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International Journal of Engineering Continuity
Published by Sultan Publisher
ISSN : -     EISSN : 29632390     DOI : https://doi.org/10.58291/ijec
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering
The International Journal of Engineering Continuity is peer-reviewed, open access, and published twice a year online with coverage covering engineering and technology. It aims to promote novelty and contribution followed by the theory and practice of technology and engineering. The expansion of these concerns includes solutions to specific challenges of developing countries and addresses science and technology problems from a multidisciplinary perspective. Published papers will continue to have a high standard of excellence. This is ensured by having every papers examined through strict procedures by members of the international editorial board. The aim is to establish that the submitted paper meets the requirements, especially in the context of proven application-based research work.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 64 Documents
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A Hybrid Neural Network and Sugeno-Type Fuzzy Approach for Object Classification to Assist Navigation of Visually Impaired Individuals Using Ultrasonic Sensor Arrays Solihin, Ridwan; Hasanah, Rahmawati; Setiadi, Budi; Supriyadi, Tata; Sudrajat, Sudrajat; Tri Hartono, R Wahyu
International Journal of Engineering Continuity Vol. 4 No. 2 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i2.416

Abstract

This study proposes a hybrid neural network that integrates a multilayer perceptron (MLP) with optimised Sugeno-type fuzzy reasoning for object classification. The system employs a vertically mounted array of ultrasonic sensors arranged 10 cm apart at heights ranging from 80 cm to 180 cm. Each sensor measures the distance of passing objects, producing eleven readings that capture vertical distance patterns. These readings are processed by an MLP with a single hidden layer of 22 neurones to identify characteristic spatial signatures. A refined similarity-based classification is then performed using an optimised Sugeno-type fuzzy inference system configured with five linguistic variables: Very Low (VL), Low (L), Medium (M), High (H), and Very High (VH). Training and testing were conducted using datasets collected at SLBN-A Citeureup, Cimahi, comprising two object categories: human (visually impaired individuals) and nonhuman (inanimate objects). The model was trained for 100 epochs with a learning rate of 0.001. Experimental results show accuracy exceeding 90%, with the hybrid model outperforming the conventional MLP by 1.83%. This improvement reduces false positives and prevents erroneous obstacle warnings. The integration of fuzzy reasoning also enhances the system's robustness to uncertainty and stabilises decision-making when class boundaries overlap.  
Comparative Study of PSO, GA, and ACO for Optimizing Dielectric Performance in Fly Ash Filled Silicone Rubber Thahara, Andi Amar; Christiono, Christiono; Fikri, Miftahul; Garniwa M. K., Iwa; Wirandi, Mohammad
International Journal of Engineering Continuity Vol. 4 No. 2 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i2.439

Abstract

This study investigates the optimization of coal fly ash composition as a filler in Silicone Rubber (SiR) insulator materials, aiming to enhance their dielectric characteristics. Compositional optimization was achieved by evaluating and comparing three advanced meta-heuristic algorithms Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO), using the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) as performance metrics. The utilized fly ash, containing dominant silica, alumina, and iron oxides, was directly incorporated into the SiR matrix. Results indicate that, compared to PSO, GA and ACO exhibited superior performance and consistency. Specifically, for Relative Permittivity, the optimal composition of 80% yielded the lowest errors with GA and ACO (RMSE = 0.0751; MAPE = 0.9044). For Hydrophobicity, these two algorithms showed superior accuracy in the RMSE metric (RMSE = 0.8883) at 15.39% loading. These findings underscore the scientific contribution of this study by establishing the superior reliability of GA and ACO for optimizing fly ash composition in SiR, thus providing a robust analytical methodology to advance the use of industrial waste for high-performance dielectric materials.
Analysis of Pressure Drop in Clean Water Piping Installation Using Revit Software Aslamia, Soibatul; Haryadi, Deni; Komarudin, Komarudin
International Journal of Engineering Continuity Vol. 4 No. 2 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i2.405

Abstract

Clean water piping systems in industrial facilities must be designed to ensure adequate residual pressure at all outlets while minimizing energy losses. One critical factor influencing system performance is pressure drop, which results from both friction in straight pipes and localized losses in fittings, valves, and other components. This study analyzes the pressure drop in the clean water distribution network of PT XYZ, Kendal Industrial Estate, using two approaches: manual calculation based on the Darcy–Weisbach equation with total loss coefficients, and simulation using Autodesk Revit’s Pressure Loss Report tool. The manual calculation yielded a total pressure drop of 2.30 bar (≈ 23.0 mH₂O) along the critical path, with approximately 72% of the loss originating from fittings and 28% from pipe wall friction. The Revit simulation produced a total pressure drop of 2.10 bar (≈ 21.4 mH₂O) for the same route, resulting in a deviation of 8.7%, which is within the accepted tolerance of ±10% for BIM-based hydraulic validation. The results demonstrate that Revit can reliably model hydraulic performance when accurate material, dimension, and fixture data are provided. The findings emphasize that optimization strategies should focus on reducing localized losses by minimizing fittings, improving pipe routing, and increasing branch diameters in high-velocity sections. These measures can enhance residual pressure, improve system efficiency, and reduce pump energy requirements. The study validates the use of Autodesk Revit as an effective tool for preliminary hydraulic analysis in compliance with SNI 03-6481-2000, while confirming the importance of manual validation during the design process.
Integration of BERT and LSTM for Predicting Cybersecurity Service Trends Based on LinkedIn Data Firdaus, Mohamad; Azzery, Yasep; Prasetio, Dimaz Arno
International Journal of Engineering Continuity Vol. 4 No. 2 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i2.424

Abstract

The analysis and prediction of evolving cybersecurity service demands are constrained by existing methodologies, which are either semantically shallow (keyword-based TF-IDF) or contextually limited (standalone LSTM time-series models that overlook textual meaning). To bridge this scientific gap, this study develops and validates an integrated artificial intelligence framework combining Bidirectional Encoder Representations from Transformers (BERT) for deep semantic analysis and Long Short-Term Memory (LSTM) for sequential trend prediction. This pipeline is applied to a large-scale corpus of cybersecurity job descriptions collected from LinkedIn, serving as a proxy for real-world market intelligence. The methodology utilizes BERT embeddings (768-dimensional) for nuanced feature extraction, which are then combined with pseudo-temporal segmented data (proxy timeline) to enable sequential forecasting via the LSTM component. Experimental results confirm the model's robustness, the BERT component achieved 89% classification accuracy (87% precision, 88% recall) in service categorization, significantly outperforming baseline methods such as TF-IDF (which typically achieve below 75% accuracy). The LSTM component demonstrated strong predictive capability for trend forecasting, achieving a Root Mean Squared Error (RMSE) of 0.12. These findings validate the technical viability of the unified BERT-LSTM architecture for capturing both contextual and sequential patterns in professional data. The output provides organizations with objective, data-driven insights for strategic planning, thereby enhancing organizational resilience and market competitiveness in dynamic environments, particularly relevant for the Indonesian cybersecurity market.

Page 7 of 7 | Total Record : 64

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