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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 366 Documents
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Behavior of Lightweight Aggregate Concrete with FRP Confinement: Experimental Insights for Structural Applications Butje Alfonsius Louk Fanggi; Yuyun Tajunnisa; Hazen Masrafat; Jusuf Wilson Meynerd Rafael; Alva Yuventus Lukas; Niakku Immanuel Maggang; Joko Suparmanto; Melati Tabita Kirana Thei
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.v8i3.1650

Abstract

Lightweight aggregate concrete reduces structural dead load but generally exhibits lower compressive strength and ductility than normal-weight concrete. This study experimentally evaluates the effectiveness of carbon-fibre-reinforced polymer (CFRP) confinement for low-density lightweight aggregate concrete, an area in which data for square sections remain limited. Twelve 300-mm-high specimens with a density of approximately 1550 kg/m³ were tested under monotonic concentric compression. The investigated parameters were concrete compressive strength (15 and 28 MPa), cross-sectional shape (square and circular), and number of CFRP layers (one and two). Failure occurred through localized, extensive, or hoop rupture of the CFRP. The confined specimens exhibited approximately bilinear stress–strain responses and substantial improvements in strength and deformation capacity. For square specimens with 15 MPa concrete, two CFRP layers increased the average strength ratio to 2.27 and the strain ratio to 23.39. Circular specimens developed greater confinement efficiency, reaching an average strength ratio of 3.76 with two layers. Lower-strength concrete showed larger relative ductility gains than higher-strength concrete. These findings demonstrate that CFRP confinement can substantially reduce the brittle response of low-density lightweight concrete and support its use in lightweight, resilient, and earthquake-resistant structural applications.
Optimizing Acoustic Fingerprinting for Synchronized Audio Binary Matching Andi Bahtiar Semma; Kusrini Kusrini; Arief Setyanto; Bruno da Silva; An Braeken
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.v8i3.2744

Abstract

As interconnected devices proliferate, secure and efficient pairing methods are critical. Environmental acoustic signals offer a promising solution, but their effectiveness depends on robust audio features that perform well across varying conditions. This study investigates optimal audio features for fingerprinting, focusing on synchronized audio in time-frequency domains. Six diverse datasets were collected across controlled environments to simulate real-world scenarios. Thirteen audio features were extracted and analyzed for robustness across distances, devices, scenes, and sample lengths. Cosine similarity assessed consistency, while the Youden index determined thresholds. The Mel Spectrogram, particularly with 5-second samples, achieved an AUC of 0.8758 and a J-statistic of 0.7278. Augmenting it with Tonnetz and spectral bandwidth yielded the highest performance (AUC: 0.9346, J-statistic: 0.7955, Accuracy: 0.8320, recall: 0.9648), demonstrating the potential of combining robust base features with complementary acoustic characteristics for reliable device pairing.
Freeze-Dried Phycocyanin Microcapsules: Effect of Maltodextrin-Soy Protein Ratios on Encapsulation Efficiency and Particle Properties Siti Aisiyah; Ira Juliani Anwar; Dian Marlina; Ana Indrayati; Desi Purwaningsih
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.v8i3.2759

Abstract

Natural pigments such as phycocyanin are highly sensitive to environmental conditions and therefore require stabilization to broaden their applications in functional products. This study aimed to evaluate the effect of varying ratios of maltodextrin and soy protein isolate (SPI) on the physical properties and encapsulation performance of freeze-dried phycocyanin microcapsules. Phycocyanin was extracted using phosphate buffer and encapsulated in three formulations with different maltodextrin–SPI proportions. The obtained microcapsules were characterized using UV–Vis spectrophotometry and particle size analysis (PSA) to determine encapsulation efficiency (EE), yield, moisture content, purity index, and particle size distribution. All experiments were performed in triplicate (n = 3), and data were analyzed using one-way ANOVA (p < 0.05). The formulation with a higher proportion of maltodextrin exhibited the best performance, achieving high EE (≈96%), low moisture content, and uniform particle size. These findings highlight that optimizing biopolymer combinations can enhance the quality and stability of phycocyanin microcapsules, demonstrating potential applications in functional food and pharmaceutical formulations.
Separable Convolutional Hierarchical Decomposition for Lightweight Residential Load Forecasting in Smart Grids Satriawan Rasyid Purnama; Henri Tantyoko; Adi Wibowo; Yesaya Rudolf Susanto Widyanto
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.v8i3.2869

Abstract

Residential load forecasting is essential for maintaining grid stability and energy management in smart grids. However, achieving accurate real-time forecasting under resource constraints remains challenging because Transformer and LSTM models can be computationally demanding, while lightweight linear models such as DLinear have limited modeling flexibility. This study investigates whether a hierarchical separable convolutional framework can provide accurate and efficient residential load forecasting. To address this, SeparableCLF, a lightweight hierarchical decomposition model using depthwise separable convolution, is proposed and evaluated on hourly OpenEI residential load data from 20 U.S. states (2012) at forecast horizons of 6, 12, 24, 48, and 96 h. Relative to DLinear, SeparableCLF reduced MAPE by 0.93, 1.54, and 0.79 percentage points at 24, 48, and 96 h, respectively while requiring substantially fewer parameters than Transformer and LSTM models.SeparableCLF achieved the lowest MAPE at 12, 24, and 48 h. DLinear achieved the lowest errors at 6 h, whereas LSTM achieved the lowest MAPE at 96 h; at 96 h, SeparableCLF retained the lowest MAE, MSE, and RMSE among the compared models, indicating suitability for real-time forecasting on smart meters and edge-based smart grid devices.
Prediction of Ground Vibration due to Blasting Activities of Coal Open Pit Mine Near Village Residents Using Multivariate Logarithmic Regression Alloysius Vendhi Prasmoro; Nurhadi Siswanto; Budi Santosa; Giri Waluyo Nugraha; Mohd Shukor Salleh
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.v8i3.3616

Abstract

Blasting activities are among the most important in mining and can have a negative impact on the environment and surrounding communities, causing disruption and even damaging nearby buildings and infrastructure. If the community protests and demonstrates, mining operations may be shut down, which would be very detrimental to the company. Studies are needed on effective planning to reduce the negative impacts. Ground vibrations measured by Peak Particle Velocity (PPV) are subject to thresholds set by each region's standards; in this research area, the maximum threshold is 3 mm/s to avoid damage to nearby buildings or settlements. The important variables are the explosive charge per delay and the distance, along with other blasting geometry variables such as spacing, burden, stemming, powder factor, and number of blast holes. Several previous researchers have used methods to predict PPV, with detonation parameters as the independent variables. This research uses general empirical methods and multivariate logarithmic regression (MLR). Prediction using MLR is better than general empirical methods; with R2 = 0.925, RMSE = 0.247, MAE = 0.548, MAPE = 0.218, and VAF = 96.66%, indicating near-perfect prediction. The MLR model produces a reference maximum explosive charge per delay of 59.09 kg.
Design and Development of SIPADMA: A Software Engineering Approach for Water Consumption Anomaly Detection in Public Water Utilities Laras Wulansari; Bambang Agus herlambang
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.v8i3.4219

Abstract

Water utilities continuously face challenges in identifying abnormal customer water consumption caused by pipe leakage, illegal connections, meter reading errors, and unusual consumption behavior. Conventional manual inspection requires considerable time and often delays corrective actions. This study presents the design and development of SIPADMA (Sistem Deteksi Anomali Pemakaian Air), a web-based information system developed using Software Engineering principles to automate anomaly detection and support operational decision making. The system was developed following the Waterfall Software Development Life Cycle (SDLC), beginning with requirement analysis, UML-based system modeling, implementation using Flask, and functional testing. SIPADMA integrates statistical anomaly detection using Z-Score combined with rule-based scoring and provides interactive dashboards, anomaly filtering, customer history visualization, and automatic report generation. Evaluation using 3,996 billing records representing 666 customers demonstrated that the system successfully identified 163 anomalous accounts requiring further inspection. The developed system improves operational efficiency by automating data analysis and prioritizing field verification, demonstrating the effectiveness of software engineering practices in developing intelligent decision-support systems for public utilities.

Page 37 of 37 | Total Record : 366

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