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asset@upgris.ac.id
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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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Kota semarang,
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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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Digital Transformation of Import Logistics for Operational Efficiency: Case-Based Evidence from the Plastics Industry Kurniawan, Fajar Indra; Alamsjah, Firdaus
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.1967

Abstract

This study aims to explore the use of digital systems to reduce import process costs, increase the percentage of national direct deliveries from ports to consumers, and analyze the impact of proposed improvement measures. The research employs a single case study approach with data collected through observation, document analysis, and quantitative data collection from one of the biggest plastic resin distribution companies in Indonesia. The data were analyzed using the CIMO (Context-Intervention-Mechanism-Outcome) logic to understand the context and impact of the interventions implemented. The findings reveal that the application of digital technologies such as the Internet of Things (IoT), Blockchain, and cloud-based management systems with real-time container tracking digital applications can optimize supply chain processes and sustainability. There was a notable reduction in the national average container storage costs at ports from $2.4/Ton to $1.8/Ton and an increase in the average percentage of national direct deliveries from ports to consumers from 17.3% to 22.0% before and after the implementation of the container tracking system. These results confirm that adopting digital technologies in plastic resin distribution companies not only improves operational efficiency, provides a competitive advantage in the market but also reduces carbon emission by using single trip directly to customers. Companies are advised to enhance employee training related to new technologies and adopt integrated systems within supply chain processes. Furthermore, continuous innovation using technologies such as artificial intelligence is essential for maintaining industry competitiveness and sustainability.
Recent Advances in Catalytic Systems for the Sustainable Synthesis of Ethyl Levulinate from Biomass Mhd. Shaumi Al Anshar; Luqman Buchori; Didi Dwi Anggoro; Setia Budi Sasongko; Istadi
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.1973

Abstract

The esterification of levulinic acid to ethyl levulinate presents challenges in catalyst efficiency, reusability, and environmentally friendly process design, restricting commercial scalability.  This study examines recent studies on diverse catalysts, including Deep Eutectic Solvents (DES), homogeneous and heterogeneous systems, and their effects on yield.  DES is positioned as a more sustainable option, with yields as high as 99.8%, quicker reaction times, and a lower environmental effect.  While heterogeneous catalysts require harsher conditions and have reusability difficulties, DES provides a greener and more efficient alternative to produce ethyl levulinate.  Life cycle assessments (LCA) of DES procedures reveal reductions in energy usage and greenhouse gas emissions of up to 69.72%.  Future research should focus on improving DES recovery and scalability for industrial applications.  This effort supports the United Nations' Sustainable Development Goals (SDGs), namely SDG 7 (Affordable and Clean Energy), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action).
Computational Assessment of Orthopedic Implant Durability Using Finite Element Analysis Haryanto, Ismoyo; Bagastomo, Riondityo Soni; Ismail, Rifky; Siregar, Januar Parlaungan; Cionita, Tezara
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.1977

Abstract

Finite Element Analysis (FEA) provides a rapid and cost-effective method to evaluate orthopedic implants. This research investigates the mechanical performance and long-term durability of a seven-hole SS 316L Basic Fragment Set (BFS) reconstruction plate designed for pelvic fractures. Adhering to ASTM standards, material properties were defined via tensile testing (ASTM E8), while static and fatigue analyses were performed using a displacement control method in a four-point bending test setup in SOLIDWORKS 2024 (ASTM F382). The static analysis predicted failure from plastic deformation at a force of 367 N, with a maximum stress of 621.92 MPa. The fatigue simulation predicted a lifespan of 483,754 cycles. To validate the simulation, these computational results were compared to experimental data, demonstrating high accuracy with deviations of only 3.34% for maximum force and 1.19% for fatigue life. These findings confirm that FEA is a highly reliable tool for predicting mechanical performance, enabling the orthopedic industry to optimize implant designs, enhance patient safety, and improve production efficiency.
Artificial Neural Network-Based Forecasting of Rice Yield Using Environmental and Agricultural Data Priyanto; Muhammad Faisal; Mochamad Imamudin
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.2019

Abstract

This study presents a high-accuracy predictive model for rice production in Indonesia using Artificial Neural Networks (ANN), achieving an R² of 98.11%, Mean Absolute Error (MAE) of 0.0966, and Mean Squared Error (MSE) of 0.0189. Climate variability remains a significant challenge to rice cultivation in regions like Malang City, where unpredictable environmental factors such as rainfall, temperature, and humidity hinder effective crop planning and yield estimation. To address this, we developed a Multilayer Perceptron (MLP)-based ANN model incorporating agro-environmental variables: rainfall, temperature, humidity, harvested area, and production quantity. Historical data from 2009 to 2024 were sourced from the Meteorology, Climatology, and Geophysics Agency (BMKG) and the Central Statistics Agency (BPS). The dataset underwent preprocessing, including cleaning, feature extraction, Z-Score normalization, and partitioning into training and testing sets. The proposed ANN architecture consists of an input layer, three hidden layers, and an output layer for regression tasks. Comparative evaluation against Random Forest, K-Nearest Neighbors, and Support Vector Regression demonstrated the ANN’s superior ability to model complex nonlinear relationships in agricultural data. The results highlight the role of intelligent data-driven systems in enhancing the accuracy of yield forecasting, supporting sustainable agricultural practices, and informing national food security policy.
Optimizing Human Resource Performance in Building Construction through Technology-Enhanced Strategy Development Husodo, Ibnu Toto; Pratikso, Pratikso; Wibowo, Kartono
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.2083

Abstract

Construction projects involve complex processes requiring effective management and skilled human resources to ensure successful outcomes. This study analyzes key factors influencing human resource (HR) performance in building construction, identifying ability, working conditions, organizational structure, motivation, discipline, and compensation as critical determinants. A structured questionnaire using a 5-point Likert scale was employed as the data collection instrument, distributed to 130 contractors, with 114 valid responses collected in Semarang, Indonesia. Data analysis using SPSS v.27 confirmed that all indicators are valid, reliable, and positively perceived, with “ability” receiving the highest rating (mean = 4.8). Practical implications for project stakeholders include the need to implement targeted training, performance-based incentives, leadership development, and optimized recruitment. Technological integration is also emphasized for enhancing communication and decision-making efficiency. The findings underscore the importance of a comprehensive HR strategy that addresses both individual competencies and systemic organizational support, advancing sustainable engineering practices and improving project productivity in dynamic construction environments.
An Artificial Neural Network Approach for Predicting Pavement Distress: A Case Study Toward Sustainable Road Maintenance Yogi Oktopianto; Antonius; Rochim, Abdul
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.2133

Abstract

The Surface Distress Index (SDI) is a crucial parameter to consider when determining road conditions as part of an effective maintenance strategy. This study aims to develop an SDI prediction model using road surface distress data to enhance maintenance planning. The developed Artificial Neural Network (ANN) model resulted in an optimal structure with two hidden layers comprising 6 neurons and 4 neurons, respectively. The model was trained using two years of surface distress data collected from 40 road sections managed by the city’s road maintenance division. Variables used included Composition, Condition, Depression, Patches, Damage types, Crack Area, and Crack Width. The results demonstrated high accuracy in predicting SDI, with model performance achieving an R² of 0.87. This model can be applied to optimize the efficiency of road maintenance strategies.
Comparative Analysis of Observed and Empirical Rainfall Distribution for Flood Hydrograph Modeling Afifah, Risdiana; Sri Sangkawati; Suripin; Dyah Ari Wulandari
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.2142

Abstract

Flood disasters in Indonesia are persistent challenges during the rainy season, primarily due to intense rainfall and inadequate flood control. This study evaluates hourly rainfall to characterize hydrology and predict flood discharge more accurately, benefiting water infrastructure planning. The research used modified Mononobe methods, observational data, and rainfall-runoff modeling, including HEC-HMS simulations with the SCS-CN unit hydrograph. Observed rainfall simulated a flood discharge of 779.7 m³/s, while empirical rainfall yielded 3623 m³/s, showing a 79.12% deviation. Comparing flood hydrographs, recorded rainfall data closely matched previous studies (R² = 0.94), unlike empirical rainfall (R² = 0.88). The study concludes that observed rainfall is highly effective for estimating flood runoff, accurately representing local characteristics. This method significantly aids planning and design of water resource infrastructure like dams, weirs, and bridges at the study site.
Predicting Habitat Suitability of Mahseer Fish (Tor spp.) in Tropical River Systems Using MaxEnt and Google Earth Engine: A Geospatial Modeling Approach Jefri Permadi; Nia Kurniawan; Diana Arfiati; Agung Pramana Warih Marhendra
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.2201

Abstract

Rivers are vital freshwater habitats that face threats of degradation and climate change. Mahseer fish, a key species, is in decline. This study predicted Mahseer fish habitats in Central Java using the Google Earth Engine and the MaxEnt machine learning algorithm. Environmental predictors, including NDVI, elevation, slope, river order, temperature, and rainfall, were extracted from Sentinel, SRTM, MODIS, and CHIRPS data. The model identified river order as the most influential variable (73%), followed by elevation (18%) and rainfall (8%), with an AUC score of 0.7, indicating fair accuracy. Suitable habitats were located in upstream river orders (1–3), typically at higher elevations. These findings provide spatial guidance for conservation planning, such as identifying critical habitats, prioritizing upstream areas, and establishing seasonal fishing ban. This approach supports biodiversity protection and aligns with the Sustainable Development Goals by offering a scalable tool for freshwater ecosystem management. Using MaxEnt with GEE shows promise for rapid, and cost-effective species distribution modeling in data-limited tropical regions.
Evaluating the Role of Extractives in Biomass Pyrolysis for Enhanced Hydrogen Syngas Production Suprianto, Teguh; Muhammad Kasim; Darmansyah
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.2132

Abstract

This study explores how extractive content in lignocellulosic biomass affects syngas quality during fixed-bed pyrolysis-gasification, specifically focusing on hydrogen (H₂) concentration. While woody biomass is a known energy source, the link between its non-structural organic compounds (extractives) and H₂ in syngas is often overlooked. We investigated teak, coconut, and jackfruit wood to understand this influence and optimize temperature for better biomass-to-hydrogen conversion. An MQ-8 sensor detected H₂ levels. Results show that biomass with high extractive content significantly boosts syngas H₂. Jackfruit wood yielded the highest H₂ concentration (2898 ppm at 471°C), outperforming coconut wood (1965 ppm at 444°C) by 41.7% and teak wood (1931 ppm at 395°C) by 50.1%. This is due to jackfruit's high cellulose and extractive content, which decompose efficiently at higher temperatures. Overall, high-extractive biomass improves syngas quality and expands sustainable options for hydrogen production.
Biosorption of Chromiun in Batik Wastewater Using SCOBY Microbial Biomass: A Sustainable Bioremediation Approach Nur Lu’lu Fitriyani; Dina Adelia; Slamet Budiyanto; Ristiawati; Jaya Maulana; Muhammad Choiroel Anwar
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.2199

Abstract

Batik wastewater poses an environmental threat due to hazardous heavy metals like lead, cadmium, and chromium (Cr). This study investigated the effectiveness of SCOBY (Symbiotic Culture of Bacteria and Yeast), a microbial consortium from kombucha production, in reducing Cr levels in batik wastewater. SCOBY is a promising biosorbent for heavy metals. The research aimed to assess SCOBY's ability to decrease Cr contamination in different types of batik wastewater (hand-drawn, stamped, and printed) over varying incubation times. Using a quasi-experimental approach, wastewater samples were collected from small and medium industries in Pekalongan City. Results showed that SCOBY effectively reduced Cr levels across all batik wastewater types and incubation periods. The most significant reduction occurred at 3 hours of incubation. Specifically, Cr levels decreased by 53% in hand-drawn batik wastewater, 44% in stamped batik wastewater, and an impressive 71% in printed batik wastewater. These findings suggest that SCOBY treatment is a viable and effective alternative for managing batik wastewater.

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