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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 12 Documents
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Search results for , issue "Vol. 4 No. 2 (2025): ijec" : 12 Documents clear
Integration of Javanese Sengkalan and Steganography for Key Exchange in End-to-End Encryption over HTTP Eko Heri Susanto; Joseph Dedy Irawan; Fikri Pradana Efendi
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.v4i2.396

Abstract

This research proposes an HTTP-based end-to-end encryption key exchange mechanism without TLS. The system uses Javanese Sengkalan to convert OTPs into private and public key pairs. The public key is embedded into images using steganography. Before being encrypted with ChaCha20, the data is compressed with the Brotli algorithm. To enhance randomness, a nonce is generated by converting the Gregorian date to the Javanese calendar, then hashed with SHA-256. Tests were conducted on four aspects: man-in-the-middle attacks, data size efficiency, randomness of the encryption results, and the entropy value of the key exchange. The results show that this approach is suitable for devices with limited resources. However, the entropy value is still low, so the system is not sufficiently secure against brute-force attacks. The contribution of this work lies in introducing a unique key exchange method that integrates Javanese Sengkalan with steganography.
Optimization Model of IoT and Machine Learning for Renewable Energy-Powered Aeroponic Systems Silviana Windasari; Abdurohman Abdurohman; Adi Affandi Ratib; Ade Frihadi; Khalid Montazi
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.v4i2.426

Abstract

This study proposes an optimization model integrating Internet of Things (IoT) and Machine Learning (ML) for renewable energy-powered aeroponic systems as a conceptual framework to enhance sustainable agriculture and address global food security challenges. The model is designed to mitigate land degradation, water scarcity, and the impacts of climate variability on crop productivity. It combines IoT-based real-time monitoring of key environmental variables temperature, humidity, pH, electrical conductivity, and light intensity with Long Short-Term Memory (LSTM) networks for time-series prediction of crop growth and resource requirements. Renewable energy sources, particularly solar photovoltaic systems with battery storage, ensure reliable and environmentally friendly power supply. The proposed approach emphasizes predictive optimization, where IoT data streams inform adaptive LSTM algorithms for precise irrigation and nutrient control. Model performance is evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²). Although the study remains conceptual and simulation-based, validation results demonstrate high predictive accuracy and efficiency. This research establishes a foundational framework for subsequent prototype development, experimental validation, and techno-economic evaluation toward scalable, energy-efficient, and sustainable smart farming systems.
Automation of Forks-Conveyor System using Integrated Photodiode Sensor Ricky Sinaga; Lanny W. Pandjaitan; Lukas Lukas
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.v4i2.427

Abstract

Desynchronization between incoming containers and lifting forks prior to the starwheel is a common source of misalignment, container drops, and excessive mechanical load in 19-L bottled water filling lines. This study proposes a low-cost retrofit system that integrates a photodiode sensor with timer–relay logic to regulate start–stop motor control based on the real-time fork position. The system was implemented upstream of the filling station and evaluated during a three-week trial in an operating commercial facility. Results showed that the intervention reduced average starwheel load from 7.54 kg to 2.30 kg and decreased container-fall incidents by approximately 50%. In addition, the modification eliminated the need for one operator per shift, corresponding to annual labor savings of more than IDR 150 million and a payback period of less than one month. These findings demonstrate that photodiode-based synchronization can provide an industry-validated, cost-effective retrofit solution for packaging operations without the requirement for PLC reprogramming or major structural modification. Future work will address long-term durability, adaptability to different container geometries, and the potential integration of feedback and monitoring functions.
An Energy-Efficient ESP32 IoT System for Real-Time Detection of WiFi Deatuhentication Attacks Faizal Riza; Dannie Febrianto Hendrakusuma; Budi Wibowo
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.v4i2.433

Abstract

WiFi deauthentication attacks pose a serious threat to users on public WiFi networks by forcibly disconnecting them from access points, often as a prelude to man-in-the-middle exploits. To counter this threat, we developed an energy-efficient ESP32-based IoT system that monitors WiFi traffic in real time to identify deauthentication attack patterns. The device captures deauthentication frames in monitor mode and immediately notifies users through on-device audible/visual alarms (buzzer, LED/OLED) and digital channels (MQTT dashboard and Telegram bot). Experimental evaluation under moderate and high attack scenarios demonstrated robust performance: detection accuracy remained above 97% even under heavy attack traffic (97.8% at peak intensity). Furthermore, the system’s duty-cycled design limited average power consumption to ~79 mA (~30% lower than continuous monitoring) and achieved a rapid notification latency of ~270 ms, confirming real-time responsiveness. By combining physical indicators with online alerts, the system effectively warns users and improves public digital security literacy by making cyber threats immediately visible and understandable. Overall, these results establish the proposed system as a low-power, real-time attack detection solution that enhances WiFi network security and user awareness.
Genetic Algorithm-Based Contingency Ranking for the 500 kV JAMALI Interconnection System Irnanda Priyadi; Novalio Daratha; Yuli Rodiah; Ika Novia Anggraini; Tri Sutradi; Ade Sri Wahyuni; Makmun Reza Razali
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.v4i2.332

Abstract

The performance of an electric power system is strongly tied to how well it can handle disturbances. In daily operation, one of the most frequent and serious disturbances is the loss of a transmission line. When a line trips, its load must be shared by the rest of the network. Sometimes this redistribution is harmless, but in stressed conditions it can create overloads and trigger further outages. To reduce this risk, system operators rely on contingency analysis. The (N-1) criterion, which considers the effect of losing a single component, is the most common standard. However, when applied to a large network, the number of cases becomes very high, and the analysis can be time-consuming. In this work, contingency ranking using a Genetic Algorithm (GA) is studied for two systems: the IEEE 30-bus test grid and the 500 kV Java–Madura–Bali (JAMALI) interconnection in Indonesia. The GA follows the usual cycle of initialization, selection, crossover, mutation, and fitness evaluation, with the Voltage Performance Index (VPI) used to measure severity. Different parameter settings were tested. The results show that line 36 (bus 28–27) is most critical in the IEEE 30-bus system with a VPI of 56.5915, while line 35 (Bangil–Paiton) is most critical in the JAMALI system with a VPI of 95.3947. These outcomes highlight the usefulness of GA in identifying vulnerable transmission lines.
A Hybrid Neural Network and Sugeno-Type Fuzzy Approach for Object Classification to Assist Navigation of Visually Impaired Individuals Using Ultrasonic Sensor Arrays Ridwan Solihin; Rahmawati Hasanah; Budi Setiadi; Tata Supriyadi; Sudrajat Sudrajat; R Wahyu Tri Hartono
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 Andi Amar Thahara; Christiono Christiono; Miftahul Fikri; Iwa Garniwa M. K.; Mohammad Wirandi
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 Soibatul Aslamia; Deni Haryadi; 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 Mohamad Firdaus; Yasep Azzery; Dimaz Arno Prasetio
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.
Evaluation of a Filament-Winding Composite and Aluminium 6061 Frame for Electric Vehicles Yunus Bakhtiar Arafat; Muhamad Fahmi As'ari; Rachmat Anggi Marvianto; Muhamad Hananuputra Setianto; Wahyu Sulistiyo
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.v4i2.443

Abstract

This study evaluates the structural performance of electric motorcycle frames made of 6061 aluminium alloy and filament winding-based composites using the Finite Element Method (FEM) approach. The main objective of this study is to compare the deformation response, stress distribution, and safety factors of both materials in the same frame geometry and loading conditions, so that the influence of material characteristics on structural behaviour can be analysed objectively. FEM simulations were performed with static loading representing vehicle operating conditions, while aluminium was modelled as isotropic and composite as orthotropic to capture its anisotropic properties. The analysis results show that filament winding composite chassis tend to have more controlled deformation and higher safety factors than 6061 aluminium alloy, although the stress distribution in composites shows sensitivity to fibre configuration and profile thickness. These findings indicate that fibre orientation plays an important role in directing structural stiffness and load distribution in composite chassis. However, the interpretation of stress results and safety factors in composites needs to be done carefully because the Von Mises criteria have limitations in representing anisotropic material failure. The main contribution of this research lies in presenting a controlled structural comparison between metal and filament winding composite materials, as well as confirming the potential and limitations of composites as materials for electric vehicle chassis.

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