emissions. The industrial sector is one of the most significant contributors of emissions, particulary from production activities and the use of fossil fuels. The ammonia (NH3) industry is an important chemical sub-sector that still relies on fossil fuels and contributes 1.6 tons of CO2 emissions per ton of ammonia production, accounting for nearly 2% of global carbon emissions. Therefore, Carbon Capture Utilization (CCU) technology is needed to support the decarbonization of the industry. This study uses Aspen Plus V14 software for process simulation. Three CO2 utilization pathways were simulated: methanol production (CH3OH), sodium bicarbonate production (NaHCO3), and methane production (CH4). The result show that converting CO2 into sodium bicarbonate (NaHCO3) yields the most favorable result with a profit of $65,277,360/year, a positive NPV of $840,647,028, an IRR exceeding the bank interest rate (10%) at 54%, and a POT in the fifth year. Additionally, sodium bicarbonate is environmentally sustainable, as evidenced by a CO2 emission reduction rate of 96%. The assessment was carried out under the assumptions of stable market conditions, sufficient availability of green hydrogen and ideal operating parameters, However, this study acknowledges inherent limitations, including catalyst performance, high energy requirements, and the challenge of integration with existing infrastructure, which may hinder large-scale implementation.

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