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Advance Sustainable Science, Environmental Engineering and Technology (ASSET) Jl. Sidodadi Timur No.24, Karangtempel, Kec. Semarang Tim., Kota Semarang, Jawa Tengah 50232
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INDONESIA
Advance Sustainable Science, Engineering and Technology (ASSET)
Published by Universitas PGRI Semarang
ISSN : -     EISSN : 27154211     DOI : https://doi.org/10.26877/asset
Core Subject : Science, Engineering,
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering
Advance Sustainable Science, Engineering and Technology (ASSET) is a peer-reviewed open-access international scientific journal dedicated to the latest advancements in sciences, applied sciences and engineering, as well as relating sustainable technology. This journal aims to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of sciences, engineering, and technology. The Scope of ASSET Journal is: Biology and Application Chemistry and Application Mechanical Engineering Physics and Application Information Technology Electrical Engineering Mathematics Pharmacy Statistics
Arjuna Subject : Teknik Kimia (semua kategori) - Teknik Kimia (Lain-Lain)
Articles 272 Documents
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Enhancing Energy Efficiency in Industrial Warehouses Using Life Cycle Cost Analysis (LCCA) Jaya, Pratama; Roni Sahroni, Taufik
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/6pd8qm77

Abstract

Industrial warehouses use inefficient lighting systems that occupy a significant portion of energy consumption and operating expenses. The aim of this study is to attain higher energy efficiency and reduce the lighting cost by replacing conventional high-pressure mercury vapor (HPL-N) lamps with LEDs in a warehouse environment. The study employs the Life Cycle Cost Analysis (LCCA) methodology to evaluate long-term cost-effectiveness and save energy. Data were collected using literature review, field surveys, and light simulation using Dialux software at PT XYZ warehouse in Cilegon, Indonesia, between January and June 2024. Findings reveal that the use of LED lighting, in conjunction with the optimization of the light point number, reduces energy consumption by 65%, saving 121,929.816 kWh of energy annually. Within 11 years, installation of LED saves an amount of Rp 947,683,204 compared to Rp 1,796,422,585 with HPL-N systems—an amount of 50.66% or Rp 848,739,381 saved. The results show the energy and economic benefits of the use of LED in industrial warehouses to be advantageous for national energy saving as well as satisfying light standards.
Implementing Lean Manufacturing Using Value Stream Mapping for Automotive Maintenance Efficiency Sinatrya, Yusha; Diah Ekawati, Ardhianiswari
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2091

Abstract

Transportation is very vital to society these days as it provides mobility and maintains economic growth. In January 2022, Indonesia was Southeast Asia's largest auto market. PT Blue Bird Tbk., one of the largest car rental and transportation companies in Indonesia, faced very critical problems involving workshop efficiency and customer satisfaction. This study will optimize the car repair process via the implementation of Lean Manufacturing principles with the Digital Value Stream Mapping (DVSM) technique. Identification of waste was conducted via Waste Assessment Model (WAM) and a directed questionnaire, wherein the three major types of waste were overproduction (21.07%), defects (16.89%), and waiting (16.65%). For these, two VALSAT tools—Process Activity Mapping and Supply Chain Response Matrix—were employed. Simulation using Arena software was performed to validate the proposed improvements. Upon implementation, the lead time for car repair decreased from 12.887 seconds to 12.203 seconds, a decrease of 5.3%. The research indicates that lean tools can be effectively applied in car maintenance services, particularly in developing countries, to increase operating efficiency and service performance.
Determinants of MSME Sustainability: A Regression-Based Study in Labuhanbatu Ritonga, Mulkan; Nasution, Ade Parlaungan; Muti’ah, Rahma
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2180

Abstract

This study aims to identify factors that influence the development of healthy and independent MSMEs in Labuhanbatu Regency, Indonesia.  The research method uses a quantitative approach with primary data collection through interviews and questionnaires distributed to 342 MSME actors in Labuhanbatu Regency. The sample was taken using a stratified random sampling technique based on the 2019 MSME population. The data were analyzed using multiple regression techniques to test the effect of the independent variables partially and simultaneously on the dependent variable. The results showed that education and skills (β = 25.299, p < 0.01), locus of control (β = 4.452, p < 0.01), government support and policies (β = 18.001, p < 0.01), and access to capital and financial resources (β = 9.332, p < 0.01) have a positive and significant influence on the development of healthy and independent MSMEs. In contrast, financial literacy (β = 1.025, p > 0.1), partnership networks (β = 1.005, p > 0.1), and infrastructure and technology (β = 1.087, p > 0.1) did not contribute significantly in this study. These findings emphasize the need for human resource capacity building and government policies that support access to capital as key strategies to encourage the sustainability and self-reliance of MSMEs in the region.
Physics-Informed Neural Network with Thevenin Equivalent Circuit for Accurate SOC Li-ion Battery Estimation Apribowo, Chico Hermanu Brillianto; Ashidqi, Muhamad Dzaky; Arifin, Zainal; Santoso , Henry Probo
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2613

Abstract

Accurate state of charge (SOC) estimation is essential for the safety, performance, and longevity of lithium-ion batteries. Physics-based models such as equivalent circuit models (ECMs) are computationally efficient but struggle under nonlinear and time-varying conditions, whereas purely data-driven approaches often lack interpretability. This study proposes a hybrid framework that integrates a physics-informed neural network (PINN) with a first-order Thevenin ECM for dynamic SOC estimation using only terminal voltage and current inputs. The method incorporates physically derived parameters including open-circuit voltage (OCV), polarization resistance, and capacitance identified through pulse testing. An eighth-order OCV–SOC polynomial regression optimized with a genetic algorithm (GA) enables nonlinear mapping, while the Newton–Raphson (NR) method is applied for final SOC estimation. Experimental validation was performed on 18 Ah lithium iron phosphate (LFP) cells over 300 charge–discharge cycles at 20 °C, extended up to 2000 cycles under 1C/2C rates with cut-off voltages of 3.7 V and 2.7 V. Comparative analysis with extended kalman filters (EKF) and standard neural networks (NN) demonstrates the superiority of the proposed method, achieving a root mean squared error (RMSE) of 0.103, mean absolute percentage error (MAPE) of 0.702%, and coefficient of determination (R²) of 0.998. By embedding physical constraints into the learning process, the PINN enhances accuracy, robustness, and generalizability, while reducing estimation uncertainty, thereby offering a scalable and interpretable solution for real-time battery management systems (BMS) in electric vehicles (EVs) and battery energy storage systems (BESS).
Synthesis of Silica Gel From Rice Husk Ash for Sustainable Air Conditioning Requirements Syahbardia; Berkah Fajar Tamtomo Kiono; Sonny Handojo Winoto; Mohamad Said Kartono Tony Suryo; Dedi Lazuardi
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.1944

Abstract

An increase in air conditioning demand, driven by global warming and the need for comfort, highlights the importance of energy-efficient systems like desiccant cooling. This research explores using rice husk ash (RHA), an agricultural waste product from Indonesia, to synthesize silica gel for these systems. The study involved synthesizing silica gel from RHA through chemical processes and comparing its sorption capacity to commercial silica gel. Two synthesis methods were tested: a direct reaction of water glass with acid compounds and an impregnation process on honeycomb walls. The results indicate that the direct reaction method produces a silica gel with better pore performance. Ultimately, the study found that RHA from West Java, Indonesia, is a viable raw material for desiccant air conditioning, although its sorption capacity is slightly lower than that of commercial silica gel. This offers a valuable use for agricultural waste.
Integrating Occupational Safety and Health Management Systems (SMK3) with Total Quality Management (TQM) in Micro-Scale Manufacturing Enterprises Heronasia, Sani; Zuraida, Rida
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2054

Abstract

Improving a company's Occupational Safety and Health Management System (SMK3) is key to achieving Total Quality Management (TQM) goals. This study uses questionnaires, fishbone diagrams, and Fault Tree Analysis to evaluate SMK3 implementation at a micro-scale manufacturing business, Malang Roster. The primary goal is to ensure work quality while maintaining a sustainable safety system. Findings show the most significant issues are low discipline in using personal protective equipment (PPE), poor supervision, and outdated equipment. Other challenges include inadequate health insurance planning and the use of subpar equipment. While safety signs are present, unauthorized access to hazardous areas remains a problem, requiring corrective action. To effectively integrate SMK3 into a TQM framework, companies must strengthen provisions, implementation, and monitoring. This study suggests that consistent supervision, targeted training, and structured audits are crucial for improving occupational safety in small manufacturing environments.
Boosting-Based Machine Learning Models and Hyperparameter Tuning for Predicting Vehicle Carbon Dioxide Emission Ridwan Petervan Siburian, Firman; Suharjito
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2097

Abstract

Sustainable development and climate change are central agendas in global policy and research. This study examines and compares three ensemble learning models using Gradient Boosting Machine, Categorical Boosting, and Extreme Gradient Boosting for forecasting vehicle carbon dioxide (CO2) emissions. Data preprocessing with Interquartile Range (IQR) and median imputation is among the methods used to address missing values in CO₂ rating and smog rating variables. SHAP and PDP were employed for feature importance analysis and model interpretability. The findings from the third experiment demonstrate that Extreme Gradient Boosting (XGBoost) outperformed other models achieving a Coefficient Determination of 0.9988, Root-Mean-Square Error of 2.1696, Mean-Absolute Error of 0.4977, and Mean-Absolute-Percentage Error of 0.0019. The primary predictive features included combined fuel consumption (liters/100 km), city and highway fuel consumption, ethanol fuel consumption, model year, engine size and diesel consumption. The findings suggest the potential of boosting-based models for supporting sustainable transport planning, policy for emission reduction, and evidence-based policy making.
High-Resolution Smart Card-Based OD Matrix for Optimizing Jakarta’s LRT Operations Fadillah, Ikhsan Rahmat; E. Gunawan, Fergyanto
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2254

Abstract

Efficient urban mobility is essential to support transportation planning and policy. However, traditional methods are often limited in data resolution, lacking the ability to describe passenger movement dynamics in detail. This study aims to analyze passenger mobility patterns using high-resolution tap-in/tap-out data from the closed-loop LRT system in Jakarta during January-February 2025. The methods used include constructing an origin-destination (OD) matrix based on 185,512 trip records, as well as temporal and spatial analysis of passenger flows. The results showed the existence of peak hour patterns on weekdays (07.00-09.00 and 17.00-19.00), trip spikes on weekends and holidays (14.00-18.00), and high flow concentrations at interchange stations such as Velodrome and North Boulevard. While data from the closed system allows for accurate trip tracking, potential data gaps due to technical errors or user behavior remain a concern for long-term analysis. The findings suggest that high-resolution smart card data can provide operationally relevant insights for short-term decision-making, such as schedule adjustments or fleet allocation. However, for long-term strategic planning, integration with predictive models and other planning tools remains necessary. This research fills a gap in the literature by showing that even limited-duration datasets can be leveraged to effectively support data-driven transportation management.
Hydrogeochemical Characterization and Subsurface Flow Analysis of Volcanic Hot Springs Riogilang, Hendra; Octovian Berty Alexander Sompie; Riogilang, Herawaty
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.1818

Abstract

Understanding of the hydrogeochemical system of volcanic hot springs in Tompaso, Minahasa, is still limited, especially regarding the origin of water and subsurface flow patterns. This study aims to classify geothermal water types and analyze groundwater flow systems in volcanic environments. A total of 15 hot spring samples were analyzed using field measurements (temperature, pH, electrical conductivity), ion chromatography, titration, and spectrophotometry. Data validation was done with ion balance, and water classification using Piper diagrams. The results showed four main types of water, namely Mg-SO₄, H-SO₄, Na-Cl, and Na-SO₄. Most of the samples are from meteoric water directly heated by the geothermal system, while two samples (HT-11A and HT-11B) were heated by steam. These findings provide a scientific basis for sustainable geothermal exploration and groundwater conservation in volcanic areas with variable geothermal systems at the study site
Secure Visual Image Encryption Using Lorenz Chaos, Steganography, and Wavelet-Based Steganography Authentication Aqilah Wijaya, Salsabil Farah; Wahida, Ida; Koredianto, Koredianto
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2069

Abstract

Medical images play a vital role in diagnosis and clinical decision-making, yet their transmission and storage pose significant privacy and security challenges. This research proposes a visual stego-encryption system that integrates the Lorenz chaotic algorithm with Discrete Wavelet Transform (DWT) to embed both secret medical data and a doctor's digital signature into a visually meaningful encrypted image (VMEI). The system employs dual-layer embedding and role-based access control, allowing administrators to input patient and doctor data while enabling doctors to perform secure validation and decryption. A series of evaluation scenarios were conducted, including variations in image resolution, geometric transformations (rotation), Lorenz initial conditions, alpha embedding parameters, and multivariate optimization, along with user-role-based validation testing. Performance metrics based on Peak Signal-to-Noise Ratio (PSNR) and Bit Error Rate (BER) demonstrate that the system consistently achieves high visual fidelity (PSNR > 30 dB) and low data loss (BER ≈ 0) across all image types. The optimal configuration—using a 4096×4096 carrier, 1024×1024 secret, and 256×256 signature with α₁ = 0.28, α₂ = 0.07, and initial condition (0.2, 0.8, 1.5)—resulted in a PSNR of 33.01 dB for the secret image. These results confirm that the proposed system provides a robust, secure, and visually accurate method for medical image encryption, suitable for integration into real-world digital healthcare infrastructures.

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