International Journal of Engineering Continuity
International Journal of Engineering Continuity (IJEC) is a peer-reviewed international journal that publishes high-quality original research articles and review papers in engineering and technology, with a particular emphasis on system reliability, operational continuity, resilience, sustainability, and engineering innovation. The journal provides a platform for researchers, academics, engineers, and practitioners to disseminate original research findings and technological advancements that contribute to sustainable engineering solutions. IJEC welcomes original research articles and review papers across a broad range of engineering disciplines, including, but not limited to, materials engineering, mechanical engineering, electrical engineering, civil engineering, industrial engineering, manufacturing systems, renewable energy, transportation systems, automation and control, artificial intelligence, the Internet of Things (IoT), computer engineering, data science, smart systems, engineering management, and other interdisciplinary engineering applications that support operational continuity and sustainable development. IJEC is published biannually, in March and September, by Sultan Publisher. Publisher: Sultan Publisher, Indonesia Published since 2022
A Traceable Engineering-Evidence Framework for Maintenance Prioritization of Public Direct Current Fast Chargers
Public direct current fast chargers generate heterogeneous operational snapshots, service records, and platform-specific diagnostics that cannot be treated directly as field-supported failures. Existing monitoring identifies abnormal states, while conventional Failure Mode and Effects Analysis (FMEA) ranks an already defined failure taxonomy; neither explicitly operationalizes a qualification step between raw diagnostics and maintenance mechanisms. This study develops a pre-FMEA engineering-evidence framework integrating data normalization, subsystem classification, evidence registration, service-report correlation, manufacturer-document verification, and structured score review. The bounded case comprised 30 ABB Terra T54HV diagnostic datasets collected across multiple public sites during a January–December 2025 collection window (19,866 records and 877 unique keys) and 30 associated field service reports. The evidence set retained a common cross-source schema and linked diagnostic context to documented interventions; it is not claimed to represent all charger populations or a complete failure census. Of the diagnostic keys, 546 formed a common cross-source core and 217 varied across sites. Three primary service categories—communication and authorization, global interlock, and voltage-related cases—accounted for 25 of 30 documented interventions (83.3%). Their internally reviewed screening RPNs were 105, 90, and 72; no score was independently validated. The framework supports inspectable maintenance prioritization by preserving a source-to-mechanism evidence path, excluding snapshot repetition from occurrence, disclosing overlapping mechanism links, and retaining unresolved scores as provisional.
Copyright (c) 2026 Herlangga Arisanto, Masbah R. T. Siregar, Baskoro Abie Pandowo https://creativecommons.org/licenses/by-sa/4.0/
Active Low-Frequency Acoustic Excitation Wave Intervention for Al2O3 Nanofluid Stabilization and Convective Heat Transfer Enhancement in a Double-Pipe Heat Exchanger
The practical deployment of nanofluids within industrial thermal engineering is frequently constrained by nanoparticle agglomeration and subsequent sedimentation. This experimental study investigates the efficacy of active low-frequency acoustic excitation in enhancing convective heat transfer performance and temporarily mitigating the macroscopic settling of Al2O3-water nanofluids within a counter-flow double-pipe heat exchanger. Surfactant-free colloidal suspensions of γ-Al2O3 nanoparticles were synthesized at volume fractions ranging from 0.2% to 0.8%, and subjected to continuous acoustic frequencies of 100 Hz, 500 Hz, and 1000 Hz. Statistically validated three-dimensional surface response modeling (p<0.001) reveals that the maximum convective heat transfer coefficient, reaching approximately 620 W/m2. K, is attained at the maximum tested volume fraction of 0.8% coupled with a 1000 Hz acoustic frequency. It is hypothesized that the low-frequency acoustic waves generate sustained macroscopic acoustic streaming, which perturb the viscous sublayer and induce forced mixing. Consequently, qualitative visual inspections suggest that this acoustic agitation temporarily delays macroscopic particle sedimentation over the observed short-term operating window without requiring chemical surfactants. While this work demonstrates the fundamental thermal potential of active non-chemical interventions, long-term colloidal stability, net energy benefits, and in-situ hydraulic penalties remain unverified, highlighting critical requirements for future industrial validation.
Copyright (c) 2026 Ario Geraldi, Sri Poernomo Sari https://creativecommons.org/licenses/by-sa/4.0/
Building Information Modeling Using Cubicost TME for Electrical Quantity Take-Off and Website-Based Cost Estimation at BTN Ecopark Gandul
: Conventional quantity take-off (QTO) of electrical works relies on two-dimensional drawings and cannot capture the actual three-dimensional routing of feeder cables, cable trays, and cable ladders, causing volume and cost inaccuracy. This research optimizes electrical works QTO using Building Information Modeling (BIM) Cubicost Take-off for Mechanical and Electrical (TME) and implements the validated results into a website-based cost estimation system. Its novelty is an applied, integrated workflow connecting Cubicost TME-based electrical QTO, engineering verification under the General Requirements for Electrical Installations (PUIL) 2020/SNI 0225:2020, and web-based cost reporting within a single Indonesian case study; although each component is established, their end-to-end integration for electrical works is rarely reported. The contribution is a reproducible workflow that converts model-derived quantities into standard-verified, openly accessible cost information, repositioning engineering validation as an integral gate within QTO. Using a quantitative descriptive-comparative method at the BTN Ecopark Gandul project, the installation was modeled three-dimensionally and validated through voltage drop, ampacity, and power demand calculations. On the conventional baseline, BIM-based cable volumes are 8.59% lower with an identical panel count, and the BIM cost estimate is 3.95% lower; all routes satisfy the 5% voltage drop limit, peaking at 1.641%. On an equivalent net basis (after removing the 5% waste allowance present only in the conventional data), the volume deviation narrows to 4.02%, showing that about half of the gross gap reflects the waste allowance and the remainder genuine routing differences; the cost difference is thus an estimated, case-specific figure, not a confirmed saving.
Copyright (c) 2026 Sugiarti Kimayah, Masbah R.T. Siregar, Taswanda Taryo https://creativecommons.org/licenses/by-sa/4.0/
Experimental Comparison of Conventional PID, Adaptive PID, and Hysteresis Temperature Control in a Laboratory-Scale Vegetable-Oil Biodiesel Reactor
The increasing demand for energy and the limited availability of diesel fuel have encouraged the development of biodiesel as a renewable energy source. Temperature stability during transesterification is important because it affects reaction performance and biodiesel yield. This study aimed to experimentally compare conventional PID, adaptive PID, and hysteresis temperature control in a laboratory-scale biodiesel reactor. The system was tested using palm oil and castor oil at setpoints of 50°C and 60°C with conventional PID, adaptive PID, and hysteresis control. Each combination of control method, feedstock, and temperature was evaluated in a single experimental run. The system consisted of a Type-K thermocouple, temperature controller, solid-state relay, heating element, magnetic stirrer, RS-485 module, and computer. Performance was evaluated based on rise time, overshoot, settling time, steady-state error, density, and yield. Under the tested conditions, adaptive PID produced no overshoot and showed the lowest observed controller-indicated steady-state error of 0.056°C. Hysteresis control produced the shortest rise and settling times but showed the highest observed steady-state error of 0.625°C and overshoot under several conditions. The highest observed yield in the single-run experiments was 95.423%, obtained from castor oil at 60°C using adaptive PID.
Copyright (c) 2026 Nikolaus Dharmawan Dharsono, Lilly S. Wasitova, Masbah R.T. Siregar https://creativecommons.org/licenses/by-sa/4.0/
Interpretable machine learning for prediction and inverse design of electrical resistivity multicomponent aluminium alloys
Aluminium is the standard interconnect metal in CMOS technology and can be deposited at low temperature, yet its intrinsically low electrical resistivity makes it inefficient as a Joule heating element, forcing microheater designs toward platinum or polysilicon at the cost of process complexity. This study reframes the problem as one of composition engineering rather than material substitution and develops a machine learning framework to predict and inversely design the electrical resistivity of aluminium alloys. A curated dataset of 220 commercial alloys, described by nine compositional features (Si, Fe, Cu, Mn, Mg, Cr, Zn, Ti, Zr) and spanning 2.80–6.40 µΩ·cm, was assembled from a public materials database. Four regression models were benchmarked: Linear Regression (R² = 0.635), Support Vector Regression (R² = 0.821), Random Forest (R² = 0.837), and Extreme Gradient Boosting, which performed best on an independent hold-out test partition (test R² = 0.861; test MAPE = 3.9%) and remained stable under k-fold cross-validation carried out within the training partition (CV-R² = 0.839; CV-RMSE = 0.396 µΩ·cm). The pronounced gap between the linear baseline and the non-linear learners confirms that resistivity in multicomponent aluminium alloys is governed by interacting solute effects. Feature importance analysis identified magnesium (0.554), copper (0.215), and zinc (0.153) as the dominant contributors, accounting for approximately 92% of the model's explanatory power, consistent with solid-solution and impurity scattering mechanisms. Surrogate-based inverse screening produced heavily alloyed Al–Zn–Mg–Cu candidates with predicted resistivity of 9.26–9.52 µΩ·cm. These values lie outside the multivariate domain of the training data: the candidates carry 24.2–32.3 wt.% total solute, leaving an aluminium balance of only 67.7–75.8 wt.%, and they exceed the training maximum for Cu, Zn and Zr in every case. They are therefore reported as an extrapolative indication of direction in composition space rather than as validated alloy candidates, and require aluminium-balance constraints, thermodynamic screening and experimental validation before any device-level claim can be made.
Copyright (c) 2026 Deni Haryadi, Aji Abdillah Kharisma, Hamzah Ali Nashirudin, Haris Rudianto https://creativecommons.org/licenses/by-sa/4.0/
Field-Validated Triplen Harmonic and Neutral Current Mitigation Using an Active Harmonic Filter
Nonlinear loads in commercial buildings increasingly distort low-voltage distribution systems and may cause excessive neutral-current loading. Third-order triplen harmonics are especially critical in three-phase four-wire systems because their zero-sequence components accumulate in the neutral conductor instead of cancelling among phases. Despite extensive harmonic research, few field-based studies jointly quantify triplen-driven neutral loading, verify Active Harmonic Filter (AHF) performance on an operating commercial site, and confirm IEEE Std 519-2022 compliance within a single case, leaving a practical evidence gap. This paper presents a field-validated assessment of triplen harmonic mitigation in a commercial building distribution system using a shunt Active Harmonic Filter (AHF). Field measurements were obtained at the incoming side of the SDP Amazon Atrium Senen distribution panel using a power quality analyzer, and the results were evaluated using IEEE Std 519-2022. A MATLAB/Simulink model was developed to represent the measured operating condition and support performance interpretation. The model was validated against the field data—accurate for voltage distortion and power factor but under-predicting current distortion—so it is positioned as a trend-analysis rather than a sizing tool. The pre-mitigation condition showed severe current distortion, with phase-current THDi values of 69.27%, 81.47%, and 56.28%, a voltage THD of 5.40%, a neutral current of 96.89 A, and a power factor of 0.82. After AHF operation, the THDi values decreased to 9.87%, 12.18%, and 8.46%, while THDv decreased to 3.35%, neutral current decreased to 23.41 A, and power factor increased to 0.99. Whereas conventional THDi and TDD indices quantify overall distortion but not the zero-sequence burden carried by the neutral conductor, this study introduces a dedicated metric for that burden. The proposed Neutral Harmonic Mitigation Ratio (NHMR) reached 75.84%, indicating substantial reduction of triplen-related neutral loading. Evaluated through Total Demand Distortion under the measured short-circuit ratio, the post-mitigation condition satisfied the IEEE Std 519-2022 current-distortion limit. The results show that a field-validated AHF approach can effectively mitigate triplen harmonic effects and improve power quality in commercial three-phase four-wire distribution systems. Beyond the numerical gains, the field-validated framework gives facility engineers a practical, standards-referenced basis for diagnosing neutral-current risk and planning AHF-based mitigation in commercial power systems.
Copyright (c) 2026 Zakky Daniel Haq; Kuntjoro Pinardi, Abdul Multi https://creativecommons.org/licenses/by-sa/4.0/
Predictive Modeling of Nonlinear Breakdown Voltage in Silicone Rubber Polymer Insulators with Fly Ash Filler Using the GLME and BPNN Methods
The expansion of Indonesia's transmission and distribution network increases the demand for high-voltage insulator materials with reliable dielectric performance and sustainable manufacturing. Silicone rubber (SiR) filled with coal fly ash is a promising alternative; however, its breakdown voltage varies nonlinearly with filler composition, temperature, and fly ash source. This study proposes a complementary statistical machine learning framework using Generalized Linear Mixed Effects (GLME) for interpretable statistical analysis and a Backpropagation Neural Network (BPNN) for flexible nonlinear prediction. The analysis used 512 secondary observations from four Indonesian fly ash sources, with filler compositions of 10–80% and temperatures of 35–50°C. Breakdown voltage increased with fly ash content up to an optimum of approximately 60–65% before declining at higher loadings, while increasing temperature consistently reduced dielectric strength. GLME achieved R² = 0.7457, RMSE = 0.0354, and MAPE = 1.57%, whereas the five-fold cross-validated BPNN showed slightly better average predictive performance, with R² = 0.7617, RMSE = 0.0343, and MAPE = 1.56%. GLME provides interpretable and statistically testable coefficients, whereas BPNN captures additional complex nonlinear patterns without requiring a predefined functional form. SEM and XRF characterization supported the observed nonlinear trends through particle dispersion and fly ash oxide composition, predominantly SiO₂ and Al₂O₃. The proposed complementary framework combines statistical interpretability with nonlinear predictive capability, supporting fly ash composition selection, efficient material screening, and breakdown voltage prediction for high voltage silicone rubber insulator applications.
Copyright (c) 2026 Davina Salmah An’nafri, Christiono Christiono, Miftahul Fikri, Nurmiati Pasra, Andi Dyah Harum https://creativecommons.org/licenses/by-sa/4.0/
Chemical Fingerprint Assessment of Surface Resistance in Silicone Rubber Insulators Using Coal Fly Ash as Filler
To support the infrastructure targets of Indonesia's PLN Electricity Supply Business Plan (RUPTL) 2025–2034 while managing abundant coal fly ash waste, this study investigates CFA valorization as a functional filler in silicone rubber insulators. Addressing the need for sustainable grid expansion, the research primarily focuses on the relationship between FTIR-derived chemical descriptors, CFA composition, and composite surface resistance. Chemical fingerprinting analysis demonstrates that CFA with high SiO₂ content (42.0%) better supports the composite's siloxane networks compared to iron-rich variants. Statistical modeling (adjusted R² > 0.92) confirms filler concentration as the primary predictor of resistance (p < 0.001). Optimal structural stability was associated with a 2:1 stoichiometric ratio of dimethyl groups—directly attached to the central silicon atoms (Si(CH₃)₂)—relative to the Si-O-Si backbone. Furthermore, a critical percolation threshold was identified at 70% filler concentration, beyond this loading level the integrity of the fundamental PDMS polymer structure is compromised, resulting in deteriorated insulator performance. These findings suggest that FTIR-based chemical fingerprinting has the potential to serve as an effective quality control framework, offering a scientifically grounded, sustainable material alternative in direct alignment with RUPTL strategic objectives.
Copyright (c) 2026 Harjib Haridh, Syamsir Abduh, Christiono Christiono https://creativecommons.org/licenses/by-sa/4.0/
Comparative Evaluation of Artificial Neural Networks and Monte Carlo Simulation for Transformer Insulating Oil Lifetime Prediction
Transformer insulating oil is an important factor for the reliability and service life of power transformers. It provides electrical insulation and heat dissipation. Accurate lifetime prediction is essential for asset management and condition-based maintenance. In this study, the comparison of Artificial Neural Network (ANN) and Monte Carlo Simulation (MCS) techniques is presented to predict transformer insulating oil lifetime based on three physicochemical parameters such as acid content, moisture content and breakdown voltage. The model was developed and validated on an experimental dataset of 18 transformer insulating oil samples. The ANN model was based on a multilayer perceptron architecture with three hidden layers (80-80-40 neurons). The MCS model was run for 3000 simulation iterations to include the input uncertainty. The model performance was assessed using mean Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and coefficient of determination (R2). The ANN model produced better results with a MAPE of 8.20%, an RMSE of 4.15 months and an R2 of 0.98, surpassing the MCS model, which achieved a MAPE of 11.50%, an RMSE of 9.40 months and an R2 of 0.89. The results presented show that ANN is a more accurate and reliable methodology for the prediction of transformer insulating oil lifetime that allows efficient condition-based maintenance and transformer asset management.
Copyright (c) 2026 Irnanda Priyadi, Yuli Rodiah, Makmun Reza Razali, Shara Alya Gifani Muhyisunah https://creativecommons.org/licenses/by-sa/4.0/
Predicting Employee Intent-to-Stay from Engagement Survey Data: An Interpretable, Class Imbalanced Machine Learning Case Study
Employee retention is a strategic need for capital intensive firms, such as state-owned power enterprises, whose service continuity throughout the upstream to downstream value chain relies on a stable and engaged workforce. Most predictive HR studies focus on attrition; however, a proactive approach necessitates recognizing employees whose commitment to remain is not firmly established, allowing for retention initiatives to commence prior to the escalation of disengagement. This research establishes an interpretable machine learning framework to forecast employee desire to remain, based on the CRISP-DM approach. The organization wide Employee Engagement Survey, comprising 32,907 respondents and 102 engineered predictors across 53 engagement items and demographic attributes, involves preprocessing, exploratory analysis, dimensionality reduction (PCA and t-SNE), K Means clustering, supervised classification, multi metric evaluation, and permutation based interpretability. The aim is highly skewed as only 6% of employees reported less than full commitment to stay. The evaluation is therefore focused on ROC AUC, recall and precision recall (PR AUC) and not accuracy. Six algorithms were evaluated. Logistic Regression found the optimal balance (ROC AUC = 0.853, recall = 0.758, PR AUC = 0.318) accurately identifying about 75% of employees not fully committed. Interpretability study identified proximity to retirement age, confidence in the company's future, feeling of vitality at work and achievement of career goals as most significant determinants.. The contribution is not a novel algorithm but rather the insight revealed by this analysis: proximity to retirement is the predominant factor, causing a simplistic model to disproportionately identify senior employees. This illustrates that proactive intent to stay predictions should be regarded as interpretable decision support rather than solely an accuracy driven endeavor.
Copyright (c) 2026 Bagus Satrio Diharjo, Ira Puspitasari, Agustinus Titis Iswara https://creativecommons.org/licenses/by-sa/4.0/
Integrating Demand Forecasting, Aggregate Planning, and Sensitivity Analysis for Cost-Efficient Production: A Case Study in Furniture Manufacturing
Production planning under demand uncertainty remains a critical challenge in make-to-stock manufacturing systems, particularly when forecasting results are not explicitly linked to cost-based planning decisions. This study develops an integrated framework that combines demand forecasting, aggregate planning, and sensitivity analysis to identify a cost-efficient production policy in a furniture manufacturing company. A quantitative case study was conducted using 12 months of historical demand data for S4S products from July 2024 to June 2025. Three forecasting methods, namely Single Exponential Smoothing, Linear Regression, and Holt’s Trend Method, were evaluated using MAPE, MAD, and RMSE. The best-performing forecast was then used as input for aggregate planning under Level and Chase strategies. To assess the robustness of the planning decision, a one-way sensitivity analysis was conducted by varying key cost parameters by ±20%. The results show that Holt’s Trend Method with α = 0.4 and β = 0.1 provided the best overall forecasting performance, with a MAPE of 1.63%, MAD of 67.34 units, and RMSE of 106.08 units. Using this forecast as the demand input, the Chase Strategy generated the lowest total production cost of Rp.185,900,000, compared with Rp.189,523,750 under the Level Strategy. Sensitivity analysis confirmed that the Chase Strategy remained the preferred strategy under all tested cost-parameter scenarios. These findings demonstrate that integrating forecasting validation, aggregate planning, and sensitivity analysis can improve medium-term production planning decisions and provide practical guidance for manufacturing firms facing fluctuating demand.
Copyright (c) 2026 Ardhy Lazuardy, Syehan Syehan, Arief Nurdini https://creativecommons.org/licenses/by-sa/4.0/
Innovation in an IoT-Based Smart Biogas Reactor Prototype for Converting Household Organic Waste into Alternative Energy
Biogas is a gas produced by the anaerobic decomposition of organic matter with the aid of microorganisms; it primarily consists of methane (CH₄), which has a high calorific value and holds potential as an alternative energy source. Biogas production is carried out using a biogas reactor as the fermentation medium. The use of household waste as feedstock aims to generate economical and environmentally friendly energy while reducing waste and water pollution caused by leachate. The development of alternative energy is becoming increasingly relevant amid global energy supply instability, including the impact of geopolitical dynamics in the Strait of Hormuz region, which serves as a major global oil distribution route. With technological advancements, innovations in smart biogas reactors based on the Internet of Things (IoT) enable real-time monitoring and control of process parameters to enhance methane gas production efficiency. This study employs an experimental method to evaluate the effectiveness of a biogas reactor design utilizing IoT devices, with a focus on real-time monitoring of temperature, pH, and methane gas concentration parameters. Based on the test results, the methane gas concentration increased from 14.08 ppm on the first day to 16.79 ppm on the ninth day. Fermentation conditions indicated an increase of 19.25%. The developed system design utilizes an ESP32 microcontroller integrated with DHT11, PH-4502C, and MQ-4 sensors, and employs Blynk and ThingSpeak for data visualization. The selection of sensors was based on considerations of suitability for the application’s needs, ease of integration with the microcontroller, market availability, and cost-effectiveness of implementation. Test results indicate that the reactor design and system can consistently transmit and present data within an optimal communication range, thereby contributing to improved efficiency, explosion safety, gas leak prevention, and control over the fermentation process in the constructed reactor. During testing, the results showed that the detected gas leak level was 0% throughout the entire observation period. Thus, the system demonstrated a 100% success rate in leak prevention. The implementation of this reactor design supports the development of a more modern, effective, and sustainable renewable energy processing system.
Copyright (c) 2026 Zulfahmi Noor, Nurmasitya Kemalaintan, Marvel Dwi Gulan Silindang, Anisa Suliyanti https://creativecommons.org/licenses/by-sa/4.0/
Experimental Investigation of a PV-Powered Blower-Assisted Solar Still: Effects of Seawater Volume on Evaporation–Condensation Efficiency
This study investigates the performance of a photovoltaic-assisted solar still integrated with a blower system to enhance desalination efficiency. The system was experimentally evaluated to examine the influence of thermal parameters on freshwater productivity and system efficiency. The results show that distilled water production ranges from 0.35 to 0.55 L/day, with an efficiency of 7–8%. The highest productivity occurred at an evaporator temperature of 47.6–48 °C, confirming temperature as the dominant parameter. The PV-powered blower increased the temperature gradient between the evaporator and condenser by promoting forced air circulation, improving heat and mass transfer. However, the system performance remains within the conventional range, indicating that heat losses and limited condensation efficiency are key constraints. This study demonstrates the potential of photovoltaic-powered, blower-assisted solar stills to enhance evaporation–condensation processes and provides a baseline for further system optimization.
Copyright (c) 2026 Zaenal Ikhsan Abdullah, Ridwan Ridwan, Iwan Setyawan, Hamzah Ali Nashirudin, Deni Haryadi https://creativecommons.org/licenses/by-sa/4.0/
Sensitivity of Coal Reserve Economic Feasibility to Price Fluctuations: A Case Study of an Open-Pit Coal Mine in East Kalimantan, Indonesia
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.
Copyright (c) 2026 Arif Deswanda Cismawan, Eddy Ibrahim, Eddy Sutriyono, M. Taufik Toha, Maulana Yusuf; Rahmat Wahyudi Putra https://creativecommons.org/licenses/by-sa/4.0/
Leveraging LangChain for Enhanced Tourism Guidance: A Retrieval-Augmented Generation Approach for SmartTour Chatbot
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.
Copyright (c) 2026 Esa Firmansyah Muchlis, Agus Mulyanto, Nasril Sany, Atikah Rifdah Ansyari https://creativecommons.org/licenses/by-sa/4.0/
Integrated 3-Layer Online Test Cheating Detection System Using YOLOv8, InsightFace, and GazeTracking Modules
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.
Copyright (c) 2026 Farrel Laogi Murjitama, Yudhy S. Purwanto https://creativecommons.org/licenses/by-sa/4.0/
Optimizing Heritage Power Distribution Using Zonal TM/TR-Package Systems
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.
Copyright (c) 2026 Suparjo, R. Kun Wardana Abyoto , Hendro Tjahjono https://creativecommons.org/licenses/by-sa/4.0/
SIPANDU: An IoT-Based Integrated River Waste Monitoring and Collection System Powered by Solar Energy
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.
Copyright (c) 2026 Irfan Kamil Kamil, Christiono Christiono, Davina Salmah An’nafri, Billy Billy, Surya Lisdi Pamungkas, Yongky Wijaya Hidayat https://creativecommons.org/licenses/by-sa/4.0/
Optimizing Battery Charging Power Cell of Electric Car Battery by Smart Charging Deep Learning Algorithm
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.
Copyright (c) 2026 Wawan Kurniadi, Denni Kurniawan https://creativecommons.org/licenses/by-sa/4.0/
The Comparison of Solar Module Damage Texture Analysis using GLCM and LBP
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.
Copyright (c) 2026 Widya Tari, Rizqia Cahyaningtyas https://creativecommons.org/licenses/by-sa/4.0/
IoT-Based Real-Time Vibration and Temperature Monitoring System for Industrial Machinery Using ESP32 and MQTT
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.
Copyright (c) 2026 Ahmad Ash Shiddiqi, Lilly S. Wasitova, Djoko Hari Nugroho https://creativecommons.org/licenses/by-sa/4.0/
Evaluation of WiFi Broadband Network on the Jabodetabek Commuter Line
This research evaluates the performance of WiFi networks based on the Universal Mobile Telecommunications System (UMTS) backbone on the Commuter Line (CL) to identify technical issues in providing stable internet services in public transportation with high mobility. The main gap lies in the inability of the existing infrastructure to maintain service continuity when the train is mobile compared to when it is immobile. This research uses an experimental method involving three testing scenarios (using a backbone modem, a signal measurement system, and a smartphone device); the study measures Quality of Service (QoS) parameters. Research results show that throughput passing through the UMTS backbone decreased by 16.8% to the DNS server and 12.68% to detik.com when the commuter line was in motion. Furthermore, packet loss passing through the UMTS backbone increased by 13.6% to the DNS server and 11.2% to detik.com when the commuter line was moving. Then, the round trip time passing through the UMTS backbone increased by 175.50% to the DNS server and 179.25% to detik.com when the commuter line was in motion. These results confirm that high mobility causes significant signal degradation, making current UMTS networks not yet capable of supporting users' broadband needs inside train carriages. The contribution of this research is is recommending a new infrastructure design in the form of optimizing BTS placement or using train repeaters to address interference. Therefore, the conclusion of this research is the need to strengthen transmission in the CL so that the WiFi implementation can achieve the expected reliability standards.
Copyright (c) 2026 Eka Sartika Rosiana, Karijadi Karijadi, Makhdor Rosadi, Rivira Yuana, Taswanda Taryo, Lilly S Wasitova, Abdul Multi, Lukman Medriavin Silalahi https://creativecommons.org/licenses/by-sa/4.0/
Rainfall Anomaly Analysis and Seasonal Climate Projection in Palembang City Using CHIRPS Data and Z-Score Method
Using CHIRPS daily rainfall data (1981–2024) which is the standard climatological period according to the World Meteorological Organization (WMO) which is at least 30 years, rainfall anomalies during the dry season (May–October) are analyzed using the Z-score statistical approach. The 2025 prediction scenario integrates moderate El Niño (SST +1.0) and negative IOD (–0.5) as correction factors for the climatological average. Anomalous rainfall values ​​will be converted into z-score values ​​if Z>0 then the value is above the average (wet/hot) and vice versa if Z<0 the value is below the average (dry/cold). The results of the study indicate a significant potential for negative anomalies (Z-score < 0) in most areas of Palembang, indicating a decrease in rainfall below normal levels, with the peak of the dry season projected in August–September 2025. For the rainfall anomaly value, it has been converted into a z-score value that has been explained using the formula so that the significant climate trend in 2024 is 0.34 according to the Standardized Rainfall Index (SPI) in the normal or mild condition category. Finally, the rainfall prediction graph for 2025 shows the final results of the analysis process, namely the highest rainfall of 369.173 mm in April and the lowest rainfall of 60.12 mm in August.
Copyright (c) 2026 Shofwan Mahsyurdin, Ermatita Ermatita https://creativecommons.org/licenses/by-sa/4.0/
The Impact of the Breakdown of the Sidoarjo Mud Dam on the Northeast Side Using the HEC-RAS Program
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.
Copyright (c) 2026 Abdurahman Hafizudin, Budijanto Widjaja https://creativecommons.org/licenses/by-sa/4.0/
Integrating ISO 50001 and PDCA Cycle for Continuous Energy Performance Improvement in Higher Education Buildings
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.
Copyright (c) 2026 Dwi Listiawati, Christiono Christiono, Ishvandono Yunaini A, Miftahul Fikri, Andi Amar Thahara https://creativecommons.org/licenses/by-sa/4.0/
Performance Analysis of an Integrated PV/T–TEC System with a PID enabled DC–DC Boost Converter for Photovoltaic Thermal Management
The temperature-induced efficiency loss of 0.4% to 0.5% for every °C above 25 °C alongside the inherent variability in solar irradiance, poses a critical challenge to the efficiency and stability of Photovoltaic (PV) modules. This study addresses this limitation by developing and analyzing an Integrated Photovoltaic Thermal-Thermoelectric Cooler (PV/T–TEC) system designed for robust thermal management and enhanced energy yield. The proposed system utilizes a synergistic hybrid cooling mechanism: a passive PV/T air collector for bulk heat dissipation from the PV panel's rear surface, coupled with an active Thermoelectric Cooler (TEC) for precise temperature stabilization. The electrical energy flow is managed by a DC–DC Boost Converter employing a PID controller, with a focus on input disturbance rejection, ensuring the TEC operates at an optimal, stable power point.Simulation and performance analysis demonstrate the significant advantages of this hybridized approach. The PV/T air collector was confirmed as the primary thermal component, achieving a peak heat dissipation QEmit approximately 7.5 times greater than the TEC-only configuration. This strategic pre-cooling successfully stabilizes the TEC's hot-side temperature, enabling the TEC to operate with a low operational temperature differential ∆T and resulting in an exceptionally high calculated Effective System Coefficient of Performance COP peaking at 14.5. The system maintains a stable operating point during peak solar radiation, maximizing the Net Electrical Power Gain. In conclusion, the integration of passive PV/T cooling, active TEC cooling, and a PID-enabled DC–DC Boost Converter provides an exceptionally efficient and stable solution for PV thermal management. The research strongly supports the efficacy of this hybrid system for significantly improving the overall energy efficiency and sustainability of solar energy applications.
Copyright (c) 2025 Priyo Adi Sesotyo, La Ode Muhamad Idris, Taufik Dwi Cahyono, Ery Sadewa https://creativecommons.org/licenses/by-sa/4.0/
Evaluation of a Filament-Winding Composite and Aluminium 6061 Frame for Electric Vehicles
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.
Copyright (c) 2025 Yunus Bakhtiar Arafat, Muhamad Fahmi As'ari, Rachmat Anggi Marvianto, Muhamad Hananuputra Setianto, Wahyu Sulistiyo https://creativecommons.org/licenses/by-sa/4.0/
Enhancing Power Factor Performance of Capacitor Bank Systems through Control Circuit Reconfiguration
This study investigates a practical method to enhance the efficiency of a campus electrical distribution system by optimizing the control and power circuitry of a capacitor bank panel. The research addresses the persistent issue of low power factor and phase imbalance resulting from non-standard wiring configurations in existing installations. Unlike conventional maintenance procedures, the proposed rewiring strategy systematically redesigns the control and power connections to ensure accurate capacitor switching and reactive power compensation in accordance with operational load variations. A diagnostic improvement evaluation framework was employed, involving pre- and post-rewiring measurements of power factor, load current balance, and reactive power under both normal and full-load conditions. The rewiring intervention increased the power factor from 0.97 to 0.99 during normal operation and from 0.70 to 0.95 under full-load simulation (1100 kVA). These improvements corresponded to a measurable reduction in reactive power demand and overall system losses, indicating a substantial gain in energy efficiency and voltage stability. The findings confirm that targeted control circuit reconfiguration can significantly enhance the operational reliability of capacitor bank systems beyond conventional maintenance practices. This work contributes a replicable, technically validated approach for improving power quality in educational and industrial electrical installations.
Copyright (c) 2026 Yuli Prasetyo, Budi Triyono, Dimas Nur Prakoso, Santi Triwijaya, Muhammad Marco Dwi Yoga https://creativecommons.org/licenses/by-sa/4.0/
Application of 3Å Synthetic Zeolite as an Adsorbent to Reduce the Hygroscopicity of B40 Biodiesel
This study investigates the effectiveness of synthetic 3Å zeolite and silica in reducing water content in B40 biodiesel, a high-FAME blend prone to moisture absorption. Adsorption tests were performed using varying adsorbent masses (1.5–7.5 g) and contact durations (60–180 minutes), with water content measured via Karl Fischer titration. Both materials significantly reduced moisture levels, with the highest reduction observed using 7.5 g of silica after 180 minutes. Multiple linear regression analysis confirmed that adsorbent mass and contact time had statistically significant effects (p < 0.001), while adsorbent type did not (p = 0.088), indicating that performance differences were not consistent across all conditions. These results refine the understanding of adsorbent behavior in biodiesel drying and support more informed selection based on operational priorities such as adsorption speed or reusability. This research contributes to the optimization of non-thermal moisture control strategies in biodiesel purification and highlights the need for future studies on adsorbent regeneration and scale-up feasibility.
Copyright (c) 2025 Sovia El Rafiqa, Eddy Ibrahim, Muhammad Rendana, M. Taufik Toha, Maulana Yusuf, Rahmat Wahyudi Putra https://creativecommons.org/licenses/by-sa/4.0/
A Hybrid Neural Network and Sugeno-Type Fuzzy Approach for Object Classification to Assist Navigation of Visually Impaired Individuals Using Ultrasonic Sensor Arrays
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.
Copyright (c) 2025 Ridwan Solihin, Rahmawati Hasanah, Budi Setiadi, Tata Supriyadi, Sudrajat Sudrajat, R Wahyu Tri Hartono https://creativecommons.org/licenses/by-sa/4.0/