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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 80 Documents
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Integrating ISO 50001 and PDCA Cycle for Continuous Energy Performance Improvement in Higher Education Buildings Dwi Listiawati; Christiono Christiono; Ishvandono Yunaini A; Miftahul Fikri; Andi Amar Thahara
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 Ahmad Ash Shiddiqi; Lilly S. Wasitova; Djoko Hari Nugroho
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 Irfan Kamil; Christiono Christiono; Davina Salmah An’nafri; Billy Billy; Surya Lisdi Pamungkas; Yongky Wijaya Hidayat
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; R. Kun Wardana Abyoto; Hendro Tjahjono
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 Farrel Laogi Murjitama; Yudhy S. Purwanto
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.
Leveraging LangChain for Enhanced Tourism Guidance: A Retrieval-Augmented Generation Approach for SmartTour Chatbot Esa Firmansyah Muchlis; Agus Mulyanto; Nasril Sany; Atikah Rifdah Ansyari
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.v5i1.533

Abstract

SmartTour chatbot is designed to provide accurate and relevant tourism guidance to travelers visiting Barru Regency. Developed using the Streamlit framework, the application offers a user-friendly interface where users can interact with the chatbot to receive information about local attractions, cultural heritage, and tourism-related services. The chatbot uses GPT-4.1 and leverages a Retrieval-Augmented Generation (RAG) approach, integrating contextual data extracted directly from a tourism guide PDF into a vector database to ensure the accuracy of responses. Text preprocessing, including text cleaning and tokenization, is implemented to enhance the system's ability to process and understand user queries effectively. The system's performance was optimized with parameters such as chunk_size = 1500, chunk_overlap = 150, and k = 9 to improve data retrieval efficiency and ensure the relevance of responses. The system was evaluated with 10 valid tourism-related questions designed to assess the chatbot's accuracy in providing relevant answers. The performance was tested under two conditions: with and without text preprocessing, achieving an accuracy rate of 80% with preprocessing and 60% without. This study demonstrates the effectiveness of combining large language models with retrieval systems to create a dynamic and reliable tourism assistant, offering valuable insights into improving tourism services in Barru Regency and similar regions.
Optimizing Battery Charging Power Cell of Electric Car Battery by Smart Charging Deep Learning Algorithm Wawan Kurniadi; Denni Kurniawan
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.v5i1.498

Abstract

The rapid growth of the automotive industry has accelerated the adoption of electric vehicles (EVs), in which battery systems play a critical role as the primary energy storage component. Efficient battery charging during the production process is therefore essential to ensure product quality, operational efficiency, and long-term battery performance. This study aims to optimize the battery cell charging process in electric vehicle manufacturing by implementing a smart charging strategy based on deep learning techniques, specifically the LSTM model. Historical charging data and relevant operational variables, including voltage, current, and time characteristics, are utilized to train the LSTM model to predict optimal charging parameters. The proposed approach enables adaptive and intelligent control of charging current and voltage profiles during production. The results demonstrate that the LSTM-based smart charging method improves charging efficiency, reduces potential battery degradation, and enhances manufacturing process consistency compared to conventional charging methods. In conclusion, the application of deep learning–based smart charging provides a promising solution for optimizing EV battery production processes. This research contributes to the development of intelligent battery management systems and supports the advancement of sustainable transportation and EV manufacturing technologies.
The Comparison of Solar Module Damage Texture Analysis using GLCM and LBP Widya Tari; Rizqia Cahyaningtyas
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.v5i1.521

Abstract

Solar modules play a vital role in renewable energy systems by converting sunlight into electrical energy. Over time, the surface of the panels can develop various issues such as cracks, scratches and stains, leading to a reduction in efficiency and energy output. Manual inspections have limitations in terms of time and cost; therefore, a solar panel damage detection system is required, utilising a reliable method for image analysis. The data used to test the model comprised 24 solar modules images, sourced from primary and secondary data. The collected images represent both physical and electrical damage. The methods used for feature extraction utilised the Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) techniques. GLCM features were calculated at four different angles (0°, 45°, 90°, and 135°), incorporating metrics such as contrast, dissimilarity, homogeneity, energy, and ASM, whilst LBP features were extracted using metrics such as mean, variance, and entropy. The process continued with damage segmentation of the images using Otsu Thresholding to calculate the proportion of damaged area. The results of the study show that the largest detected damaged area reached 35% for GLCM and 27% for LBP. These results indicate that GLCM is more effective in class separation, whilst LBP is capable of capturing local texture patterns. This model has the potential to support the automatic maintenance of solar panels and improve the efficiency of solar energy utilisation.
The Impact of the Breakdown of the Sidoarjo Mud Dam on the Northeast Side Using the HEC-RAS Program Abdurahman Hafizudin; Budijanto Widjaja
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.v5i1.458

Abstract

The eruption of the Sidoarjo mud, commonly known as the Lapindo mudflow, has continuously dumped slurry since 2006, necessitating the construction of an emergency containment embankment with limited engineering control and a high risk of structural failure. One of the critical locations is the northeast embankment segment (P.75), which is located near the residential zone of South Kalitengah, Gempolsari Village. This study aims to predict the flow direction and the level of impact of mud inundation if there is a breakdown of the embankment. Five dam breakdown simulation scenarios were performed using HEC-RAS by varying the Liquid Limit Index (LI), with the material behavior modeled as a non-Newton Bingham flow characterized by viscosity and yield stress parameters, supported by DEM based topography, Manning coefficients derived from land cover, and inflow hydrographs. The results showed that higher LI values led to slower settling times, longer and wider flow paths, shallower flow depths, and much larger inundation areas, extending to the residential sector. The simulated transport and deposition metrics were aligned with previously documented mudflow events, although the flat local topography led to slower accumulation. This study contributes an empirical LI-based rheological-hydrodynamic relationship to predictive hazard modeling and provides important insights for regional risk mitigation, emergency evacuation planning, and embankment strengthening strategies in mud volcanic disaster zones. In addition, a scenario-based sensitivity and uncertainty analysis was performed to assess the influence of rheological variations, surface roughness, and hydrographic parameters on inundation area and maximum depth, so that the interpretation of the results can be placed in the context of input limitations and numerical assumptions.
Sensitivity of Coal Reserve Economic Feasibility to Price Fluctuations: A Case Study of an Open-Pit Coal Mine in East Kalimantan, Indonesia Arif Deswanda Cismawan; Eddy Ibrahim; Eddy Sutriyono; M. Taufik Toha; Maulana Yusuf; Rahmat Wahyudi Putra
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.v5i1.547

Abstract

Coal price volatility is one of the most important factors affecting the economic feasibility of mining operations, particularly marginal coal reserves that are highly sensitive to changes in cost and revenue. This study evaluates the economic feasibility of marginal coal reserves under different coal price scenarios using a case study from an open-pit coal mine in East Kalimantan, Indonesia. The analysis applies a simplified discounted cash flow framework combined with operating margin analysis, critical price determination, and price sensitivity assessment. The results show that, at a base coal price of USD 43,97/ton, the project remains economically feasible, with an operating margin of USD 6,05/ton and a net present value of USD 82,91 million. However, a 20% decline in coal price results in a negative margin of USD 2,74/ton, making the project no longer economically feasible. The critical price was identified at USD 37,92/ton, representing the break-even threshold. The sensitivity analysis further demonstrates that the economic status of marginal coal reserves is dynamic and can shift from feasible to marginal or not feasible depending on market conditions. These findings highlight the importance of integrating price sensitivity into reserve evaluation and mine planning. The proposed framework provides a practical decision-support approach for reserve classification, production planning, and resource optimization under uncertain economic conditions. The main contribution of this study is the development of a practical techno-economic classification framework that links coal price variation with reserve feasibility status through the integration of critical price, operating margin, and break-even stripping ratio. This framework allows marginal reserves to be evaluated as dynamic economic entities rather than as fixed reserve categories under a single base-case price assumption.

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