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Alde Alanda
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International Journal of Advanced Science Computing and Engineering
Published by Society of Visual Informatics
ISSN : 27147533     EISSN : 27147533     DOI : https://doi.org/10.30630/ijasce
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
The journal scopes include (but not limited to) the followings: Computer Science : Artificial Intelligence, Data Mining, Database, Data Warehouse, Big Data, Machine Learning, Operating System, Algorithm Computer Engineering : Computer Architecture, Computer Network, Computer Security, Embedded system, Coud Computing, Internet of Thing, Robotics, Computer Hardware Information Technology : Information System, Internet & Mobile Computing, Geographical Information System Visualization : Virtual Reality, Augmented Reality, Multimedia, Computer Vision, Computer Graphics, Pattern & Speech Recognition, image processing Social Informatics: ICT interaction with society, ICT application in social science, ICT as a social research tool, ICT education
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6 Documents
 1 
Search results for , issue "vol. 8 no. 2 (2026)" : 6 Documents clear
The Structural Design and Optimization of an Industrial Lift Platform Using Composite Profiles Mayur S. Awale; Sanjaykumar Gawade
International Journal of Advanced Science Computing and Engineering Vol. 8 No. 2 (2026)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.8.2.283

Abstract

The structural design and optimization of an industrial lift platform through the application of composite profiles, specifically sandwich structures, to enhance performance characteristics compared to conventional metallic (steel)designs. The primary goal is to significantly reduce the platform's overall weight without compromising essential safety factors and functional requirements, such as load-bearing capacity and stiffness. The methodology under development involves detailed 3D models of the lift platform using CAD software and performing extensive structural analyses, typically the Finite Element Method (FEM) in software like ANSYS Workbench. Various composite material properties and cross? sectional geometries (e.g., triangular, rectangular, circular cores) are explored and compared under simulated operational conditions, including static and dynamic loads. Optimization techniques, such as topology and size optimization, are applied to determine the most efficient design parameters for the composite structure. The results consistently demonstrate that platforms can achieve substantial weight savings, often in the range of 19% to 50% compared to steel structures, while meeting or exceeding strength and deflection limits. The findings confirm the technical feasibility and practical implications of using composite profiles for lightweight, high-performance industrial lift platforms, leading to potential benefits in cost savings, energy efficiency, and ease of transport. The study technically validates the feasibility of utilizing optimized composite sandwich profiles in the design of industrial lift platforms. Implementing these materials reduces weight, translating into practical benefits such as lower operational costs, improved energy efficiency, and longer service life. The outcomes of this research provide a robust design framework for future lightweight industrial-machinery components.
Enhancing Material Quality Control in Construction: A Data-Driven Approach Using PDCA and DMAIC Dony Susandi; Lia Milana; Dinda Ulzanah Nurislamiyati
International Journal of Advanced Science Computing and Engineering Vol. 8 No. 2 (2026)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.8.2.300

Abstract

Quality control is a critical aspect of construction projects, ensuring that materials and implementation align with established standards. This study focuses on the quality control of ready-mix concrete used in the capping beam lining works of the Cipelang Irrigation Modernization Project. Initial observations indicated that some concrete materials did not meet the required compressive strength, potentially compromising structural integrity and durability. To address these concerns, the study applied PDCA and DMAIC methodologies to systematically monitor and enhance material quality. Compressive strength tests were performed in accordance with ASTM C39 and BS EN 12390-3 standards, covering specimen preparation, curing, and testing procedures. Additionally, Statistical Process Control (SPC) techniques, including control charts, were utilized to identify variations in mix composition and implement necessary corrective actions. A deeper analysis using Failure Mode and Effect Analysis (FMEA) revealed that quality issues stemmed from three primary factors: human resources, materials, and methodologies. Through corrective measures, the study achieved significant improvements in concrete quality. The hammer test results showed an increase of 42 kg/cm² (~26.25%), while the compressive strength test results improved by 68.17 kg/cm² (~40%). These enhancements reduced material defects, minimized rework, and ensured adherence to project quality requirements. By adopting a structured, systematic approach, this study presents a scalable quality control framework applicable to similar construction projects. The findings contribute to safer, high-quality, and sustainable infrastructure by optimizing material performance and reducing quality inconsistencies.
Effect of Variation in Casting Parameters and Gating System Design on the Quality of Aluminum Tensile Test Specimens Using Altair Inspire Cast Simulation Joni Arif; Mujiyono; Tiwan; Ardian Maulana
International Journal of Advanced Science Computing and Engineering Vol. 8 No. 2 (2026)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.8.2.301

Abstract

A356 aluminum alloy has been widely applied in the automotive, aerospace, and marine industries thanks to a combination of superior mechanical properties, namely lightweight, high corrosion resistance, and adequate mechanical strength. However, aluminum casting often produces defects such as porosity, misruns, and shrinkage, which significantly degrade the final product's quality and integrity. This study examines how variations in casting parameters and duct system design affect the quality of A356 aluminum tensile test specimens according to the JIS H 5202 standard, using Altair Inspire Cast simulation software. The simulation varied casting temperature, molten-metal flow rate, and duct-system geometry to observe mold filling, solidification, and defect formation. The simulation results show that optimizing these three parameters significantly reduces internal defects and produces a more even solidification distribution. The optimal configuration was a casting temperature of 720°C, a flow rate of 0.45 m/s, and a tapered ducted bottom gating system design, which Inspire Cast analysis predicted would yield the lowest porosity index (<0.5%). The tensile test specimens are printed using optimal parameters and physically tested according to the JIS H 5202 standard. The mechanical test results showed an average tensile strength of 215 MPa, yield strength of 165 MPa, and elongation of 5.8%. These values meet or exceed the minimum limits required by JIS H 5202: UTS = 195 MPa, yield strength = 130 MPa, and elongation = 4%. Metallographic observations using optical microscopy also confirmed the lack of porosity in the optimal specimen compared with the control specimen, with the specimen density reaching 2.67 g/cm³, close to the theoretical value for alloy A356.
PrasathSrinivasan Sushmitha: Algorithm for String Encryption and Decryption J.S.Prasath J.Srinivasan; Sushmitha G.S
International Journal of Advanced Science Computing and Engineering Vol. 8 No. 2 (2026)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.8.2.302

Abstract

Security is essential for communication of messages, data, and private information. Security threats and vulnerabilities increase rapidly due to the wide usage of wireless media for data transmission. Intruders can access and alter sensitive data and private information transmitted across the Internet. Security algorithms are important to ensure secure communication without unauthorized access or modification. This work proposes a novel cryptography scheme for encrypting text messages. This proposed algorithm encrypts the input string into ciphertext through a series of mathematical operations. The inverse operation of encryption is performed at the receiver to obtain the original string from the ciphertext. This proposed cryptography can be utilized for preventing theft and fraud in online money transactions, securing email messages, file encryption, securing web information, ensuring data confidentiality across the internet, assuring data security in remote access, securing the patients' information transmitted across wireless networks, enabling secure communication of the country’s border information, preventing credit card fraud, securing the industrial continuous varying process parameters, encryption of WhatsApp messages, and securing the individual’s private information.
Feature Selection for Machine Learning-Based Multiclass Cyberattack Classification Asmau Usman; Ibrahim Anka Salihu; Abdulrahman Aminu Ghali; Bilkisu Larai Muhammad Bello; Aminu Aminu Mu'azu; Amina Sambo Magaji
International Journal of Advanced Science Computing and Engineering Vol. 8 No. 2 (2026)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.8.2.303

Abstract

Cyberattack classification supports timely threat identification and network security monitoring. However, model effectiveness depends not only on the selected algorithm but also on whether the available features contain sufficient information to distinguish attack classes. This study evaluates the effect of feature selection on multiclass cyberattack classification and assesses the discriminative capability of a synthetic cybersecurity dataset. The dataset contains 40,000 records from three nearly balanced classes: distributed denial-of-service, malware, and intrusion. Preprocessing included removing high-cardinality and potentially post-detection attributes, handling missing security indicators, one-hot encoding categorical variables, and applying a stratified 80:20 training/testing split. We evaluated Random Forest, Logistic Regression, and XGBoost using all usable features and the ten highest-ranked features identified through Random Forest importance. We measured performance using accuracy, macro precision, macro recall, and macro F1-score, and included a stratified dummy classifier as a baseline. Logistic Regression using all features achieved the highest accuracy of 33.95%, while XGBoost using the Top-10 subset achieved the highest macro F1-score of 33.69%. The dummy baseline obtained 33.90% accuracy, showing that none of the models produced a practically meaningful improvement over chance-level classification. Feature reduction slightly improved Random Forest and XGBoost but reduced Logistic Regression performance. These findings indicate that the dataset features have limited discriminative capability for separating the three attack categories. Further research should validate the analysis using real-world traffic, alternative feature-selection methods, and cross-dataset experiments before interpreting model complexity and reported accuracy.
A Comprehensive Survey of Transient Execution Vulnerabilities: from Meltdown and Specter to Modern Variants Faisal Mushtaq
International Journal of Advanced Science Computing and Engineering Vol. 8 No. 2 (2026)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.8.2.304

Abstract

Meltdown and Specter exploit hardware vulnerabilities in modern processors. These vulnerabilities allow programs to steal data currently being processed on the computer. Although programs are typically not permitted to read data from other programs, a malicious program can exploit Meltdown and Specter to access secrets stored in the memory of other running programs. This analysis covers distinct attack vectors across multiple processor architectures and provides a systematic taxonomy based on exploitation mechanisms, targeted microarchitectural components, and threat models. Although implementation-specific vulnerabilities like Meltdown have been effectively mitigated, architectural issues exemplified by Specter continue to challenge the security community. This paper also includes explainable machine learning methods for detecting Meltdown and Specter. The dataset utilized was synthetic and does not represent actual attacks. Genuine attackers may evade detection, as we did not test against real malicious activity. The detector requires further assessment on authentic systems to evaluate its operational overhead and reliability. Future research should concentrate on: (1) designing processors with speculation techniques that leave no detectable traces; (2) developing tools for the preemptive identification of vulnerabilities; (3) establishing formal methodologies to validate security guarantees; and (4) designing architectures, such as RISC-V, with integrated security features.

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