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Mahardika Darmawan Kusuma Wardana
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PELS (Procedia of Engineering and Life Science)
Published by Universitas Muhammadiyah Sidoarjo
ISSN : -     EISSN : 28072243     DOI : https://doi.org/10.21070/pels
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
PELS (Procedia of Engineering and Life Science) is an international journal published by Faculty of Science and Technology Universitas Muhammadiyah Sidoarjo. The research article submitted to this online journal will be double blind peer-reviewed (Both reviewer and author remain anonymous to each other). The accepted research articles will be available online following the journal peer-reviewing process. Language used in this journal is Bahasa (Indonesia) or English. Aims and Scope of this journal is science and technology.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 678 Documents
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The Use of Attribute-Based Encryption for Secure Data Access: Penggunaan Enkripsi Berbasis Atribut untuk Akses Data yang Aman Malath Sabri Kareem
Procedia of Engineering and Life Science Vol. 8 No. 2 (2025): Proceedings of the 8th Seminar Nasional Sains 2025
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v8i2.2963

Abstract

General Background: Secure data sharing in cloud computing and Internet of Things environments requires advanced cryptographic mechanisms to ensure privacy and controlled access. Specific Background: Attribute-based encryption provides fine-grained access control, while blockchain technology introduces decentralized trust and data integrity. Knowledge Gap: Existing approaches often lack an integrated framework that combines encryption flexibility with decentralized security for scalable data sharing systems. Aims: This study aims to develop a secure data sharing framework by integrating attribute-based encryption with blockchain technology. Results: The findings indicate that the proposed approach enables fine-grained access control, strengthens data confidentiality, and supports secure and verifiable transactions in distributed environments. The framework also demonstrates improved reliability in managing encrypted data across multiple systems. Novelty: This study presents a combined architecture that integrates attribute-based encryption with blockchain to address limitations in traditional centralized security models. Implications: The results suggest that the proposed framework can support secure data sharing in cloud and IoT applications, offering a scalable solution for modern distributed systems. Keywords: Attribute Based Encryption, Blockchain Security, Data Sharing, Cloud Computing, Access Control Key Findings Highlights Fine grained policies regulate encrypted information access Distributed ledger supports transparent verification processes Integrated model enables secure multi environment communication
Artificial Intelligence-Driven Control and Optimization in 6G Communication Networks: A Comprehensive Review Mirza, Ali Ahmed; Mahmood, Taha Abdulwahid; Dhulkefl, Elaf Jirjees
Procedia of Engineering and Life Science Vol. 8 No. 2 (2025): Proceedings of the 8th Seminar Nasional Sains 2025
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v8i2.3000

Abstract

General Background: The transition toward sixth-generation (6G) communication networks represents a shift from model-driven systems to intelligence-driven architectures centered on artificial intelligence (AI). Specific Background: AI techniques, including machine learning, deep learning, and reinforcement learning, are increasingly applied to network control and resource optimization tasks within highly dynamic and heterogeneous environments. Knowledge Gap: Existing research remains fragmented, with most studies addressing isolated network functions and lacking system-level integration, scalability considerations, and deployment feasibility. Aims: This study provides a system-level critical review of AI-driven control and optimization in 6G networks, evaluating capabilities, limitations, and architectural implications. Results: The analysis shows that while AI approaches improve adaptability and performance, they face challenges related to computational complexity, scalability, data constraints, interoperability, and limited explainability, alongside a clear gap between algorithmic advances and real-world implementation. Novelty: The study offers a structured synthesis, comparative evaluation of AI paradigms, and highlights the necessity of integrated and architecture-aware AI frameworks. Implications: The findings suggest that future 6G systems require hybrid AI models and unified frameworks to support scalable, reliable, and autonomous network operations, bridging theoretical innovation with deployment-oriented design. Highlights:• Identifies fragmentation of AI solutions across isolated network functions• Reveals trade-offs among learning paradigms in system-level deployment• Emphasizes need for integrated frameworks for scalable intelligent networks Keywords: Artificial Intelligence, 6G Communication Networks, Network Control, Resource Optimization, AI Native Architecture
Blockchain-Based Solutions for Secure Data Transmission in Distributed Networks: Solusi Berbasis Blockchain untuk Transmisi Data yang Aman dalam Jaringan Terdistribusi Al-Nuaimi, Yasir Ali Khalid
Procedia of Engineering and Life Science Vol. 8 No. 2 (2025): Proceedings of the 8th Seminar Nasional Sains 2025
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v8i2.3001

Abstract

This study addresses security challenges in distributed networks, particularly in IoT and healthcare systems, where data breaches, latency, and scalability issues remain critical concerns. General Background: Distributed environments increasingly face cyber threats and limitations of centralized security models. Specific Background: Blockchain technology offers decentralized validation, immutability, and transparency but suffers from performance and scalability constraints. Knowledge Gap: Existing solutions lack integrated frameworks combining efficient consensus, encryption, real-time analytics, and empirical validation in large-scale and real-world environments. Aims: This study proposes a blockchain-based security framework integrating Proof-of-Authority consensus, AES-256 encryption, smart contracts, and AI-driven threat detection. Results: Experimental evaluation through simulations and a hospital deployment with 250 IoT devices shows data integrity up to 98%, latency reduced to 23 ms, and attack detection time of 35 ms, outperforming centralized systems despite moderate energy increase. Novelty: The framework uniquely combines PoA, AI analytics, and smart contract automation within a scalable architecture validated in real-world scenarios. Implications: The findings support deployment in critical sectors such as healthcare and smart energy, providing a reliable and transparent approach to secure data transmission in distributed networks. Keywords: Blockchain Security, Distributed Networks, IoT Data Protection, Proof-of-Authority, Threat Detection Key Findings Highlights High integrity maintained during cyberattack scenarios across large-scale networks Rapid detection and mitigation achieved in real-time operational settings Practical deployment validated through hospital-based IoT environment
A study of the properties of an epoxy cotton compound after adding silica atoms in different proportions Munadhil Kadhim Alomairy
Procedia of Engineering and Life Science Vol. 8 No. 2 (2025): Proceedings of the 8th Seminar Nasional Sains 2025
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v8i2.3019

Abstract

General Background: Natural fiber-reinforced polymer composites have attracted significant attention as lightweight, corrosion-resistant, and environmentally friendly materials for structural and industrial applications. Specific Background: Epoxy resin reinforced with woven cotton fabric offers a sustainable composite system, while nanosilica is widely used to modify matrix properties and improve mechanical performance. Knowledge Gap: However, the optimal nanosilica content for balancing tensile strength, compressive behavior, and toughness in cotton/epoxy composites remains unclear. Aims: This study investigated the mechanical properties of woven cotton fabric-reinforced epoxy composites containing 0.5, 1, and 5 wt.% nanosilica. Results: Composite laminates were fabricated by hand lay-up using six cotton layers and tested according to ASTM standards. The tensile results showed that 0.5 wt.% nanosilica provided the highest tensile strength, with a maximum increase of 21.26%. Compressive testing indicated that 1 wt.% nanosilica exhibited superior load-bearing capacity, while toughness analysis showed the highest value for 0.5 wt.% nanosilica at 28,753.17 J/m³. In contrast, 5 wt.% nanosilica resulted in lower performance, likely due to particle agglomeration and non-uniform dispersion. Novelty: This study demonstrates that the optimal nanosilica loading depends on the specific mechanical property evaluated rather than a single universal filler concentration. Implications: The findings support the design of cotton/epoxy nanocomposites for sustainable engineering applications requiring tailored tensile, compressive, and energy absorption properties. Highlights: • 0.5 wt.% nanosilica produced the highest tensile strength and toughness in the composite laminates.• 1 wt.% nanosilica provided the greatest compressive load-bearing capacity.• 5 wt.% nanosilica reduced performance due to agglomeration and non-uniform particle distribution. Keywords: Cotton Epoxy Composite, Nanosilica, Tensile Strength, Compressive Strength, Toughness
The Effect of Trash Fish Feed on the Quality and Organoleptic Characteristics of Mud Crab (Scylla serrata) Meat After 15 Days of Fattening: Pengaruh Pakan Rucah Terhadap Mutu Dan Organoleptik Daging Kepiting Bakau (Scylla Serrata) Pasca 15 Hari Penggemukan Angky Soedrijanto
Procedia of Engineering and Life Science Vol. 10 No. 1 (2026): Proceedings of the 9th Seminar Nasional Sains 2026
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v10i1.3103

Abstract

Rucah is a local term for fish caught by fishermen with low economic value. The use of several types of trash fish is effective and efficient in the mud crab fattening process to produce good quality and taste of crab meat. The research aims to identify and compare several types of trash fish used as natural feed for fattening mud crabs on the quality and taste of crab meat. The crabs tested came from fishermen's catches, weighing between 150 – 160 grams per head, which have been determined to be skinny crabs and must be fattened before being sold. The types of trash fish tested were: tilapia (Tilapia mossambica), keting (Ketengus ketengus), and mullet (Mugil cephalus). Test location in traditional pond plots in Sidoarjo district. The crabs tested were raised in closed plastic containers placed at the bottom of the pond, for 15 days with the same weight of feed being given. The results show that there is no significant difference in the quality and taste of crab meat. The taste of crab remains very prominent even though qualitatively the panelists think there are differences in natural taste, however preferences are subjective according to the tastes of each consumer. Trash fish has proven to be very effective in producing crab meat that is dense, white, soft, and has a very strong aroma.
Numerical Analysis of Groundwater Seepage and Exit Gradients in Hydraulic Structures Zaid N. Hashim; Alaa Mohsin Dawood; Hana M. Alkassar
Procedia of Engineering and Life Science Vol. 10 No. 1 (2026): Proceedings of the 9th Seminar Nasional Sains 2026
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v10i1.3118

Abstract

General Background: Seepage beneath hydraulic structure foundations poses significant risks of piping failure and structural instability. Specific Background: Installing vertical sheet piles alters flow paths and mitigates subsurface water pressure under variable upstream heads. Knowledge Gap: Precise comparative quantifications of seepage flow rates and exit gradients with and without sheet piles remain insufficiently detailed for varying head conditions. Aims: This study evaluates seepage discharge and exit gradients under hydraulic heads up to 10 meters using 2D finite element modeling. Results: Omitting sheet piles increases maximum seepage rates from 361.1x10-3 to 491.3x10-3 m3/sec/m and exit gradients from 0.41 to 1.21. Novelty: Quantitative modeling demonstrates that removing sheet piles elevates seepage discharge by 26.5% and exit gradient by 66.6%. Implications: Incorporating sheet piles is essential in foundation design to prevent subsurface erosion and ensure long-term structural safety. Keywords: Exit Gradient, Hydraulic Structures, SEEP/W, Seepage Analysis, Sheet Piles Key Findings Highlights Sheet pile installation decreases peak under-foundation seepage discharge by 26.5 percent at maximum head. Critical exit gradients rise by 66.6 percent when structural cutoff barriers are omitted. Steady-state finite element simulations confirm severe piping risks without foundation sheet piles.
Experimental and Theoretical Study for Evaluating Journal Bearing Performance under Internal Combustion Engine Operating Conditions Using Mixed Simulation Methodology Kabas Aziz Hassan; Mariya Thaer Abdulhadi; Shuhub Ahmed Shihab
Procedia of Engineering and Life Science Vol. 10 No. 1 (2026): Proceedings of the 9th Seminar Nasional Sains 2026
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v10i1.3106

Abstract

General Background Modern internal combustion engines demand high efficiency and operational durability under severe thermal and mechanical stresses. Specific Background Journal bearings frequently operate under severe loading, transitioning from hydrodynamic to mixed lubrication regimes. Knowledge Gap However, predicting frictional losses remains challenging due to complex interactions between elastic surface deformations, piezoviscosity, and shear thinning. Aims This study presents an integrated methodology combining experimental test-rig evaluations with multi-body hydroelastic simulations to evaluate journal bearing performance. Results Results indicate that while temperature reduces oil viscosity, high hydrodynamic pressures exceeding 200 MPa increase viscosity, whereas shear rates up to 2.2×10⁷ s⁻¹ cause noticeable shear thinning at high speeds. Additionally, initial edge contact stresses decrease rapidly during the running-in phase. Novelty A fully coupled thermo-elasto-hydrodynamic framework incorporating worn surface topographies is established. Implications These insights enable optimized bearing geometry designs and advanced lubricant formulations for high-efficiency engines. Key Findings Highlights Combined hydroelastic simulation and dynamic testing accurately predict journal bearing friction loss. Pressure-viscosity and shear thinning effects significantly influence high-load fluid film stability. Edge wear during running-in rapidly reduces peak contact stress without altering hydrodynamic torque. Keywords :  Journal Bearings, Mixed Lubrication, Elastohydrodynamics, Friction Torque, Internal Combustion Engines
Enhancing Neural Network Performance with Generative Adversarial Networks Mohammed Hamdan Yousif
Procedia of Engineering and Life Science Vol. 10 No. 1 (2026): Proceedings of the 9th Seminar Nasional Sains 2026
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v10i1.3128

Abstract

General Background Deep neural networks achieve remarkable success in computer vision but remain susceptible to overfitting and data scarcity. Specific Background Generative Adversarial Networks offer a framework to produce high-fidelity synthetic images through competitive zero-sum training between generator and discriminator sub-networks. Knowledge Gap However, the direct impact of generative augmentation on network generalization and adversarial robustness under noisy conditions requires rigorous empirical quantification. Aims This study evaluates how integrating generative synthetic samples into the training pipeline enhances neural network classification accuracy and resilience. Results Incorporating augmented images into CIFAR-10 training improved classification accuracy from 89.7% to 94.3%, while simultaneously increasing resilience against FGSM, PGD, and CW adversarial attacks. Novelty A unified evaluation architecture combining Wasserstein gradient penalty formulations with noisy data testing is established. Implications These findings demonstrate that generative synthetic data effectively mitigates data limitations and stabilizes deep learning deployment in unpredictable operational environments. Key Findings Highlights Synthetic data augmentation increases convolutional neural network accuracy on CIFAR-10 to 94.3%. Adversarial training enhances model resilience against FGSM, PGD, and CW attacks. Generative samples mitigate overfitting and restore classification accuracy under noisy data conditions. Keywords :  Generative Adversarial Networks, Neural Networks, Data Augmentation, Synthetic Data, Adversarial Robustness

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