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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 75 Documents
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Integrating ISO 50001 and PDCA Cycle for Continuous Energy Performance Improvement in Higher Education Buildings Listiawati, Dwi; Christiono, Christiono; Yunaini A, Ishvandono; Fikri, Miftahul; Amar Thahara, Andi
International Journal of Engineering Continuity Vol. 5 No. 1 (2026): IJEC
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v5i1.472

Abstract

This study proposes a systematic framework for energy performance improvement in institutional facilities by integrating technical auditing with the ISO 50001:2018 standard. Utilizing the Plan-Do-Check-Act (PDCA) cycle, a comprehensive energy baseline for the ITPLN Building was established based on 2024 data, revealing an annual consumption of 1,405,600.80 kWh. In the Check phase, the calculated Energy Consumption Intensity (IKE) of 104.78 kWh/m²/year classified the building as Efficient under ESDM Regulation No. 3/2025. Quantitative analysis identified HVAC (57%) and Lighting (18%) as primary drivers, necessitated by an average ambient temperature of 30°C. To address inefficiencies, the Act phase formulated strategic Energy Saving Opportunities (ESO) such as LED retrofitting and AC standardization. These interventions are projected to reduce consumption by 42,168.02 kWh/year, lowering the IKE to 101.6 kWh/m²/year—a 3% efficiency gain. The study concludes that integrating ISO 50001 with physical audit data provides a replicable and economically measurable strategy for optimizing energy performance, with systematic maintenance recommended to ensure long-term operational sustainability.
IoT-Based Real-Time Vibration and Temperature Monitoring System for Industrial Machinery Using ESP32 and MQTT Ash Shiddiqi, Ahmad; Wasitova, Lilly S.; Hari Nugroho, Djoko
International Journal of Engineering Continuity Vol. 5 No. 1 (2026): IJEC
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v5i1.519

Abstract

This study presents the design and validation of an Internet of Things (IoT)–based real-time vibration and temperature monitoring system for industrial machinery using an ESP32 microcontroller and MQTT communication. The proposed system addresses limitations of periodic manual inspection by enabling continuous monitoring with on-device signal processing and direct compliance evaluation with ISO 10816-3. The main contribution of this work is the implementation of ISO-based vibration severity classification directly at the edge level, integrating multi-sensor acquisition with real-time Root Mean Square (RMS) and Fast Fourier Transform (FFT) processing without relying on predictive or machine learning algorithms. This architecture enables low-latency decision support, reduced bandwidth usage, and improved system independence from cloud computation. The system integrates two ADXL345 vibration sensors and two temperature sensors into a single ESP32 node for synchronized monitoring. Experimental validation on an industrial reciprocating compressor demonstrated stable data acquisition and 100% communication availability during testing. RMS vibration values ranged from 2.15 to 2.17 mm/s, with operating temperatures around 67 °C. FFT analysis identified dominant frequencies consistent with machine characteristics. According to ISO 10816-3 classification, the monitored condition was within safe to early warning levels, confirming the reliability and practical feasibility of the proposed edge-based monitoring approach for condition-based maintenance.
SIPANDU: An IoT-Based Integrated River Waste Monitoring and Collection System Powered by Solar Energy Kamil, Irfan; Christiono, Christiono; Salmah An’nafri, Davina; Billy, Billy; Lisdi Pamungkas, Surya; Wijaya Hidayat, Yongky
International Journal of Engineering Continuity Vol. 5 No. 1 (2026): IJEC
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v5i1.523

Abstract

River pollution caused by waste accumulation, especially plastic waste, has become a significant environmental problem in urban areas. Rivers act as transportation routes that carry waste from land to sea and contribute to global plastic pollution. Various Internet of Things (IoT)-based water quality monitoring systems have been developed to monitor environmental conditions in real time. However, most existing research still focuses on monitoring environmental parameters without integrating with automatic waste transport mechanisms or independent energy sources, which limits the effectiveness of the system, especially in river locations far from electricity sources. This study proposes SIPANDU (Integrated River Waste Monitoring System), an IoT-based system that integrates direct river condition monitoring, waste transport mechanisms using automatic conveyors, and the use of renewable energy through solar power plants. This system consists of a 100 Wp solar panel, a battery for energy storage, water quality sensors (pH and TDS), an ultrasonic sensor to detect the presence of waste, and a web-based monitoring platform for real-time data visualization. The test results show that the solar panels produce a maximum power of 61.1 W with an average power of around 41.87 W. The conveyor system is capable of transporting up to 5 kg of waste with an average power consumption of 33.43 W. The integration of the IoT system, renewable energy, and automatic waste transportation shows that SIPANDU can function as a river monitoring system as well as a sustainable technology solution for waste management in rivers.
Optimizing Heritage Power Distribution Using Zonal TM/TR-Package Systems Suparjo, Suparjo; Wardana Abyoto , R. Kun; Tjahjono, Hendro
International Journal of Engineering Continuity Vol. 5 No. 1 (2026): IJEC
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v5i1.525

Abstract

Electrical power distribution in heritage conservation areas faces the dual challenge of meeting modern technical standards while adhering to strict preservation regulations. Traditional centralized low-voltage systems often result in critical voltage drops and power losses due to extended feeder lengths. This study proposes and evaluates an optimized Zonal TM/TR-Package distribution system as a solution. Using ETAP 22.5 software, a comparative load flow analysis was conducted on a 20-hectare government heritage complex comprising 21 protected buildings. The simulation results confirm that the proposed zonal configuration significantly outperforms the existing centralized system, reducing maximum voltage drops from 12.04% to 4.48% and decreasing total active power losses (I2R) by 47.6%, and improving system efficiency from 95.32% to 97.50%, ensuring full compliance with PUIL 2020 safety standards. Critically, the reliability assessment — evaluated using SAIDI and SAIFI indices — demonstrates a 37.44% improvement in SAIDI (from 9.27 to 5.80 hours/customer/year), while SAIFI remains stable at 0.77 interruptions/customer/year, confirming that zonal fault isolation substantially reduces outage duration without increasing interruption frequency. Furthermore, the modular design minimizes physical footprint, preserving the site’s aesthetic value. Beyond a case study, this research contributes to electrical engineering science by establishing a replicable technical framework for revitalizing heritage infrastructure, balancing efficient power delivery with architectural conservation.
Integrated 3-Layer Online Test Cheating Detection System Using YOLOv8, InsightFace, and GazeTracking Modules Murjitama, Farrel Laogi; Purwanto, Yudhy S.
International Journal of Engineering Continuity Vol. 5 No. 1 (2026): IJEC
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v5i1.529

Abstract

The adoption of online tests has introduced significant challenges in maintaining academic integrity, particularly in real-time detection of cheating behaviors. This study proposes an intelligent proctoring system that automatically detects suspicious participant behavior during an online test by integrating image processing and computer vision techniques. The system integrates a YOLOv8s model based on the YOLO neural network algorithm to localize and classify facial states and suspicious objects in each video frame. This detection layer is complemented by an InsightFace face recognition module, which extracts deep facial embedding features and performs similarity matching against a registered reference image to continuously verify the identity of the participant and detect attempts at impersonation. In parallel, the GazeTracking module analyzes eye landmarks and pupil dynamics to monitor eye behavior, including blinking and significant gaze deviation, providing additional behavioral cues related to attention and potential cheating. The system consists of three detection layers: (1) YOLOv8s for object and behavior detection, (2) InsightFace for identity verification, and (3) GazeTracking for eye behavior analysis. Together, these components form a synchronized computer vision module that performs real-time analysis from live video streams, allowing the system to classify behavioral states such as abnormal head orientation, multiple faces, foreign objects, no face detected, identity mismatch, and eye closure. The experimental results show that the YOLOv8s model achieves an mAP@50 of 0.9918, a precision of 0.9856, and a recall of 0.9903 on the validation set while maintaining real-time performance at an average of 10 frames per second. The findings demonstrate that deep learning-based visual monitoring can effectively support automated online exam supervision, offering a viable computer vision-based proctoring approach.

Page 8 of 8 | Total Record : 75

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