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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 59 Documents
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A Fuzzy-Based Spatial Condition Detection System Using Square Area Mapping to Support The Mobility of Individuals with Visual Impairments Supriyadi, Tata; Solihin, Ridwan; Habinuddin, Endang; Sudrajat, Sudrajat; Utomo, TB; Setiadi, Budi; Nugraha, Ramdhan
International Journal of Engineering Continuity Vol. 4 No. 1 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i1.395

Abstract

This research developed, designed, and implemented a cane prototype with the ability to identify spatial conditions, which can help the mobility of blind people in the form of decision information on choosing a path that is free from obstacles. The electronic space sensing system uses ultrasonic-type non-contact/non-visual sensors. Ultrasonic sensors are installed at three points: left, front, and right (L, F, R) of the stick. When the stick swings left-right or vice versa, each sensor will produce an array of distance data and then average it. The average distance of each point is calculated by the Left Side Square Area (LSSA) and Right Side Square Area (RSSA). The LSSA and RSSA values ​​are used as fuzzy input, a fuzzy inference process is carried out using a rule base, and defuzzification is used for decision output on the microcontroller. The system translates the decision results into sound (beep) and vibration information for the user. The results of the second experiment with blind people in two different scenarios show that the system can be an effective support during mobility in the hall and is a feasible prototype for training blind people with new O&M techniques towards the use of travel aids.
Implementation and Analysis of Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for Irrigation Abdurohman, Abdurohman; Siregar, Marsul; Olivia Sereati, Catherine; Windasari, Silviana; W. Pandjaitan, MM. Lanny
International Journal of Engineering Continuity Vol. 4 No. 1 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i1.399

Abstract

Efficient water management in agriculture is crucial due to dynamic environmental conditions and increasing resource scarcity. Fuzzy Inference System (FIS) is widely applied in irrigation control for its ability to handle uncertaintys using rule-based domain knowledge. However, conventional FIS lacks adaptability to environmental changes, limiting its long-term accuracy and responsiveness. Adaptive Neuro-Fuzzy Inference System (ANFIS) addresses this limitation by combining fuzzy logic with neural network learning, enabling automatic adjustment of model parameters based on data patterns. This study compares the performance of FIS and ANFIS in predicting optimal irrigation levels based on soil moisture, air temperature, relative humidity, and solar radiation. A synthetic dataset of 1,000 samples simulating realistic agricultural conditions was generated and normalized to improve computational consistency. The FIS model uses triangular membership functions and five expert-defined fuzzy rules, while ANFIS employs Gaussian membership functions with parameters optimized using the ADAM algorithm over 50 training epochs. Results show that ANFIS outperforms FIS, lowering RMSE from 0.13 to 0.07, halving MAE from 0.10 to 0.05, and increasing R² from 0.85 to 0.93, indicating a substantially better predictive performance. This study demonstrates that ANFIS is more adaptive, accurate, and computationally efficient, contributing to the advancement of intelligent and sustainable irrigation systems in precision agriculture.
Smart Home Security System Using Object Recognition with the EfficientDet Algorithm: A Real-Time Approach Suyatno, Suyatno; Natali, Yus; Reza Fachrurrozi, Nurwan; Roihan, Muhamad; Dorand, Pietra; Ghani, Naufal
International Journal of Engineering Continuity Vol. 4 No. 1 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i1.400

Abstract

The EfficientDet method, which is implemented on the Raspberry Pi for real-time detection in resource-constrained contexts, is the basis for the smart home security system presented in this study.  The system integrates CCTV cameras, motion sensors, and detectors to identify and classify objects, sending notifications via WhatsApp via the Twilio API.  The EfficientDet-D0 model achieves an accuracy of 94.8%, an average processing time of 45 ms, and a memory usage of about 850 MB.  When compared to moving individuals or non-human things, testing shows that stationary human items have a higher detection accuracy.  Notifications are transmitted roughly every three seconds, with an average latency of 1.4 to 1.8 seconds.  The suggested method provides object recognition, real-time monitoring, and configuration flexibility in contrast to traditional IoT-based systems.  These results highlight the potential of EfficientDet as a reliable and adaptable solution for home security.  Future improvements include improving accuracy in a variety of environmental conditions and implementing adaptive learning.
Influence of TiO₂ Nanofluid Concentration on Friction Factor and Reynolds Number in Laminar–Turbulent Flow through 4 mm and 6 mm Acrylic Pipes Ali Nashirudin, Hamzah; Samsul Bakhri, Mohammad; Haryadi, Deni; Poernomo Sari, Sri
International Journal of Engineering Continuity Vol. 4 No. 1 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i1.404

Abstract

This study examines the hydraulic and thermal performance of TiO₂–water nanofluids in small-diameter acrylic pipes, focusing on the influence of nanoparticle concentration and pipe geometry. Experiments were conducted using internal diameters of 4 mm and 6 mm, with TiO₂ volume concentrations of 0.3% and 0.5%. Nanofluids were prepared via a two-step method combining magnetic stirring and ultrasonic sonication to ensure uniform dispersion. Flow parameters, including Reynolds number, friction factor, and Nusselt number, were determined from measured pressure drop and flow rate data. Results show that increasing TiO₂ concentration elevates friction factor, with the effect more pronounced in smaller pipes due to intensified wall shear and higher surface-area-to-volume ratios. The 0.3% nanofluid consistently achieved higher Reynolds numbers and competitive heat transfer performance, while 0.5% concentration often reduced Nusselt number at equivalent flow conditions, likely due to viscosity-induced flow resistance and particle agglomeration. Deviations from classical laminar and turbulent correlations were observed, particularly in the transitional regime, indicating altered boundary layer behaviour. These findings highlight the need for optimised nanoparticle loading and diameter selection to balance heat transfer enhancement against hydraulic penalties in compact thermal management systems.
Development of an IoT-Based Prototype for Optimizing Hazardous Materials and Equipment Storage to Enhance HSE in Laboratories Windasari, Silviana; Abdurohman, Abdurohman; Rochmad, Imbuh; Budiyanto, Setiyo
International Journal of Engineering Continuity Vol. 4 No. 1 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i1.414

Abstract

Laboratory incidents are often precipitated by misplacement of hazardous materials and delayed recognition of unsafe conditions. Earlier laboratory safety solutions typically centered on sensors and dashboards, including IoT monitoring, improve situational awareness but remain largely reactive, operate at room/building scale, seldom enforce item-level storage rules, and rarely report alert selectivity (false-alarm behaviour). This work presents a compact prototype that combines RFID-based storage-zone verification with environmental sensing to support Health, Safety, Security, and Environment (HSSE) compliance at the storage-unit level. An ESP32-based controller integrates three RFID readers (low/medium/high-risk compartments) with temperature humidity and gas sensors; data are streamed to an IoT interface for real-time visualization and notification (e.g., implemented via Blynk), while rule-based logic triggers local (buzzer) and remote alerts when a tagged item is placed in the wrong zone or thresholds are exceeded. A scenario-driven evaluation across 18 cases (correct/mismatched placements for representative items) yielded 100% RFID tag detection and placement validation, an average response time of 2.37 s, and no false alarms under correct placements. These results indicate that joining placement verification with multi-sensor monitoring provides selective, low-latency warnings while avoiding nuisance alerts, establishing quantitative baselines for scalable smart-laboratory HSSE enforcement.
Integration of Javanese Sengkalan and Steganography for Key Exchange in End-to-End Encryption over HTTP Susanto, Eko Heri; Dedy Irawan, Joseph; Pradana Efendi, Fikri
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 Windasari, Silviana; Abdurohman, Abdurohman; Affandi Ratib, Adi; Frihadi, Ade; Montazi, Khalid
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 Sinaga, Ricky; W. Pandjaitan, Lanny; 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 Riza, Faizal; Febrianto Hendrakusuma, Dannie; Wibowo, Budi
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.

Page 6 of 6 | Total Record : 59

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