Journal of Renewable Energy and Smart Device
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<![CDATA[Comparative Analysis of GRU and MLP Models for Extreme Rainfall Nowcasting Using AWS ]]>
<![CDATA[Hartanto]]>;
<![CDATA[Sajarwo Sanggai]]>;
<![CDATA[Taswanda Taryo]]>;
<![CDATA[Ananda, Naufal]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 3 No. 2 April 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v3i2.421
<![CDATA[Accurate forecasting of rainfall intensity is critical for hydrometeorological disaster mitigation in tropical regions like Indonesia. While high-resolution AWS data provides an opportunity to improve forecasting over conventional manual gauges, processing this volatile time-series data requires advanced computational models. This study comparatively evaluates predictive performance of static feed-forward MLP and sequential memory GRU deep learning architecture. Utilizing a three-year dataset (2022–2024) from three stations representing coastal, lowland, and mountainous topographies, the study reconstructed minute-aggregated AWS data using a sliding window algorithm. This successfully validated the digital sensors against manual Hellmann-type rain gauges, achieving a strong correlation (R > 0.80) for hourly accumulations. Both deep learning models were then trained using historical rainfall, temperature differences, and humidity differences. The empirical results demonstrate that the GRU model quantitatively outperforms the MLP, achieving a higher average classification accuracy of 96.49% (compared to 95.49%) and a lower RMSE of 1.51 mm (compared to 1.59 mm). The GRU’s gating mechanism proved significantly more robust in handling sharp data fluctuations across diverse terrains. However, the analysis revealed a shared structural limitation: both architectures severely underestimated extreme peak rainfall amplitudes. This anomaly stems from the inherent sparsity of extreme weather data and the mathematical conservatism induced by MSE loss function. Ultimately, while the GRU is highly recommended as a reliable frontline trigger for early warning systems, estimating absolute extreme rainfall magnitudes necessitates future exploration of non-standard loss functions and spatial data integration.]]>
<![CDATA[Deteksi Anomali Data Sensor Kelembaban Tanah Menggunakan Kalman Filter dan Aturan 3-Sigma ]]>
<![CDATA[Erniajan, Yunita]]>;
<![CDATA[Nirmala, Irma]]>;
<![CDATA[Hidayati, Rahmi]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 3 No. 2 April 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v3i2.426
<![CDATA[Monitoring kelembaban tanah berbasis Internet of Things (IoT) merupakan komponen penting dalam sistem pertanian presisi. Namun, sensor kelembaban tanah resistif seperti YL-69 kerap menghasilkan pembacaan yang tidak stabil akibat gangguan noise dan fluktuasi lingkungan, sehingga berpotensi menyebabkan kesalahan pada sistem pengambilan keputusan otomatis. Penelitian ini mengusulkan sistem deteksi anomali data sensor kelembaban tanah dengan mengintegrasikan algoritma Kalman Filter adaptif dan metode statistik aturan 3-sigma. Kalman Filter dengan nilai kovariansi noise proses (Q) yang bersifat dinamis diterapkan untuk mereduksi noise dan meningkatkan stabilitas pembacaan sensor, sementara deteksi anomali dilakukan berdasarkan rentang normal yang dihitung menggunakan standar deviasi kumulatif. Implementasi sistem menggunakan mikrokontroler NodeMCU ESP32 yang terhubung ke basis data MySQL dengan antarmuka berbasis website sebagai media visualisasi dan notifikasi. Pengujian dilakukan pada tiga kondisi kelembaban tanah, yaitu kering, lembab, dan basah, menggunakan total 101 sampel data yang di antaranya mencakup 23 injeksi data spike sebagai simulasi kegagalan sensor. Hasil pengujian menunjukkan bahwa Kalman Filter berhasil menurunkan koefisien variasi secara signifikan: dari 26,01% menjadi 6,00% pada kondisi kering, dari 36,31% menjadi 2,37% pada kondisi lembab, dan dari 57,82% menjadi 4,00% pada kondisi basah. Sistem juga berhasil mendeteksi seluruh data anomali yang diinjeksikan dengan akurasi 100%, disertai notifikasi pop-up secara real-time pada antarmuka website.]]>
<![CDATA[Design and Calibration of Water Quality Monitoring System Based on Internet of Things ]]>
<![CDATA[Basino]]>;
<![CDATA[Rafif Zainun]]>;
<![CDATA[Berbudi Wibowo]]>;
<![CDATA[Rahmad Surya Hadi Saputra]]>;
<![CDATA[I Ketut Daging]]>;
<![CDATA[Yusuf Syam]]>;
<![CDATA[Akhmad Syarifudin]]>;
<![CDATA[Ade Hermawan]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 3 No. 2 April 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v3i2.429
<![CDATA[Real-time water quality monitoring is crucial for modern aquaculture. However, low-cost Internet of Things (IoT) systems frequently struggle with analog sensor precision due to the limitations of internal microcontrollers. This study presents the design, calibration, and performance evaluation of a highly precise IoT-based water quality monitoring system. The hardware architecture utilizes NodeMCU ESP-32 microcontroller integrated with an external ADS1115 16-bit Analog-to-Digital Converter (ADC) module. This integration effectively mitigates signal noise and accurately processes analog inputs. The system continuously measures temperature using a DS18B20 sensor, alongside pH, Dissolved Oxygen (DO), and Total Dissolved Solids (TDS). To ensure industrial-grade reliability, rigorous sensor calibration was executed using linear regression and standard buffer solutions prior to deployment. A 14-day comparative field test was then conducted against calibrated commercial handheld instruments to validate the system's accuracy. The statistical evaluation demonstrated exceptional precision, yielding minimal average measurement errors of 0.08°C for temperature, 0.35 for pH, 0.24 mg/L for DO, and 6.80 ppm for TDS. Furthermore, linear regression analysis confirmed highly robust data correlations between the IoT sensors and the standard devices. The system achieved coefficient of determination ($R^2$) values of 0.9928 for the temperature sensor, 0.8906 for pH, 0.9962 for DO, and 0.7656 for TDS. These results mathematically confirm that integrating an external high-resolution ADC alongside comprehensive statistical calibration significantly enhances measurement stability. Ultimately, this approach successfully elevates the precision of low-cost IoT monitoring systems for aquaculture applications.]]>
<![CDATA[Distributed Solar Generation for Voltage Improvement and Loss Reduction in a 20 kV Network ]]>
<![CDATA[Nurdiansyah, Rian]]>;
<![CDATA[Faridah, Linda]]>;
<![CDATA[Muhammad Waliyyuddin Annur]]>;
<![CDATA[Sutisna]]>;
<![CDATA[Nurwijayanti Kn]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 3 No. 2 April 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v3i2.431
<![CDATA[Distributed photovoltaic (PV) generation has emerged as a key solution for enhancing the performance of medium-voltage (MV) distribution networks, particularly in isolated systems with limited conventional generation. This study investigates the impact of centralized and distributed PV integration on voltage profile and power losses in the 20 kV East Sumba distribution system. A steady-state load flow analysis using the Newton–Raphson method was performed in DIgSILENT PowerFactory under three PV penetration levels (20%, 50%, and 100% of peak load). The base-case condition shows significant undervoltage at remote buses and total active power losses of 474.47 kW, mainly due to long radial feeders and concentrated loading. The results indicate that centralized PV placement yields the strongest voltage recovery near the interconnection point at 100% penetration, whereas the minimum system power loss is achieved at 50% penetration. By contrast, distributed PV placement provides more uniform voltage support across remote buses, although with lower effectiveness in reducing total system losses. These findings reveal a clear operational trade-off between voltage improvement and loss minimization, implying that the preferred PV placement strategy should be selected according to the specific technical objective of the network. This study provides practical insights for planning and optimizing PV integration in weak and isolated MV distribution systems.]]>
<![CDATA[Design And Control Modelling of An IoT-Enabled Solar-Powered Ac Lighting System with Embedded Power Conversion ]]>
<![CDATA[Amar, Ishak]]>;
<![CDATA[Wawan Mariki, I Wayan]]>;
<![CDATA[Fahri Anwar]]>;
<![CDATA[Achmad Romadin]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 3 No. 2 April 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v3i2.534
<![CDATA[This study investigates the performance of a photovoltaic-based power conversion and wireless monitoring system designed for small-scale renewable energy applications. The proposed system integrates a DC–AC inverter, closed-loop voltage regulation, and wireless communication using an embedded controller to enable stable power delivery and real-time monitoring. Experimental testing was conducted under varying irradiance and load conditions to evaluate electrical performance, conversion efficiency, dynamic response, and communication behavior. The results show that output power increased proportionally with irradiance, reaching a maximum of 53.27 W at 1000 W/m². The inverter demonstrated stable conversion efficiency in the range of 86.40%–89.11%, with an average efficiency of 87.94%, indicating effective power conversion across different operating conditions. Dynamic response analysis revealed an overdamped behavior with no observable overshoot and voltage ripple below 5%, confirming stable voltage regulation and agreement with the proposed second-order transfer function model. In addition, the wireless monitoring subsystem operated reliably, with Wi-Fi providing lower response time than Bluetooth for real-time control and monitoring purposes. Overall, the experimental results confirm that the proposed system is capable of achieving stable electrical performance, efficient energy conversion, and reliable wireless communication, making it suitable for standalone solar lighting and other small-scale distributed renewable energy applications.]]>
<![CDATA[Validation of SpO₂ and Heart Rate Monitoring in a Smart Patient Bed System for Home Care ]]>
<![CDATA[Arianto, Eko]]>;
<![CDATA[Sulistyo, Dion]]>;
<![CDATA[Hidayat, Syarif]]>;
<![CDATA[Dwiatmojo, Sebastianus Christian]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 3 No. 2 April 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v3i2.581
<![CDATA[ Continuous monitoring of vital signs is essential in-patient care, especially for home care and hospital-at-home applications requiring long-term, non-diagnostic observation. Oxygen saturation (SpO₂) and heart rate are commonly monitored; however, conventional systems rely on standalone or wearable devices that may reduce patient comfort and compliance. Integrating monitoring into a smart patient bed system enables passive and continuous data acquisition without interfering with patient activity. This study aims to validate the performance of SpO₂ and heart rate monitoring integrated into a smart patient bed system for home care applications. The main contribution is the experimental validation of a smart patient bed system as a non-diagnostic monitoring solution, offering an alternative to wearable systems. Validation was performed by comparing measurements from the proposed system with a commercial pulse oximeter and a bedside patient monitor under resting conditions. Absolute error, mean absolute error, and standard deviation were calculated to evaluate accuracy and stability. Results show good agreement with both reference devices. The mean absolute error was below 1% for SpO₂ and below 1 bpm for heart rate, with low standard deviations indicating stable performance. In conclusion, the system provides a reliable solution for continuous vital sign monitoring in home care environments. This study also demonstrates the feasibility of integrating optical sensing into a smart patient bed system for long-term monitoring]]>
<![CDATA[Systematic Literature Review of SEP-Based Clustering Protocols in Wireless Sensor Networks: Taxonomy, Critical Analysis, and Adaptive Multi-Parameter Framework Proposal ]]>
<![CDATA[Purnamasari, Dian Neipa]]>;
<![CDATA[Pratama, Muhhammad Iyan Putra]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 3 No. 2 April 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v3i2.658
<![CDATA[Clustering protocols derived from the Stable Election Protocol (SEP) have been extensively studied for improving energy efficiency in heterogeneous Wireless Sensor Networks (WSNs). However, existing studies remain fragmented, with inconsistent evaluation settings, non-standardized performance metrics, and limited analysis of parameter sensitivity, making cross-study comparison unreliable. This paper presents a PRISMA-guided Systematic Literature Review (SLR) of 42 primary studies published between 2021 and 2026. The review classifies SEP-based approaches into four categories: energy-based (33%), distance-aware (24%), AI-based (21%), and hybrid (21%), and critically evaluates their methodological rigor. The analysis reveals three key research gaps: lack of evaluation standardization, insufficient exploration of heterogeneity parameters, and absence of realistic deployment scenarios. To address these issues, this study proposes ADSEP (Adaptive Dynamic SEP), a three-layer conceptual framework integrating energy normalization, spatial awareness, and adaptive routing strategies. Unlike prior work, ADSEP is positioned as a structured research direction with a defined validation protocol rather than a standalone protocol. This work contributes not only a taxonomy and critical synthesis of SEP-based protocols but also establishes a principled roadmap for future research in heterogeneous WSN clustering.]]>
<![CDATA[Prediction Of the Standardized Precipitation Index Drought Indicator: Case Study in Palembang, Indonesia ]]>
<![CDATA[Marzuki Sinambela]]>;
<![CDATA[Ahmad Fauzi Faishal Hadi]]>;
<![CDATA[Nizirwan Anwar]]>;
<![CDATA[Naufal Ananda]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 3 No. 2 April 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v3i2.666
<![CDATA[Assessing drought risk in equatorial urban hydrosystems presents a critical challenge for water resource management and climate adaptation. This study applies Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) deep learning models to forecast meteorological drought in Palembang, which is a low-lying, tidally-influenced metropolitan area in Indonesia, using a 30-year monthly rainfall dataset (1993–2024) from three monitoring stations. Models were developed to predict the Standardized Precipitation Index (SPI) across short-term (SPI-1), medium-term (SPI-3), and longer-term (SPI-6) timescales. While LSTM showed a marginal overall performance advantage over RNN, the study's principal finding is the significant spatial heterogeneity in drought risk across the three stations, a signal physically linked to localized land use, soil properties, and hydro-climatic conditions. Model predictability was highest for SPI-3, consistent with the region's dominant monsoon cycle, while lower SPI-6 performance highlights the limitations of a univariate approach in the presence of large-scale drivers such as the El Niño–Southern Oscillation (ENSO). Forecasts for 2025–2026 reveal three distinct risk profiles: a consistent drying trend at one station, increased rainfall volatility at another, and relative stability at the third. These findings demonstrate that effective drought management in complex equatorial regions requires localized, station-specific early warning systems rather than uniform city-wide approaches.]]>
<![CDATA[A Cyber-Physical Approach to Black Soldier Fly Cultivation: Integrating IoT Monitoring with Adaptive Watering Control ]]>
<![CDATA[Astutik, Rini Puji]]>;
<![CDATA[Mas'ud, Arbi Alfian]]>;
<![CDATA[Ramadhani, Muhammad]]>;
<![CDATA[Nur, Samsudin]]>;
<![CDATA[Zahara, Soffa]]>;
<![CDATA[Muslimin, Mohammad]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 3 No. 2 April 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v3i2.668
<![CDATA[This article details the creation and deployment of an IoT-integrated system designed for the activated watering and environmental supervision of Black Soldier Fly (BSF) maggot production. Given that maggot yield is heavily reliant on precise climate control, traditional manual techniques often fall short due to their labor-intensive nature and susceptibility to human error. The implemented solution utilizes an ESP32 microcontroller interfaced with an SHT20 sensor to monitor temperature and humidity, managing three key actuators: a heating element, a cooling fan, and a water pump. Communication is facilitated through the Telegram Bot API, offering both a manual override and an autonomous mode driven by a Mamdani fuzzy logic engine. Experimental results confirmed the system's high precision, with temperature and humidity sensing showing accuracies of 99.13% and 99.27%, respectively. Furthermore, evaluations of the fuzzy logic controller across 12 trials proved its capability to maintain optimal conditions without operational conflicts. This system offers a scalable approach to modernizing insect-based agriculture by stabilizing environmental parameters and reducing operational overhead]]>
<![CDATA[Solar-Powered IoT-Based Soil Temperature and Moisture Monitoring System for Chili Seedling Cultivation ]]>
<![CDATA[Bakri, Hasrul]]>
<![CDATA[ Journal of Renewable Energy and Smart Device Vol. 3 No. 2 April 2026 ]]>
Publisher : <![CDATA[PT. Global Research Collaboration ]]>
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DOI: 10.66314/joresd.v3i2.674
<![CDATA[Chili seedling cultivation requires optimal soil temperature and moisture, but conventional monitoring is inaccurate and lacks real-time capability, especially in off-grid areas. While IoT-based soil monitoring systems have been reported, most rely on grid electricity, lack mobile-based notifications, and are not specifically designed for chili seedlings. This study develops a solar-powered IoT system for real-time monitoring of soil temperature and moisture tailored for chili seedling cultivation. The system integrates an ESP32, DS18B20, soil moisture sensor, and a solar power subsystem (10W panel, 12V 7Ah battery), with Telegram-based alerts. Experimental results (n=10 repetitions per condition, n=15 users for usability) show the system achieves 98.22% accuracy (dry soil) and 95.74% (wet soil), with a mean soil moisture accuracy of 96%. Statistical analysis (paired t-test) reveals a significant difference between dry and wet conditions (p<0.05). System quality based on ISO 25010 shows functionality and portability at 100%, usability at 4.75/5 (very good). The system operates autonomously for 24 hours on solar power, making it suitable for remote agriculture. This study contributes a validated, off-grid, real-time monitoring system with statistical evidence, supporting precision agriculture for chili seedlings.]]>
