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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. International Journal of Engineering Continuity 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. International Journal of Engineering Continuity 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. International Journal of Engineering Continuity is published biannually, in March and September, by Sultan Publisher
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 89 Documents
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Integrating Demand Forecasting, Aggregate Planning, and Sensitivity Analysis for Cost-Efficient Production: A Case Study in Furniture Manufacturing Ardhy Lazuardy; Syehan Syehan; Arief Nurdini
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.v5i1.571

Abstract

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.
Innovation in an IoT-Based Smart Biogas Reactor Prototype for Converting Household Organic Waste into Alternative Energy Zulfahmi Noor; Nurmasitya Kemalaintan; Marvel Dwi Gulan Silindang; Anisa Suliyanti
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.v5i1.572

Abstract

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.
Predicting Employee Intent-to-Stay from Engagement Survey Data: An Interpretable, Class Imbalanced Machine Learning Case Study Bagus Satrio Diharjo; Ira Puspitasari; Agustinus Titis Iswara
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.v5i1.583

Abstract

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.
Experimental Investigation of a PV-Powered Blower-Assisted Solar Still: Effects of Seawater Volume on Evaporation–Condensation Efficiency Zaenal Ikhsan Abdullah; Ridwan Ridwan; Iwan Setyawan; Hamzah Ali Nashirudin; Deni Haryadi
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.v5i1.554

Abstract

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.
Chemical Fingerprint Assessment of Surface Resistance in Silicone Rubber Insulators Using Coal Fly Ash as Filler Harjib Haridh; Syamsir Abduh; Christiono Christiono
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.v5i1.578

Abstract

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.
Comparative Evaluation of Artificial Neural Networks and Monte Carlo Simulation for Transformer Insulating Oil Lifetime Prediction Irnanda Priyadi; Yuli Rodiah; Makmun Reza Razali; Shara Alya Gifani Muhyisunah
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.v5i1.604

Abstract

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.
Predictive Modeling of Nonlinear Breakdown Voltage in Silicone Rubber Polymer Insulators with Fly Ash Filler Using the GLME and BPNN Methods Davina Salmah An’nafri; Christiono Christiono; Miftahul Fikri; Nurmiati Pasra; Andi Dyah Harum
International Journal of Engineering Continuity Vol. 5 No. 2 (2026): IJEC
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v5i2.607

Abstract

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.
Interpretable machine learning for prediction and inverse design of electrical resistivity multicomponent aluminium alloys Deni Haryadi; Aji Abdillah Kharisma; Hamzah Ali Nashirudin; Haris Rudianto
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.v5i1.632

Abstract

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.
Field-Validated Triplen Harmonic and Neutral Current Mitigation Using an Active Harmonic Filter Zakky Daniel Haq; Kuntjoro Pinardi; Abdul Multi
International Journal of Engineering Continuity Vol. 5 No. 2 (2026): IJEC
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v5i2.626

Abstract

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.

Page 9 of 9 | Total Record : 89

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