Journal of Renewable Energy and Smart Device
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<![CDATA[Development of a Web-Based Document Archiving System for Local Government Using Laravel: A Software Engineering Approach ]]>
<![CDATA[Abdullah Ardi]]>;
<![CDATA[Adina Apriyani]]>;
<![CDATA[Memed Saputra]]>;
<![CDATA[Mega Wahyu Rhamadani]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v4i1.611
<![CDATA[Most local government digitization efforts fail to address workflow inefficiencies and lack structured governance mechanisms, limiting their operational impact. This study presents a governance-oriented web-based document archiving system built with Laravel MVC and Agile (Scrum), implemented at BKAD Kabupaten Kapuas. The key novelty lies in integrating role-based access control (RBAC), audit trails, and structured metadata management as core architectural elements systematically restructuring document workflows rather than merely digitizing existing processes. Evaluation through functional testing, performance comparison, and user satisfaction analysis confirmed substantial operational improvements, including dramatic reductions in retrieval time and error rates, alongside near-perfect data completeness. User satisfaction consistently exceeded 4.3/5.0 across all dimensions, validating system usability across varying levels of digital literacy. The modular open-source architecture provides a replicable model for similar institutions, with direct implications for scalable and accountable e-government transformation.]]>
<![CDATA[An Intelligent Case Based Reasoning Approach for Diagnosing Fish Quality Decline Using IoT ]]>
<![CDATA[Charis Fathul Hadi]]>;
<![CDATA[Dewi Mutamimah]]>;
<![CDATA[Firman Hidayat]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v4i1.731
<![CDATA[Indonesia ranks as one of the world’s largest fish producers, with an annual production of 6.54 million tons. Among the most economically valuable marine commodities is scad fish (Decapterus spp.). However, fish quality deterioration remains a major challenge due to environmental conditions, post-harvest handling, and inadequate storage methods. This study integrates Internet of Things (IoT) technology and Case-Based Reasoning (CBR) to diagnose fish quality deterioration in real time. Data were collected over a 24-hour period using IoT sensors that monitored temperature, humidity, pH, and ammonia levels. The CBR algorithm compared sensor data with historical cases through similarity measurements to provide diagnostic outcomes. Results indicate that between 07:00 and 09:00, fish maintained freshness (pre-rigor stage) with a similarity of 96.66%. However, quality decreased significantly from 12:00 to 06:00, reaching a similarity of 71.06% at 06:00. Findings highlight that temperature and ammonia levels are key factors driving spoilage, while humidity accelerates microbial growth and pH variation reflects rigor and post-rigor phases. The proposed system achieved a diagnostic accuracy of 96.47% compared to expert evaluations. These results demonstrate the system’s reliability in monitoring fish quality and its potential application in seafood supply chains. By minimizing spoilage, the system provides both economic and societal benefits through improved food safety and distribution efficiency. This study contributes a practical and scalable approach for intelligent food monitoring systems.]]>
<![CDATA[Design and Implementation of a Multivariable Decoupling Control System for HPAL Autoclave with Web-Based SCADA Interface ]]>
<![CDATA[Afandy Moh]]>;
<![CDATA[Yulianti Malik]]>;
<![CDATA[Abdul Haris Mubarak]]>;
<![CDATA[Muhammad Ikbal Rianto]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v4i1.856
<![CDATA[This study presents the design and experimental evaluation of a laboratory-scale High-Pressure Acid Leaching (HPAL) autoclave integrating a Siemens S7-1200 programmable logic controller (PLC), Modbus RTU industrial communication, an Internet of Things (IoT) gateway, and a cloud-based Web-SCADA platform for real-time monitoring and process supervision. The platform was developed to provide a safe laboratory-scale environment for hydrometallurgical process experimentation and industrial automation education under a lower-severity operating envelope than industrial HPAL systems. Experimental validation was conducted using limonite laterite nickel ore with nominal sulfuric acid concentrations of 4 wt.% and 5 wt.% in single experimental runs at a reactor temperature setpoint of 150°C. The implemented closed-loop ON–OFF controller with hysteresis maintained the reactor temperature near the setpoint after a heating period of approximately 1.48 h, while the stirring system operated within 188–192 rpm and the reactor pressure remained stable at approximately 3.2–3.4 bar throughout the holding period. Continuous data acquisition and visualization through the Web-SCADA platform were achieved without communication interruption between the PLC, IoT gateway, and cloud server during the experiments. The leaching process produced liquid filtrate and solid residue, with measured solution volume reductions of 15% for the nominal 4 wt.% sulfuric acid experiment and 12% for the nominal 5 wt.% sulfuric acid experiment. These observations represent process validation only, as comprehensive metallurgical evaluation, including nickel recovery and extraction efficiency, was beyond the scope of this study. The results demonstrate the feasibility of integrating industrial automation, IoT communication, and cloud-based monitoring into a laboratory-scale HPAL platform for process control, data acquisition, and engineering education.]]>
<![CDATA[A Cluster-Informed Robust Prioritization Model For De-Dieselization Screening Of Isolated Diesel Power Plants ]]>
<![CDATA[Syafrullah]]>;
<![CDATA[Rinaldy Dalimi]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v4i1.948
<![CDATA[De-dieselization of isolated diesel power plants is not only a generation replacement problem but also an early-stage portfolio screening problem, because candidate locations differ in operating scale, generation cost, fuel efficiency, asset condition, reserve adequacy, operating intensity, and fuel logistics pressure. Existing prioritization approaches may identify ranked alternatives, but they often provide limited explanation of location heterogeneity and limited evidence on shortlist stability under expert-weight uncertainty. This study develops a cluster-informed robust prioritization model for early-stage de-dieselization screening by integrating PCA-K-Means, AHP-TOPSIS, and Monte Carlo robustness analysis. The model was applied to 190 isolated diesel power plant locations. PCA-K-Means identified five technical-economic typologies, while AHP-TOPSIS produced the baseline ranking using six prioritization criteria. Monte Carlo simulation evaluated ranking stability under ±20% AHP weight perturbation across 5,000 iterations. Fuel logistics cost, reserve margin, and generation cost became the most influential criteria. The global ranking was stable, with a median Spearman correlation of 0.997 and a P5-P95 range of 0.978-0.999. Eight locations formed a robust Top-10 core, eight formed a robust Top-20 extension, and several candidates were classified as weight-sensitive. The model helps planners distinguish immediate priority candidates, extended screening candidates, and locations requiring additional verification before detailed feasibility studies.]]>
<![CDATA[Development of a Microwave-Driven In-Liquid Plasma Reactor for Nickel Mobilization from Saprolite: A Proof-of-Concept Study ]]>
<![CDATA[Pria Gautama]]>;
<![CDATA[Shinfuku Nomura]]>;
<![CDATA[Vilia Darma Paramita]]>;
<![CDATA[Nurfiansyah Nurfiansyah]]>;
<![CDATA[A. Ariputra]]>;
<![CDATA[Arfandy Arfandy]]>;
<![CDATA[Muh. Firdan Nurdin]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v4i1.992
<![CDATA[This proof-of-concept study evaluates nickel mobilization from saprolite using a water-only, 60 s treatment and a low-cost domestic-grade microwave platform. A domestic 2.45 GHz microwave oven was modified to generate in-liquid plasma at 450 W in 150 mL distilled water (aquadest). Saprolite (<30 mesh, 10 g) was treated for 60 s, reaching a measured liquid temperature of 56 °C. The aqueous extract was analysed by atomic absorption spectroscopy (AAS), and the recovered solid was characterized by X-ray fluorescence (XRF). AAS detected 20.789 mg/L Ni in the plasma-treated extract. XRF showed that the normalized NiO mass fraction decreased from 1.6569 mass% in untreated saprolite to 1.4397 mass% after treatment, equivalent to a 13.1% relative compositional decrease; this value is not an extraction-efficiency figure. Fe₂O₃ remained nearly unchanged. The liquid- and solid-phase observations are consistent in direction and support nickel mobilization under the tested conditions. However, their implied magnitudes differ, and the absence of dry residue mass and complete liquid-recovery data prevents mass-balance closure and calculation of extraction efficiency.]]>
<![CDATA[Domain Gap and Context Bias Analysis in YOLOv11-Based Housing Condition Detection through Indoor–Outdoor Cross-Domain Evaluation ]]>
<![CDATA[Farendi Giotivano]]>;
<![CDATA[Wiyli Yustanti]]>;
<![CDATA[Ricky Eka]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v4i1.998
<![CDATA[Computer vision provides a promising solution to evaluate housing conditions. However, the differences between indoor and outdoor visual environments may cause domain gaps that affect the generalization ability of the model. This study explores the effect of domain gaps on the detection of housing physical conditions using You Only Look Once version 11 (YOLOv11) by proposing an integrated evaluation framework that combines cross-domain evaluation, EigenCAM-based model interpretability, and illumination variation analysis. A total of 2,712 housing images were classified into Indoor, Outdoor and Mixed datasets and evaluated under uniform experimental conditions. From the results, the Indoor model obtained a 42.77% relative mAP50 drop when evaluated on the Outdoor dataset, thus proving the existence of a significant domain gap. The Mixed model achieved the most consistent performance across all evaluation scenarios, although this improvement may also be partially influenced by the larger training dataset used in the Mixed configuration. EigenCAM analysis showed that single-domain models were more reliant on contextual visual cues while the Mixed model was consistently more attuned to relevant housing elements. The illumination analysis showed that the differences in brightness between the domains were not large, suggesting that context bias is more likely than illumination variation to explain the observed performance degradation. These results demonstrate that the combination of cross-domain evaluation with model interpretability provides a more complete understanding of domain gap effects and enables the creation of more robust computer vision models for housing condition assessment.]]>
<![CDATA[a Design of an IoT-Based Control and Monitoring System for a Biogas Power Generation Plant Using POME Waste from Palm Oil Mills: a Design of an IoT-Based Control and Monitoring System for a Biogas Power Generation Plant Using POME Waste from Palm Oil Mills ]]>
<![CDATA[Asminar Asminar]]>;
<![CDATA[Rifki Rifki]]>;
<![CDATA[Akhyar Muchtar]]>;
<![CDATA[Abdul Djohar]]>;
<![CDATA[Agustinus Lolok]]>;
<![CDATA[Mustarum Musaruddin]]>;
<![CDATA[Hasmina Tari Mokui]]>;
<![CDATA[Adhi Setiawan Samsul]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v4i1.1054
<![CDATA[Palm Oil Mill Effluent (POME) contains a high organic load that can be converted into biogas through anaerobic digestion and subsequently utilized for electricity generation. However, maintaining stable digester conditions requires an integrated monitoring and control system capable of observing critical operating parameters. This study presents a conceptual design and theoretical assessment of an Internet of Things (IoT)-based monitoring and control system for a POME-based biogas power plant. The proposed architecture integrates an anaerobic digester, gas holder, biogas generator, ESP32 microcontroller, temperature, pressure, methane concentration, and gas flow meter, cloud communication through MQTT/HTTP protocols, and web- and mobile-based dashboards. A threshold-based control strategy is proposed to regulate gas pressure and digester temperature. This study did not include physical system implementation or experimental field testing. Based on the adopted design assumptions, processing 300 metric tons of fresh fruit bunches generates approximately 240 m³ of POME and 6,000 m³ of biogas. Using a methane fraction of 60%, a methane calorific value of 35.7 MJ/m³, and a fixed generator efficiency of 35%, the theoretical electrical energy output is estimated at 12,495 kWh per production cycle. The proposed design provides an integrative framework for future prototype development and experimental validation of IoT-based monitoring and control in POME biogas power plants.]]>
<![CDATA[Techno-Economic Evaluation of ACCC Reconductoring and Dynamic Line Rating for 150-kV Transmission Networks Using Validated Multi-Objective Optimization ]]>
<![CDATA[Muhammad Shalahuddin Suteja]]>;
<![CDATA[Faiz Husnayain]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v4i1.1063
<![CDATA[Techno-economic evaluation of ACCC reconductoring and dynamic line rating on the 150 kV Java-Bali transmission network requires a validated multi-objective optimization framework capable of quantifying thermal, economic, and environmental benefits under real meteorological conditions. This study develops such a framework using a calibrated IEEE 118-Bus benchmark representing Indonesian grid characteristics, applying the IEEE Standard 738 thermal model to 52,608 hours of NASA POWER meteorological data across five Java-Bali cities from 2020 to 2025. A surrogate machine learning model based on Histogram-based Gradient Boosting (HistGradientBoosting) trained on 121,764 valid load flow solutions achieved R² = 0.9995 (on a held-out test set of 30,441 samples) for power loss prediction with a mean absolute error (MAE) of 4.49 MW (0.20% of the observed loss range), and 98.0% accuracy within ±1 line for thermal violation prediction, enabling Non-dominated Sorting Genetic Algorithm-II (NSGA-II) multi-objective optimization with 210× computational speedup. All Pareto-optimal solutions were re-validated using full Newton-Raphson load flow, confirming the validated nature of the optimal configurations. The IEEE 738 model achieved less than 0.07% deviation from CTC Global manufacturer data at all tested temperatures. The dynamic ampacity of existing conductors ranged from 730 A to 1,458 A, revealing 43% underutilization of median capacity under static rating. Thermal violations begin in 2028–2029 under RUPTL 2025–2034 load growth and reach 52,775 line-hours by 2034; full reconductoring eliminates over 99% of violations. Financial evaluation over 40 years at 9.5% WACC yields NPV of IDR 2,218 billion, IRR of 17.3%, BCR of 2.09, and discounted payback of 11.79 years. Selective reconductoring of 26 critical lines delivers IRR up to 109.6% and BCR up to 11.23. Sensitivity analysis confirms NPV positivity across all ±20% parameter variations and combined adverse scenarios.]]>
<![CDATA[Sentiment Analysis of X Users’ Opinions on the Free Nutritious Meal (MBG) Program Using Support Vector Machine ]]>
<![CDATA[Syarifah Putri Agustini Alkadri]]>;
<![CDATA[Rhendy Billnadzary Al Abrari]]>;
<![CDATA[Rachmat Wahid Saleh Insani]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v4i1.1094
<![CDATA[Sentiment analysis was conducted to examine public perceptions of the Free Nutritious Meal (Makan Bergizi Gratis, MBG) Program and identify prevailing opinion trends surrounding the policy. The analysis was initially conducted on 5,118 social media posts collected from X (formerly Twitter). After the preprocessing stage, which involved removing duplicate records, missing values, and irrelevant textual elements, a total of 4,332 posts remained for analysis. The resulting dataset was inherently subjective, as it comprised individual opinions reflecting diverse perspectives and styles of expression. Sentiment labeling was subsequently performed using a hybrid approach that combined lexicon-based labeling with manual annotation. The dataset comprised 50.42% positive, 38.55% negative, and 11.03% neutral sentiments. Among various text classification techniques, Support Vector Machine (SVM) was employed to classify sentiment in social media posts. The proposed framework comprised several sequential stages, including data collection, text preprocessing, hybrid sentiment labeling, word cloud visualization, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), model development, and performance evaluation. The classifier was trained and evaluated using three train-test split ratios (80:20, 70:30, and 60:40), followed by stratified 10-fold cross-validation to obtain a more robust performance assessment. The cross-validation results showed that all three data partitioning strategies achieved comparable performance. The 70:30 split produced the highest mean accuracy of 80.08 ± 1.38%, together with a weighted precision of 77.39%, a weighted recall of 80.08%, and a weighted F1-score of 76.19%.]]>
<![CDATA[Spatial and Temporal Assessment of Water Quality in the Bektiharjo River, Tuban Regency, Using the Water Quality Index and GIS-Based QUAL2Kw Modeling (2021-2025) ]]>
<![CDATA[Fadhillah Zahrotul Jannah]]>;
<![CDATA[Maritha Nilam Kusuma]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v4i1.1108
<![CDATA[The Bektiharjo River is an important water resource supporting domestic, agricultural, tourism, and ecological activities in Tuban Regency, Indonesia. Increasing anthropogenic activities have raised concerns regarding water quality degradation. This study aimed to evaluate the spatial and temporal characteristics of the Bektiharjo River during 2021-2025 by integrating the Water Quality Index (WQI), Geographic Information Systems (GIS), and the QUAL2Kw model. Secondary water quality data, including pH, TSS, DO, BOD, COD, nitrate, phosphate, and fecal coliform, were analyzed using WQI, spatial mapping, and water quality simulation under existing and critical flow conditions. The results showed that water quality declined from upstream to downstream, with average WQI values of 54-56 in the upstream segment, 48-52 in the middle segment, and 48-50 in the downstream segment, showing moderate to poor water quality. Seasonal differences were relatively small, with WQI varying by only 2-4 points across river segments. Water quality was generally higher during the rainy season in the upstream and middle reaches, whereas the downstream reach showed a slightly higher WQI during the dry season. QUAL2Kw calibration produced relative errors below 5%, showing good agreement between simulated and observed values during model calibration; however, these results represent calibration performance rather than independent model validation. Under existing conditions, DO decreased from 6.60 to 6.16 mg/L, while BOD increased from 0.89 to 2.50 mg/L from upstream to downstream. Under critical low-flow conditions, DO declined further to 4.12 mg/L, whereas BOD increased to 3.74 mg/L, showing reduced self-purification capacity. The findings show that management efforts should prioritize upstream watershed conservation, environmentally friendly agricultural practices in the middle reach, and improved domestic wastewater management in the downstream reach to support sustainable river management.]]>
