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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 386 Documents
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Data-Driven Machine Learning Models for Steam Turbine Efficiency Prediction Prima Arifa Alfariza; Hasna Fauziyah; Nikita Tjandra; Muhammad Asrol
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): 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.v8i4.2378

Abstract

Increased electricity consumption in Indonesia requires coal-fired power plants to operate at high efficiency. This study proposes a data-driven approach to predict the efficiency of coal-fired steam turbines by utilizing 844 historical operational data points from the XYZ coal-fired power plant. Input variables include main steam pressure and temperature, main steam flow, final feedwater temperature, and condenser vacuum pressure. Four machine learning algorithms, namely Random Forest, Extra Trees, XGBoost, and Support Vector Regression, were evaluated using R², RMSE, and MAE. Based on the results of the study, it can be concluded that predictive models using Random Forest, Extra Trees, and XGBoost can predict steam turbine efficiency with high accuracy, especially after hyperparameter adjustments that increase the R² value and reduce the RMSE and MAE values in all models. The Extra Trees model proved to be the best model with an R² value of 0.8655 and an MAE of 0.5612, demonstrating its ability to capture the complex relationship between operational variables and turbine efficiency. This approach has practical implications for continuous improvement strategies in coal-fired power plant operations.
Prototype and Validation of an IoT-Based Voltage and Temperature Monitoring System for Remote ISP Point-of-Presence (POP) Infrastructure I Kadek Juni Arta; Ida Bagus Gede Citta Narendra; Ida Ayu Putu Febri Imawati
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): 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.v8i4.2602

Abstract

Monitoring electricity and temperature is crucial for maintaining PoP server performance. This research developed an Internet of Things (IoT)-based tool using a NodeMCU ESP32 to directly monitor these two factors. The ZMPT101B sensor measures electrical voltage, while the DHT22 sensor monitors the temperature of the room and server rack. Information obtained from the sensor is sent in real time via a Telegram Bot to the administrator. This tool is particularly useful for PoP located remotely, as it makes it easier for administrators to monitor server conditions and prevent damage. The use of the Arduino IDE is also essential in the development of this tool for uploading programs to the microcontroller. The results of this research provide a practical solution for maintaining server and ensuring internet network stability. The urgency of this research lies in the growing need for reliable and efficient remote monitoring systems to maintain stable internet network operations, especially at PoP locations far from data centers. Undetected power outages or temperature fluctuations can cause significant server damage and result in a decrease in service quality for users.
Enhanced Self-Esteem Classification: Leveraging Data Augmentation and Transformer-Based Sentence Embeddings Reza Ahmadiansah; Mukti Ali; Rasimin; Achmad Maimun; Kastolani; Imam Subqi; Embun Bening Di Moravia; Andi Bahtiar Semma
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): 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.v8i4.2741

Abstract

This study investigates automated self-esteem assessment from self-descriptive text using transformer-based sentence embeddings. Although prior research has explored text, behavioral, and multimodal signals, the combined effects of data augmentation, embedding choice, and classifier complexity in text-only self-esteem classification remain insufficiently understood.Accordingly, this study aims to systematically evaluate embedding–classifier combinations under both low-resource and augmented data conditions. Textual self-descriptions were collected from 298 undergraduate students at UIN Salatiga and labeled using the Indonesian version of the Rosenberg Self-Esteem Scale, yielding three self-esteem categories. To address data scarcity, a controlled translation-based augmentation pipeline with expert psychological validation was applied exclusively to the training set. Seven multilingual sentence embedding models were paired with eight classification algorithms, and performance was evaluated using macro-averaged metrics, along with training and inference time. Results reveal a two-regime pattern: (1) in limited-data settings, strong embeddings with simple classifiers perform best, (2) whereas in augmented settings, representation quality dominates and classifier choice has a marginal effect. The findings suggest that prioritizing high-quality embeddings and carefully validated data augmentation enables accurate, scalable, and cost-effective text-based self-esteem assessment for real-world psychological applications.
Loading-Dependent Physicochemical Characteristics of LiMn2O4 Composites with Plasma-Modified and Ammonia-Functionalized Rice Husk Carbon Harianingsih Harianingsih; Deni Fajar Fitriyana; Januar Parlaungan Siregar; Agung Budiwirawan; Ari Dwi Nur Indriawan; Suryo Wiroyudho Wibowo; Rizky Ilham Fadzillah; Nabila Khoirunisa
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): 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.v8i4.2915

Abstract

This study investigates LiMn2O4 composites incorporated with nitrogen-functionalized rice husk-derived carbon as a sustainable secondary phase for cathode material development. Rice husk carbon was prepared through carbonization, acid-assisted activation, plasma treatment, and ammonia functionalization, then mechanically blended with LiMn2O4 at 2, 3, and 4 wt.% to obtain LMO-NC2, LMO-NC3, and LMO-NC4, respectively. FTIR analysis showed absorption bands at approximately 3390, 1625, 1400, 1008, 832, 702, and 460 cm⁻¹, corresponding to O–H, C=C, C=N, Si–O, and Mn–O-related vibrations. The minimum transmittance decreased from LMO-NC2 to LMO-NC4, particularly at ~1400 cm⁻¹ from 17.13% to 16.01%, indicating stronger carbon/nitrogen-related surface features. SEM revealed layered LiMn2O4, fine carbon deposits, interparticle voids, and agglomeration. XRD showed characteristic spinel LiMn2O4 indexed to the (111), (311), (222), (400), (331), (511), and (440) planes. BET adsorption volume increased from 160 cc/g for LMO-NC2 to approximately 169 and 176 cc/g for LMO-NC3 and LMO-NC4 at P/P₀ = 0.31. These findings demonstrate the potential of rice husk-derived carbon for sustainable LiMn2O4 composite design, supporting responsible consumption and production under SDG 12.
Sustainable Cement Paste Incorporating Andesite Waste Powder: Mechanical Performance and Feasibility for Ornamental Elements Ni Komang Ayu Agustini; I Nengah Sinarta; Noor Azline Mohd. Nasir; Warid Wazien Ahmad Zailani
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): 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.v8i4.2940

Abstract

Utilising industrial waste as a partial solution for Portland cement has become more common as the need for environmentally friendly building materials grows. The potential of using andesite waste powder (AWP), a byproduct of the stone-processing industry, as a cementitious material for ornamental cement-based products is examined in this study. The flowability, dry unit weight, and compression strength tests were done on cement paste samples with 0%, 50%, and 75% AWP replacement levels at water-to-binder ratios of 0.30, 0.35, and 0.40. As the AWP content increased, the workability and compressive strength decreased. This is because AWP has a lower hydraulic response and higher water demand. After 28 days, the mixture with 50% AWP and a water-to-binder ratio of 0.30 had the best performance, with a compression strength of 22.56 MPa and a dry unit weight of 1738.28 kg/m³. The economic analysis demonstrated that the strength-to-cost ratio of AWP-based paste was more favourable than that of conventional ornamental materials. This research demonstrates the potential of AWP as a sustainable and cost-effective alternative material for ornamental applications.
Dynamic Chaotic–Adversarial Framework for High-Capacity and Imperceptible Image Steganography Wellia Shinta Sari; Christy Atika Sari; Safira Hasna Setiyani; Agus Triyono; Rabei Raad Ali
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): 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.v8i4.3060

Abstract

The rapid growth of digital communication has intensified concerns regarding data confidentiality as sensitive information transmitted through multimedia images is increasingly vulnerable to interception and unauthorized analysis. Conventional image steganography methods often struggle to simultaneously achieve high embedding capacity, strong imperceptibility, and resistance to modern steganalysis. To address this challenge, this study proposes a steganographic framework that integrates dynamic logistic chaotic encryption with an adversarial feature-level embedding network. The chaotic sequence is generated using a time-varying logistic map within a highly unstable region, where the control parameter is adaptively derived from a hash-modulated process to produce unpredictable keystreams and strengthen payload security. The encrypted secret image is then embedded through a GAN-based generator guided by a discriminator to preserve natural image characteristics, while a dedicated extractor ensures accurate recovery. Experimental results on multiple standard test images with resolutions of 256 × 256 and 512 × 512 demonstrate high visual fidelity, achieving PSNR values above 58 dB and SSIM values above 0.995, supported by nearly identical histogram distributions between cover and stego images. These findings indicate that the proposed framework provides a promising solution for secure multimedia communication by enabling visually imperceptible and reliably recoverable hidden transmission in digital images.
Lightweight Dual-Layer Chaotic Image Encryption Using Arnold Cat Map and Henon Zigzag Diffusion Chaerul Umam; Abdussalam Abdussalam; Arif Nursetyo; Bambang Sugiarto; Husain Md Mehedul Islam
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): 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.v8i4.3085

Abstract

Digital image transmission over open networks raises significant security concerns due to the high correlation and predictable statistical properties of image data. Existing chaotic encryption schemes based on Arnold Cat Map (ACM) and Henon mapping often suffer from high computational cost, parameter sensitivity, or reliance on complex multi-stage designs. To address these limitations, this study proposes a lightweight dual-layer chaotic image encryption framework that integrates ACM-based pixel permutation with Henon Zigzag diffusion. The first layer applies ACM to disrupt spatial correlations, while the second layer embeds a Henon-based chaotic sequence into a zigzag traversal to enhance both confusion and diffusion. Experimental results demonstrate that the proposed method achieves strong security performance, with an average PSNR of 8.40 dB for cipher images, UACI of 33.67%, NPCR of 99.71%, and near-zero correlation coefficients across RGB channels, while maintaining a low average execution time of 1.80 s. These results indicate that the method produces highly randomized cipher images with strong resistance to statistical and differential attacks. Furthermore, the reduced computational complexity highlights its suitability as a lightweight and efficient solution for secure multimedia transmission in practical digital communication systems.
Microwave-Assisted Self-Healing of AC-WC Modified with Iron Powder: Mechanical Performance and Healing Rate Amalia Firdaus Mawardi; Machsus Machsus; Dadang Supriyatno; Achmad Faiz Hadi Prajitno; Muhammad Fikri Nadhif; Hazen Masrafat
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): 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.v8i4.3297

Abstract

Conventional asphalt mixtures have limited microwave absorption, reducing the effectiveness of microwave-assisted self-healing. This study evaluates the effect of iron powder (0–10% by mass of fine aggregate) as additional fine aggregate in AC-WC mixtures on mechanical performance and microwave-activated healing behavior. Cylindrical specimens (63 mm × 100 mm; three per mixture) were tested using Marshall Stability and Indirect Tensile Strength (ITS) at 25 °C. Healing efficiency was determined by the ratio of post-heating ITS to initial ITS. Results showed that 10% iron powder increased Marshall stability by 36% compared to the control and achieved the highest healing rate of 76% after the first microwave cycle. However, repeated heating reduced healing performance due to overheating and accelerated binder aging. Iron powder improves early-stage self-healing but requires controlled dosage and heating conditions for long-term durability.
Evaluating Disruption Risk to Foster Resilience in Humanitarian Supply Chain:  An Integrated SV-PSI and EDAS Model Agung Sutrisno; Christian Spreafico; Muhammad Dwisnanto Putro; Ade Yusupa; Amir Tjolleng; Kenji Lokaputra
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): 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.v8i4.3345

Abstract

The evaluation of the risks in humanitarian supply chain operations is crucial to prevent losses due to disasters. However, many quantitative studies in the literature are aimed at the profit-oriented supply chain, while those in the field are based on the subjective preferences of decision-makers, making risk prioritization less accurate. To fill this gap, this study presents a theoretically improved approach for this purpose by integrating statistical variance, preference selection index (PSI), and EDAS methods into humanitarian supply chain FMEA to prioritize risks. To demonstrate the applicability of the model, numerical calculations and sensitivity tests were conducted using secondary data. The test results indicate that the dimension of risk detection capability is an important dimension for proactive risk prevention. Furthermore, communication in humanitarian disaster response supply chain operations is a very important enabler for the resilience of the humanitarian supply chain. In the future, inclusion of the influence of relationships between decision criteria and between risk factors is recommended as a direction for further research from this study.
LDWFOX Optimization for Hyperparameter Tuning of Inception CNN in UAV-Based Vegetation Density Mapping Ricardus Anggi Pramunendar; Ashraf Alomoush; Dwi Puji Prabowo; Rama Aria Megantara; Farrikh Alzami; Nurul Anisa Sri Winarsih; Dewi Pergiwati; Guruh Fajar Shidik
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): 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.v8i4.3433

Abstract

Vegetation density classification from UAV imagery is a practical necessity in fire-prone landscapes, since fuel load on the ground directly informs risk management decisions. Convolutional neural networks handle this classification reasonably well, but good performance requires careful hyperparameter tuning, and manual trial and error produces results that are unstable and hard to reproduce. This study proposes LDW-FOX, a modified FOX metaheuristic using a Linearly Decreasing Weight mechanism to automate hyperparameter tuning for an Inception-based CNN. The original FOX algorithm applies fixed movement weights throughout optimization, causing search to stagnate early. LDW-FOX gradually reduces exploration intensity across iterations, pushing search toward exploitation as it converges. Five hyperparameters, namely learning rate, dropout rate, hidden layer size, activation function, and optimizer, were tuned on a balanced 3,000 image UAV dataset spanning three vegetation density classes. Manual tuning peaked at 61.00 percent test accuracy but varied considerably across epoch settings. LDW-FOX reached a peak test accuracy of 82.48 percent and a mean of 58.47 percent, outperforming the original FOX, whose mean was 55.30 percent. LDW-FOX showed a more consistent training-test gap than other swarm-based methods, with LDW variants beating unmodified counterparts under equal budgets. High variance across configurations indicates broader generalization needs testing.

Page 38 of 39 | Total Record : 386

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