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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 14 Documents
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Search results for , issue "Vol. 4 No. 1 (2025): ijec" : 14 Documents clear
Implementation and Analysis of Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for Irrigation Abdurohman Abdurohman; Marsul Siregar; Catherine Olivia Sereati; Silviana Windasari; MM. Lanny W. Pandjaitan
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; Yus Natali; Nurwan Reza Fachrurrozi; Muhamad Roihan; Pietra Dorand; Naufal Ghani
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 Hamzah Ali Nashirudin; Mohammad Samsul Bakhri; Deni Haryadi; Sri Poernomo Sari
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 Silviana Windasari; Abdurohman Abdurohman; Imbuh Rochmad; Setiyo Budiyanto
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.

Page 2 of 2 | Total Record : 14

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