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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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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 386 Documents
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CFD-Based Thermohydrodynamic Analysis of Vegetable Oil Lubricants in Journal Bearings Muchammad; Budi Setiyana; Agus Suprihanto; Achmad Widodo
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): 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.v8i3.3607

Abstract

This study investigates the thermohydrodynamic performance of journal bearings lubricated with palm trimethylolpropane (TMP) ester as a biolubricant, compared to conventional engine oil, using computational fluid dynamics (CFD) in ANSYS Fluent. The objective is to evaluate the influence of lubricant type on pressure distribution, load carrying capacity, friction force, and cavitation behavior. The results show that palm TMP ester generates higher hydrodynamic pressure while maintaining a similar pressure distribution pattern to engine oil. The load carrying capacity increases significantly by approximately 493% at 48 rad/s and 343% at 68 rad/s compared to engine oil. However, this improvement is accompanied by an increase in friction force of about 280% and 234% at the respective speeds due to higher viscosity. In addition, the vapor volume fraction ranges from 0.69 to 0.73, indicating cavitation, with palm TMP ester showing a slightly higher tendency. These findings demonstrate a trade-off between enhanced load support and increased friction, highlighting the potential of palm TMP ester as an environmentally friendly lubricant for hydrodynamic bearing applications.
A Comparative Evaluation of Base Isolation Effectiveness in Mitigating Seismic Response for Low-Rise and Mid-Rise Buildings I Putu Ellsa Sarassantika; I Gusti Ngurah Agung Eka Arya Tejadinata
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): 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.v8i3.3619

Abstract

Low-rise and mid-rise structures often possess natural periods that coincide with high-energy seismic response plateaus, rendering them susceptible to significant response amplification. This study evaluates the comparative effectiveness of High Damping Rubber Bearings (HDRB) in mitigating these risks for 4-story and 8-story office buildings under Indonesian seismic conditions. Using rigorous Non-Linear Time History Analysis (NLTHA) in the dominant X-direction, the research examines period elongation, lateral displacement, inter-story drift, peak floor acceleration, and base shear attenuation. Scientific findings reveal that HDRB transitions structural behavior into a near-rigid-body motion, reducing normalized inter-story drift to 0.26 and 0.12, and base shear by up to 71%. Furthermore, peak floor acceleration is curtailed by 51% to 59%, ensuring the protection of sensitive non-structural components. The results demonstrate that while base isolation provides superior seismic protection for both building scales, its efficacy in intercepting seismic energy and preventing operational disruptions is significantly more pronounced in mid-rise structures. This study confirms that HDRB is an increasingly vital strategy for enhancing functional resilience as building height increase.
Low-Dose Salinomycin Targets Multidrug Resistance via P-Glycoprotein and NF-κB in Osteosarcoma Cells Onarisa Ayu; Muhammad Rusda; Rosita Juwita Sembiring; Iqbal Pahlevi Adeputra Nasution; Ferdiansyah Mahyudin; Mustafa Mahmud Amin; Hotma Partogi Pasaribu; Tina Christina Lumban Tobing
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): 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.v8i3.2447

Abstract

Chemotherapy resistance in osteosarcoma remains a major clinical obstacle, largely driven by drug efflux mechanisms mediated by P-glycoprotein (ACBC1/ABCB1) and activation of NF-κB signaling (NFKB1). This study investigated the potential of Salinomycin to modulate these multidrug resistance–associated genes in U2OS osteosarcoma cells and explore its role in overcoming chemoresistance. A true experimental post-test-only control group design was used. U2OS cells were treated with varying concentrations of Salinomycin, doxorubicin, and their combination. Cell viability was assessed using MTT assay, while gene expression of ACBC1 and NFKB1 was quantified using qRT-PCR with SYBR Green chemistry. Relative expression levels were analyzed using the 2−ΔΔCt method normalized to GAPDH. Salinomycin demonstrated dose-dependent cytotoxic effects in the low micromolar range. At lower concentrations, it significantly reduced NFKB1 expression, while also showing a tendency to downregulate ACBC1. However, intermediate concentrations showed variable effects, including a transient increase in NFKB1 expression. The combination treatment with doxorubicin produced only modest and non-significant changes in both resistance-related genes. Overall, low-dose Salinomycin exhibited a more consistent suppressive effect on NF-κB signaling, suggesting a potential role in sensitizing osteosarcoma cells to chemotherapy. In contrast, higher doses primarily enhanced cytotoxicity without clearly improving suppression of resistance markers. This study highlights a novel dual-action, dose-dependent regulatory effect of Salinomycin at the transcriptomic level, targeting both drug efflux (ACBC1/ABCB1) and survival signaling (NF-κB/NFKB1). These findings provide new insight into its potential as an adjunct agent in overcoming multidrug resistance in osteosarcoma and warrant further validation at the protein and functional levels.
Design and Evaluation of Hazard Analysis Procedures in Mining Occupational Safety Programs Azhari Alriza; Ardhianiswari Diah Ekawati
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): 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.v8i3.2490

Abstract

Workplace accidents in the mining industry remain high, highlighting the need for systematic hazard analysis to strengthen Occupational Health and Safety (OHS) programs. This study aims to design and evaluate a Hazard Analysis Procedure (HAP) model for PT. XYZ. Using quantitative methods, the research applied the Fuzzy Delphi technique to validate OHS parameters and the Fuzzy Analytic Hierarchy Process (FAHP) to determine their relative weights. Data were obtained from ten certified OHS experts through structured questionnaires. Twenty-four indicators were identified, grouped into six main criteria: management leadership, worker participation, hazard identification and assessment, hazard prevention and control, program evaluation and improvement, and accident frequency. FAHP results showed that management commitment (0.092), hazard identification (0.088), and program evaluation (0.081) were the most influential indicators. A 95% threshold was proposed as the benchmark for successful HAP implementation. Findings provide practical recommendations and support continuous improvement of mining OHS management systems.
Application of LSTM and MODIS Satellite Imagery for Forecasting Oceanographic Dynamics and Identifying Potential Fishing Zones in the Sunda Strait Muta Ali Khalifa; Muchtar Ali Setyo Yudono; Nico Wantona Prabowo; Prakas Santoso; Farhan Rachmanto; Aditya Teguh Prasetia
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): 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.v8i3.2815

Abstract

This study integrates AQUA-MODIS satellite imagery with the Long Short-Term Memory (LSTM) model to forecast oceanographic dynamics and identify Potential Fishing Zones (PFZ) in the Sunda Strait. The dataset spanning from January 2014 to December 2024 was used for model training, while forecasts for January–August 2025 were validated using in-situ observations from six sampling stations. The model predicted sea surface temperature (SST), chlorophyll-a concentration, and ocean current speed, with SST reaching 31°C, chlorophyll-a at 3.5 mg/L, and peak current speeds of 0.4 m/s. The performance metrics for SST (MSE: 1.107, RMSE: 0.994, MAD: 0.794), chlorophyll-a (MSE: 1.609, RMSE: 1.011, MAD: 0.5739), and current speed (MSE: 0.0183, RMSE: 0.1223, MAD: 0.0959) confirmed model accuracy. The PFZ detection algorithm, based on SST, chlorophyll-a, and ocean current data, demonstrated strong spatial agreement with in-situ data, validated using metrics such as MSE and RMSE. This validation approach, employing direct in-situ comparison, supports effective fisheries management by identifying productive fishing areas under varying seasonal and climate conditions. These results underline the operational potential of the LSTM-based forecasting framework for adaptive fisheries decision-making in the Sunda Strait.
Optimization of Tracking Algorithm on Mouse Movement Monitoring Platform in Medical Testing Sutrisno Ibrahim; Rahmat Rohmani; Joko Hariyono; Faisal Rahutomo; Nanang Wiyono; Ratih Yudhani
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): 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.v8i3.2879

Abstract

Accurate monitoring of mouse behavior in the Elevated Plus Maze (EPM) is essential for anxiety-related biomedical research, yet manual observation is time-consuming, subjective, and prone to human error. This study proposes an optimized automated tracking framework that integrates YOLOv8 detection with tracking methods including an adaptive Kalman Filter and DeepSORT, and compares them with conventional trackers such as CSRT and GOTURN. System performance was evaluated using Intersection over Union (IoU), Center Location Error (CLE), and Frames Per Second (FPS), with the Weighted Scoring Method (WSM) used for overall performance comparison. Experimental results show that the proposed YOLOv8 with adaptive Kalman filtering (frame interval = 5) provides the best balance between accuracy and computational efficiency. The approach achieved an IoU of 0.89 and CLE of 2.34 while increasing processing speed from 10.44 FPS to 22.55 FPS, representing an improvement of approximately 116% compared with the baseline configuration. Despite a slight increase in failure rates, the framework maintained stable real-time tracking performance under laboratory conditions. These results demonstrate that the proposed system improves both tracking efficiency and robustness, offering a reliable automated solution for high-throughput behavioral monitoring. The framework is particularly suitable for laboratory automation environments, supporting more objective behavioral assessment and improved data integrity in preclinical biomedical research.
FTFPOS-IDF: A Fuzzy Rule-Based Thematic Term Weighting Scheme for Bloom's Taxonomy Question Classification Sucipto Sucipto; Didik Dwi Prasetya; Triyanna Widiyaningtyas
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): 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.v8i3.2957

Abstract

The increasing adoption of Artificial Intelligence (AI) in education has created a growing demand for automated and reliable assessment systems. Existing Bloom’s Taxonomy (BT) question classification approaches commonly rely on TF-IDF-based weighting schemes, which assign static term weights and often fail to capture the varying thematic importance of terms across cognitive levels. To address this limitation, this study proposes a novel Fuzzy Thematic Feature and Part-of-Speech Inverse Document Frequency (FTFPOS-IDF) weighting scheme that integrates fuzzy rule-based reasoning with Natural Language Processing (NLP) to dynamically assign thematic weights according to Bloom’s Taxonomy relevance. The proposed framework combines Machine Learning (ML) and Deep Learning (DL) classifiers with Chi-Square feature selection to reduce irrelevant features and improve classification performance. Experimental results demonstrate that FTFPOS-IDF consistently outperforms conventional TF-IDF variants across multiple classification models. The highest performance was achieved by the Multilayer Perceptron (MLP) classifier with an accuracy of 86.7%. These findings indicate that fuzzy rule-based thematic weighting can effectively enhance Bloom’s Taxonomy question classification and support scalable, reliable, and sustainable digital assessment systems in educational environments.
Metaheuristic Optimization Stacking Application for Rainfall Classification: A Comparative Study Rachmat Bintang Yudhianto Yudhianto; Agus Mohammad Soleh Agus Mohammad Soleh; Anang Kurnia
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): 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.v8i3.3061

Abstract

Accurate rainfall classification plays a vital role in effective meteorological forecasting, agricultural planning, and also to avoid natural disasters. However, standard classification models often exhibit unstable performance. This study evaluates the effectiveness of an Ensemble Stacking framework enhanced with Optimized Swarm Based and Metaheuristic method such as Artificial Bee Colony (ABC) and Cuckoo Search (CS) optimization algorithms to improve prediction reliability. The proposed approach was tested using a detailed rainfall dataset by combining basic classification models, such as Decision Tree, SVM, Naive Bayes, and kNN. The results show that Stacking Ensemble generally outperform individual basic models in Accuracy and F1 Score (reaching a median > 0.80), while unoptimized Stacking method show low variances but provides a less better result in terms of Accuracy and F1 Score. In contrast, the Stacking model optimized with ABC emerged as a better method, demonstrating the highest stability and significantly reducing the performance distribution range compared to the non-optimization and Cuckoo Search scenarios. These findings conclude that the application of Artificial Bee Colony optimization to Stacking ensembles effectively minimizes prediction variance, making it the most reliable strategy for consistent rainfall forecasting using classification modelling technique. 
Scalable TOPSIS Variants in Web-Based Decision Support Systems: A Performance Benchmarking Study on Vectorization and Uncertainty Modelling Ghufron Abdullah; Nugroho Dwi Saputro; Nurkolis
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): 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.v8i3.3122

Abstract

This study systematically benchmarks the computational performance and decision consistency of three TOPSIS variants—classical TOPSIS, vectorized TOPSIS, and Z-TOPSIS—in scalable web-based multi-criteria decision support systems (DSS). Although TOPSIS is widely used due to its simplicity and interpretability, its scalability and computational behavior under concurrent web workloads remain insufficiently explored. To address this gap, the algorithms were evaluated using datasets containing 50–500 alternatives and 10 criteria, representing realistic decision-making scenarios. Classical TOPSIS was used as the baseline, vectorized TOPSIS applied matrix-based optimization, and Z-TOPSIS incorporated Z-numbers to capture uncertainty. Experiments were conducted on a cloud-based DSS equipped with multi-core CPUs and 16–32 GB RAM, measuring execution time, throughput, response time, and ranking consistency under workloads of 50–500 concurrent users. The results show that vectorized TOPSIS reduced execution time by approximately 50–55% (26.3 ms vs. 57.9 ms) and achieved the highest throughput of 480 requests per second. In contrast, Z-TOPSIS produced higher latency (68.4 ms) due to additional reliability computations. Ranking consistency remained high between classical and vectorized TOPSIS (Kendall’s tau ≥ 0.98), while Z-TOPSIS showed minor deviations (tau = 0.91). These findings provide practical guidance for selecting TOPSIS variants in scalable web-based MCDM applications.
ECCFD-GNN: A Novel Risk-Sensitive Graph Neural Network Model for Fraudulent Transaction Detection Shilpa Srivastava; Varuna Gupta; Alok Singh Chauhan; Sakshi Kumar; Sonia Rani
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): 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.v8i3.3393

Abstract

The study presents the integration of machine learning techniques for detecting the credit card fraud. Its integration maintains a behavioral profile of cardholders and other parameters like location, frequency, amount etc. resulting in the timely detection of any anomaly from the normal behavior. A novel approach ECCFD-GNN (Enhanced Credit Card Fraud Detection based on Graph Neural networks) is proposed enhancing the performance of fraud detection. The various behavioral indicators taken into consideration are number of months with late payments, the frequency of low payments and the length of the account, which are further combined to a newly introduced feature “risk score”. The purpose of risk score is to increase the model’s sensitivity to the transactions having complex fraud risks. The approach uses three Graph Neural Network architectures namely KNN graph GNN, Radius Graph GNN and Feature Correlation GNN. The experiment is performed with both the optimizers Adam and RAdam. With Adam optimizers the results show that KNN graph GNN provides better performance when compared on the basis of different evaluation parameters with accuracy 85%, precision 76% recall 70% and F1-score as 73%.  The results are improved when tested with RAdam optimizers leading to increased accuracy, precision, recall and F1 score.

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