Journal Title Jurnal Teknik Elektro Abbreviation JTE Published Frequency 2 issues per year (June and December) DOI 10.15294/jte Print & Online ISSN 1411-0059 | 2549-1571 Business Model Open Access & Author Pays Editor in Chief Rizky Ajie Aprilianto Publisher LPPM Universitas Negeri Semarang Citation Analysis/Indexing Scopus | SINTA | DOAJ | Dimensions | Garuda Metrics Author Diversity | Statistics Jurnal Teknik Elektro starting in 2024 migrates to better secure from various unwanted things, including journal hacking and so on. To submit, the author please visit the new website page of our journal at the link https://journal.unnes.ac.id/journals/jte MIGRATION OFFICIAL STATEMENT HERE Jurnal Teknik Elektro (JTE) is an open-access, peer-reviewed journal published by the Department of Electrical Engineering, Universitas Negeri Semarang, Indonesia. JTE is published with a frequency of 2 (two) times a year, and the volume varies [p-ISSN 1411-0059 | e-ISSN 2549-1571]. Articles can be the results of research, scientific studies, and analysing and solving issues relevant to electrical engineering and computer science. It was first published in 2009. In 2019, JTE was accredited (SINTA 2) by the Directorate General of Research, Empowering and Development, Ministry of Research, Technology and Higher Education through SK 36/E/KPT/2019 for 2019-2023. Now, JTE is still accredited SINTA 2 through SK 10/C/C3/DT.05.00/2025 for 2024-2028.
A Two-Stage High-Gain Quadratic Boost Converter Using Inverse-Coupled-Inductors for Low Input-Current Ripple
High step-up DC–DC converters are required to interface low-voltage renewable energy sources with higher-voltage dc buses in dc microgrid applications. This paper presents a simulation-based two-stage high-gain DC–DC converter integrating an inverse-coupled-inductor front end with a quadratic boost rear stage. The inverse-coupled front end is employed to reduce source-side input-current ripple and mitigate dc-flux accumulation, thereby lowering the risk of magnetic-core saturation in the front-stage inductors under high-power operation. Compared with uncoupled and direct-coupled configurations, the inverse-coupled arrangement enables ripple-current cancellation between the two input phases, which improves its feasibility for higher-power implementation. The quadratic boost rear stage provides high voltage gain and supports a more stable dc-bus voltage response. Analytical derivations of the operating principle, voltage gain, and input-current ripple ratio are presented, followed by comparative simulation of the three inductor configurations. The results show that the inverse-coupled configuration achieves the lowest average input-current ripple ratio of , with a minimum value of at = 0,5. The converter also produces an output voltage close to 400 V from a 20 V input, corresponding to a voltage gain of approximately 20. These results indicate that the proposed topology offers a promising trade-off between high voltage gain, reduced front-stage magnetic stress, source-side ripple suppression, and output-voltage stability for renewable-energy-fed dc-bus applications.
Copyright (c) 2026 Jurnal Teknik Elektro
Bandwidth Enhancement of Proximity-Coupled Patch Antenna Using Dual Open-Ended Ring-Shaped Slots for Sub-6 GHz Applications
This paper presents a bandwidth enhancement of patch antenna intended for sub-6 GHz applications. A square-shaped radiating patch is configured on a double-layer FR-4 dielectric substrates with a dielectric constant of 4.3 and a total thickness of 3.2 mm. To improve the impedance bandwidth, the antenna is fed using proximity-coupling method, in which the microstrip feeding line and the ground plane are placed on the top and bottom layers of the second substrate. Dual open-ended ring-shaped slots are subsequently introduced and etched on the radiating patch element to further enhance the antenna bandwidth. Various slot structures are evaluated, and their physical parameters are varied in a parametric study to analyze their effect on antenna performance, particularly impedance bandwidth. The proposed antenna has a compact dimension of 0.38λ0×0.38λ0×0.06λ0 and exhibits wideband characteristics with a fractional bandwidth of 22.3%.
Arduino-Based ECU Simulator and OBD-II Scanner for Electric Vehicle Diagnostics with CAN BUS
Electric vehicles (EVs) require reliable and accurate diagnostic systems to ensure the performance, safety, and efficiency of their electrical and electronic components. This study presents the design and development of an Arduino-based Electronic Control Unit (ECU) simulator and an On-Board Diagnostics II (OBD-II) scanner for electric vehicle diagnostics, both using the Controller Area Network (CAN) communication protocol. The ECU simulator is built using an Arduino Mega 2560 and an MCP2515 CAN controller, equipped with potentiometers and push buttons to simulate various EV operating parameters such as motor speed, vehicle speed, battery voltage, temperature, state of charge, and regenerative braking conditions, as well as to generate Diagnostic Trouble Codes (DTCs). The OBD-II scanner is developed using an Arduino Uno, MCP2515 module, and a Nextion HMI LCD to support live data monitoring, DTC reading, and DTC clearing in accordance with SAE J1979 standards. System performance was evaluated using a CAN analyzer to verify frame structure, arbitration behaviors, data integrity, and communication reliability. The experimental results show that all transmitted CAN frames conform to the ISO 11898 standard and are correctly interpreted by the OBD-II scanner. The system achieved data accuracy of approximately 97% with an average response time of ±100 ms, indicating stable, reliable communication. The developed platform provides a low-cost, flexible, and effective solution for electric vehicle diagnostics, education, and laboratory testing without requiring real vehicles. It can serve as a foundation for future research involving multi-ECU communication and additional OBD protocols.
Automatically Retrained Machine Learning System for Rice Yield Prediction Using Open-Meteo and BPS Data
This study proposes a machine learning–based rice yield prediction system with a self-updating mechanism, using Gorontalo Province, Indonesia, as a case study. The system integrates daily climate data from Open-Meteo with agricultural statistics from the Central Bureau of Statistics (BPS) to support data-driven decision-making in agriculture. A key challenge addressed in this study is the limited availability of yield data, which are provided only at an annual scale for the period 2018–2024, without seasonal labels. To overcome this limitation, a temporal disaggregation approach is adopted to construct initial seasonal yield labels (M1, M2, M3). These constructed labels serve as approximations, enabling the development of a seasonal prediction model under data-constrained conditions. Several machine learning algorithms, namely Gradient Boosting, Random Forest, XGBoost, Ridge Regression, and Linear Regression, are evaluated using Leave-One-Out Cross-Validation (LOO-CV). Model performance is assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results indicate that ensemble-based models outperform linear baselines, with Gradient Boosting providing the best balance between prediction accuracy and model stability. The main contribution of this study is the design of an adaptive prediction system with a self-updating mechanism that supports periodic retraining and dynamic model evaluation. At its current stage, this mechanism is positioned as an initial framework rather than a fully validated continuous learning system. The proposed system is implemented as a web-based platform that supports yield prediction and planting season recommendations, providing a scalable foundation for intelligent agricultural systems in data-limited environments.
Leakage-Aware Fault Detection in Three-Phase Power Systems Using an Artificial Neural Network and Scenario-Based Data Splitting
The reliability of three-phase power systems depends heavily on the ability to detect faults quickly and accurately. However, much previous research has faced challenges such as data leakage, biased evaluations due to random data splitting, and limitations in the utilisation of system dynamics-based features. This study aims to develop a machine learning-based fault detection model that is accurate, robust, and possesses high generalisation capabilities. The method employed is a quantitative approach using an Artificial Neural Network (ANN) model as a binary classifier (normal and fault). The research process includes data preprocessing, elimination of features that could potentially cause data leakage, feature engineering based on current, voltage, energy, and system imbalance, as well as scenario-based data splitting to ensure the validity of the evaluation. The model was trained using repeated cross-validation and optimised based on the Area Under the Curve (AUC) metric. The results show that the ANN model achieved high performance with an AUC of 0.987 and an accuracy of approximately 98.5% and remained stable under varying conditions such as noise and load changes. Compared to other models, Random Forest delivered the best performance, followed by Support Vector Machine (SVM) and ANN. This study addresses the gap regarding more realistic evaluation methods that are free from data leakage. Its novelty lies in the use of scenario-based splitting, comprehensive evaluation, and the finding that raw signal features make a dominant contribution to fault detection.
Predictive Modeling of Cassava Bioethanol Production Using Random Forest
Indonesia is accelerating its energy transition to reduce reliance on fossil fuels and achieve a 23% renewable energy mix by 2025. Bioethanol has emerged as a promising renewable alternative, particularly under the current E5 (5% ethanol blend) program and the roadmap toward higher integration. This study presents a Machine Learning (ML) framework for predicting cassava-based bioethanol production in West Java, applying Random Forest (RF) modeling to estimate cassava yields and a conversion process to calculate ethanol output. The dataset comprises 3,000 monthly observations from 27 districts (2013–2024), integrating agronomic, climatic, and economic variables such as harvested area, rainfall, temperature, and farm-gate price. Model validation using blocked time-series Cross-Validation (CV) demonstrated high predictive accuracy ( ; Root Mean Square Error (RMSE) = 191 kL; Mean Absolute Error (MAE) ≈ 40 kL), confirming the effectiveness of ensemble learning in capturing nonlinear agricultural dynamics. Feature importance analysis identified harvested area and rainfall as the most influential predictors, underscoring the relevance of climate-adaptive farming and sustainable land-use strategies. Spatial evaluation highlighted Garut, Sukabumi, and Tasikmalaya as priority districts. Aggregated projections, recalculated with verified conversion factors (=5.86 megawatt-hour per kiloliter, MWh/kL), indicate an energy potential of 820,000 MWh/year and substantial Carbon Dioxide (CO₂) reduction under E5 blending. Despite limitations related to data inconsistencies in productivity units and fluctuating price records, the findings demonstrate that cassava-based bioethanol can serve as a viable pathway for renewable energy diversification, provided that agricultural productivity, infrastructure, and policy alignment are strengthened.
Performance Evaluation of Propagation Model in DVB-T2 Broadcasting: Case Study in Kebumen
The widespread change from analog to digital television broadcasting in Indonesia has led to the emergence of blank spots in some areas. To overcome this problem, an optimal propagation model is needed to improve transmitter coverage, energy efficiency, and field strength through optimal transmit power for DVB-T2 broadcasts. This research addresses the selection and analysis of an appropriate propagation model, focusing on the application of the Longley-Rice and ITU-R.P 1546-6 models in the Kebumen region of Central Java, Indonesia. This study specifically compares the two models to determine the most effective model in reducing blank spots in the area. The results show that the Longley-Rice model produces greater field strength than the ITU-R.P 1546-6 model. In addition, this study also found that the farther the receiver is from the transmitter, the higher the free space loss value. This is because due to the hilly geographical conditions in the Kebumen area, it causes high loss. And the farther the receiver is from the transmitter and the greater the frequency value, the greater the field strength value. This research not only provides relevant empirical data for the Kebumen region but also offers insights that can be used in the optimization of digital broadcasting in other urban environments in the future.
Performance Evaluation of NARX-CG Model for Electricity Forecasting: Bali Blackout Case Study
Bali experienced a widespread blackout in May 2025 that disrupted economic and social activities across the island, revealing weaknesses in electricity demand forecasting and system resilience. This study evaluates the performance of a Hybrid Nonlinear Autoregressive with Exogenous Inputs-Conjugate Gradient (NARX-CG) model as an advanced electricity forecast. The dataset covers the 2018-2023 period and includes six variables: electricity energy, connected capacity, number of customers, tariffs, Gross Regional Domestic Product (GRDP), and population, aligned with the national electricity planning framework. The NARX-CG model was developed using a 6-12-6-1 network architecture and trained with tansig transfer function. Forecasting performance was evaluated using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) metrics. Results show that the NARX-CG model achieved an MSE of 0.09853 and an average MAPE of 8.12%, outperforming conventional projections with a MAPE of 28.48%. Yearly evaluations show consistent model stability, with the lowest MAPE values of 1.93% and 5.86% in 2023 and 2022, respectively. The NARX-CG model effectively captures nonlinear temporal dependencies, enhances predictive accuracy, and contributes to improved power system reliability and resilience, providing valuable insights for adaptive energy planning following the 2025 Bali blackout.
Copyright (c) 2025 Jurnal Teknik Elektro
Conventional electromyography (EMG) systems in clinical settings present limitations including high examination costs (500,000-1,500,000 IDR), bulky equipment, and lack of remote monitoring capabilities. This study develops and validates a portable Internet of Things (IoT)-based EMG system utilizing Message Queuing Telemetry Transport (MQTT) protocol for wireless muscle activity monitoring. The system integrates an EMG V3 sensor module with NodeMCU ESP8266 microcontroller, powered by dual 18650 batteries. Implementation utilized Arduino IDE and IoT MQTT Panel application. Validation comprised functional suitability testing (ISO/IEC 25010:2012), signal characteristics analysis across five subjects with 10 trials each, and repeatability precision evaluation (ISO 17025:2017). The system demonstrated 100% functional suitability. EMG signal characteristics showed average peak-to-peak voltage of 8.95±0.62 mV during relaxation and 10.48±0.58 mV during contraction, with Root Mean Square (RMS) voltage of 1.38±0.11 mV and 1.94±0.21 mV, respectively. Signal frequency-maintained consistency at approximately 50 Hz. Overall repeatability precision achieved 97.62%. This portable EMG system addresses conventional system limitations through wireless connectivity and reduced cost while maintaining measurement accuracy suitable for muscle health monitoring applications.
Message Queuing Telemetry Transport (MQTT)
Wideband Multi-Resonant Microstrip Patch Antenna for Wireless Communication Systems
The increasing demand for high-performance wireless communication systems has driven the development of compact antennas that can support wideband operation, particularly in the 5 GHz frequency range. Microstrip patch antennas (MPAs) remain attractive for such applications due to their low profile and ease of integration, although achieving a wide impedance bandwidth remains a key challenge. This paper presents a low-profile MPA with a wideband response designed for the 5 GHz band of wireless communication systems. The proposed MPA is implemented on a single-layer FR-4 substrate with a thickness of 1.6 mm and is excited using a coaxial probe feed. To achieve a wide impedance bandwidth, a U-shaped patch is utilized as the primary radiator, complemented by a slot, a parasitic element, and a rectangular ring surrounding the main resonator, aiming to generate multiple resonant modes and improve impedance matching. The influence of each modification on antenna performance is systematically analyzed. To validate its performance, the proposed MPA is fabricated and measured, with the obtained results compared to the simulated ones. Simulation and measurement results show good agreement, confirming that the proposed MPA achieves a wideband response with a fractional bandwidth of 18.8% and a peak realized gain of 4.85 dBi. These results highlight the effectiveness of the proposed design and demonstrate its potential for compact and wideband wireless communication devices operating around 5 GHz frequency range.
Performance Comparison of Polycrystalline Solar Cells with and Without Reflectors
This research aims to analyze the comparison of power output between Polycrystalline solar panels with and without the use of mirror reflectors. The growing demand for sustainable energy sources necessitates ongoing efforts to enhance the efficiency of photovoltaic (PV) technology. In this study, a comparative experimental design was implemented over a two-day period, utilizing two identical solar panel configurations: a Reflector Equipped-Solar Panel (RESP) and a Solar Panel Without Reflector (WRSP). The electrical parameters of both configurations, including voltage, current, and power output, along with temperature, were continuously monitored and analyzed. The results indicate that the RESP consistently outperformed the WRSP. On average, the RESP produced 28–35% higher electrical current, which directly translated into a 25–32% increase in power output compared to the WRSP. The peak power of the RESP reached 21.3 W, whereas the WRSP peaked at 16.2 W. Although the RESP operated at a higher temperature (approximately 5–7 °C greater than WRSP), the power gain from increased irradiance effectively outweighed the thermal losses. These findings provide strong empirical evidence that reflective augmentation is a viable and cost-effective method for enhancing the performance of standard solar panels, offering valuable insights for maximizing renewable energy harvesting.
Arduino Controlled Automatic Capacitor Bank for Power Quality Improvement in Household Scale
The use of inductive loads on a household scale, especially in electricity customers with a power of 1300 VA, often leads to low power factor values (cos φ) which degrades power quality. This study aims to design and build an Arduino-based automatic capacitor bank system capable of dynamically improving power quality in 1300 VA household electrical installations. The system integrates an Arduino Nano microcontroller with a PZEM-004T sensor to monitor electrical parameters—such as voltage, current, and cos φ—in real time and automatically controls capacitor switching through relays to provide the most suitable compensation. Experimental results show that the system successfully increased the power factor from an average of 0.75 to 0.97 under varying load conditions. In addition, the implementation reduced reactive power by up to 65% and demonstrated potential savings in electricity bills of approximately 8–12% for household consumers. These findings indicate that the proposed system not only improves power factor close to ideal conditions (cos φ ≈ 1) but also enhances overall energy efficiency and provides tangible economic benefits for household users.
Copyright (c) 2024 Jurnal Teknik Elektro
Cat Skin Disease Diagnosis Using EfficientNetV2 for Lightweight Processing on Low-Resource Devices
Skin diseases are among the most common health issues in domestic cats. However, access to veterinarians is often limited, especially in low-resource settings. Automated image-based detection offers a fast and affordable alternative for early intervention. This paper presents a lightweight approach for diagnosing feline skin diseases using EfficientNetV2 optimized for low-resource devices. A balanced custom dataset consisting of 720 images across nine classes, namely Healthy, Mild/Severe Ringworm, Mild/Severe Acne, Mild/Severe Flea, and Mild/Severe Scabies, was compiled from Kaggle, Roboflow, and Google Images, ensuring ethical use of publicly available data. The images were augmented through rotations (0°, 90°, 180°, 270°) and horizontal flips, resulting in 5,760 images, to enhance model generalization. Five CNN architectures were benchmarked: DenseNet121, MobileNetV2, MobileNetV3, EfficientNetB0, and EfficientNetV2B0. Training was conducted with grid searches over batch sizes {64, 32, 16, 8} and learning rates {1e-3, 5e-4, 2e-4, 1e-4, 5e-5} for up to 300 epochs, and with the Adam optimizer and Reduce-LR-on-Plateau (decay factor 0.5). Early stopping (patience = 10) was used to mitigate overfitting. The best model was selected based on highest validation accuracy. The experiments were conducted on an Intel Xeon 6 CPU (2.2 GHz, 2 vCPUs) in Google Colab without GPU to simulate low-resource deployment. EfficientNetV2B0 achieved the best performance with 99.62% validation accuracy and 99.79% test accuracy, with an average inference latency of 78 ms/frame. Compared to previous studies focusing on heavyweight models or conventional ML using handcrafted features, this work highlights the feasibility of deploying an accurate real-time diagnostic pipeline on edge devices.
Urban 5G NR Deployment: A Techno-Economic Case Study in Semarang
The deployment of 5G networks in provincial capitals such as Semarang presents challenges related to high user demand, coverage requirements, and investment costs. This research aims to evaluate the network coverage and techno-economic feasibility of 5G implementation in Semarang. Using an urban macro propagation model, two scenarios were analysed: Uplink Non-Line-of-Sight (UL-NLOS) and Downlink Non-Line-of-Sight (DL-NLOS). The UL-NLOS scenario requires 11 sites, while the DL-NLOS scenario requires 6 sites to achieve full coverage of the city. The average Synchronization Signal–Reference Signal Received Power (SS-RSRP) is −125.74 dBm, indicating sufficient signal strength. A techno-economic analysis reveals that the UL-NLOS scheme yields an NPV of IDR 292.566.473.678 with an IRR of 110.25%, while the DL-NLOS scheme yields an NPV of IDR 300.000.000.000 with an IRR of 115.38%. These results confirm that both scenarios are technically feasible and economically viable. The findings suggest that 5G deployment in Semarang can yield profitable returns, providing valuable insights for mobile operators in planning future investments.
Adaptation in IoT-Fog Data Transmission: SLR and Future Perspectives on Dynamic Frequency Control
The advancement of Internet of Things (IoT) and fog computing technologies has created significant opportunities for more efficient, faster, and proximity-based data management. However, IoT-Fog systems face considerable challenges related to device heterogeneity, traffic dynamics, and the complexity of network topologies that continuously change. This study conducts a Systematic Literature Review (SLR) of various research works covering dynamic scheduling, routing, context-aware data flow, offloading, and IoT-Fog systems without adaptive mechanisms. The findings indicate that most existing approaches still rely on relatively static topology assumptions, rendering them insufficiently adaptive to real-time changes in network conditions. One area identified as a research gap is dynamic frequency control, an adaptive mechanism capable of dynamically adjusting data transmission intensity based on network conditions. The main conclusion of this study emphasizes the necessity for developing adaptive systems that are topology-agnostic and supported by dynamic frequency control to maintain optimal performance even under significant topology changes. Such systems are anticipated to become a crucial foundation for future IoT-Fog applications, including smart cities, Industry 4.0, and intelligent healthcare services, which demand high reliability and low latency.
Analysis and Characterization of Shielding Material for Mitigating Electromagnetic Interference in UAVs
This study aims to mitigate the impact of electromagnetic interference on the performance of electronic systems in Unmanned Aerial Vehicles (UAVs) by employing various shielding materials. The materials tested include carbon fiber, E-glass, E-glass with an aluminium foil, and E-glass with a copper foil. A Vector Network Analyzer (VNA) and scattering parameter (S-Parameter) analysis, including reflection, absorption, and multiple reflection, were used to evaluate the shielding effectiveness of these materials within the frequency range of 4 - 5 GHz. The results showed that E-glass coated with copper had good overall shielding performance for SER, SEA, and SET values. This material reached a SET value of 96 dB at a frequency of 4.6 GHz, followed by E-glass coated with aluminium. In addition, adding carbon layers increased the shielding effectiveness, while E-glass without coating had the lowest shielding performance compared to the other materials. These findings indicate that E-glass coated with metal provides superior shielding effectiveness compared to carbon fiber, even when used in greater thickness.
Miniaturized and Bandwidth Improvement of Bandpass Filter using A Quarter Wavelength Resonator
The miniaturization of radio frequency (RF) and microwave filters is crucial in modern communication systems, particularly in space-constrained environments such as portable, wearable, or integrated multi-band platforms. In this study, a compact single band-pass filter (BPF) is proposed using an L-shaped resonator loaded with a via-through hole. The incorporation of the via allows the creation of a quarter-wavelength resonator, which significantly reduces the physical size to 44.6% compared to traditional half-wavelength resonators commonly used in band-pass filters. Additionally, the via contributes to an improved impedance bandwidth. Two L-shaped resonators are arranged in a back-to-back configuration to realize a second-order filter response. The structure is excited through a stub-loaded transmission line that couples the signal from port one to port two. To validate the proposed design, the filter was fabricated and measured. Experimental results demonstrate a -3 dB fractional bandwidth (FBW) exceeding 25% at the centre frequency of 3.2 GHz, with a measured insertion loss of -1.662 dB across the passband, indicating good performance for compact filter applications.
Systematic Review: Enhancing Photovoltaic Performance through Hydrophobic Surface Coatings and Heat Mitigation
Surface coatings are essential for improving the performance of photovoltaic (PV) modules, particularly in challenging environments such as hot and dusty tropical regions or low-radiation subtropical areas. This study reviews recent developments in hydrophobic and heat-mitigating coatings that address two main problems: soiling and overheating. Both issues significantly reduce the efficiency and lifetime of PV systems. By systematically reviewing 32 selected studies, this paper identifies coating materials that combine self-cleaning, thermal regulation, and optical benefits. Among them, three-layer superhydrophobic coatings, silica–silane nanocomposites (e.g., SiO₂–PDMS, HDTMS–SiO₂), and Al₂O₃–H₂O nanofluids showed remarkable improvements in power output, optical transmission, and heat dissipation. For example, a three-layer superhydrophobic coating increased output power by 195.2% under simulated drizzle with heavy soiling, while SiO₂–PDMS nanocomposite films reduced surface temperature by 3.5 °C without reducing efficiency. These findings confirm that coating selection must balance hydrophobicity, heat management, durability, and cost. This review also highlights research gaps, particularly in long-term durability testing and large-scale application. The results provide practical guidelines for selecting appropriate coatings in different climates and suggest directions for developing multifunctional and scalable PV surface technologies.
Externally Validated Deep Learning Model for Multi-Disease Classification of Chest X-Rays
Accurate classification of chest X-ray (CXR) images is vital for early detection of thoracic diseases such as COVID-19, Tuberculosis, and Pneumonia, particularly in regions with limited radiological expertise. While deep learning has shown promise in CXR interpretation, many existing models rely solely on internal datasets, risking overfitting and poor generalizability. Furthermore, inadequate tuning of network architectures may limit robustness across varied imaging conditions. This study presents an externally validated deep learning framework based on Convolutional Neural Networks (CNNs) for multi-disease CXR classification. This study compared a baseline CNN with two convolutional layers against a tuned architecture with three layers across multiple image resolutions (64×64, 112×112, 224×224). The proposed model employs transfer learning with a pre-trained CNN, fine-tuned for four-class classification using a softmax output layer. Training was performed with the Adam optimizer (learning rate: 0.0001, batch size: 32) and categorical cross-entropy loss, for up to 50 epochs with early stopping. Internal validation showed the tuned model outperformed the baseline, achieving 0.97 accuracy and an F1-score of 0.89. External validation confirmed superior generalizability, with the tuned model attaining an F1-score of 0.83 and an AUC of 0.97 at 112×112 resolution, compared to the baseline’s F1-score of 0.79 and AUC of 0.94. These results highlight the potential of optimized CNN architectures as reliable, scalable tools for radiological decision support in resource-limited healthcare systems. Future work will incorporate explainable AI methods and real-world clinical validation to ensure safe, interpretable deployment.
Pilot Study: Portable Non-Invasive Blood Sugar, Cholesterol, Uric Acid Monitoring System
Degenerative diseases commonly associated with abnormal blood sugar, cholesterol, and uric acid levels require regular monitoring. Remote health monitoring technology enables children to monitor their parents' health conditions from a distance. This research presents a prototype development through Research and Development (R&D) methodology. This study developed a portable, low-cost, non-invasive detection system for blood sugar, cholesterol, and uric acid levels using the TCRT5000 sensor with Telegram integration. The compact device offers real-time monitoring advantages without blood sampling. The development followed the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. The research results show the prototype's coefficient of determination for blood sugar is 0.9733, cholesterol is 0.9411, and uric acid is 0.9610. The non-invasive prototype demonstrates measurement errors of 7.41% for blood sugar, 15.83% for cholesterol, and 14.69% for uric acid. These error rates currently exceed medical measurement standards. The system successfully integrates with the Telegram application for remote monitoring. Future research should incorporate artificial intelligence algorithms to minimize error values.
Comparative Analysis of Static Var Compensator and Distributed Generation Installation on Voltage Profile
In Indonesia, electricity is a basic need with demand that continues to grow. PT PLN (Persero) projects an increase in electricity consumption of 8.9% by early 2022, highlighting the urgent need to address frequent problems such as blackouts, power losses, and voltage sags in the power distribution system. Effective solutions, including Static VAR Compensator (SVC) and Distributed Generation (DG), have been proposed to improve voltage stability and reduce power losses. This study evaluates and compares the performance of SVC and DG on a standard IEEE 14-bus system under increased load conditions. Using power flow analysis in ETAP, we simulate the installation of SVC at 15.99 Mvar and DG at 20.58 Mvar on bus 9, which shows optimal results. The findings show that DG slightly outperforms SVC in improving voltage stability and reducing power losses, with a 0.16% greater voltage increase and a 3.2 MW or 17.3% reduction in power losses. These results indicate that although both devices meet PLN’s voltage standards and improve power system efficiency, DG provides a slightly superior improvement in overall system performance.
Techno-Economic of Rooftop Solar Power Plants for Residential Customer in Indonesia
This study evaluates the financial viability of grid-connected rooftop photovoltaic (PV) systems across seven Indonesian residential types (2.75-19.8 kWp) under current market conditions. Using a discounted cash flow model with 8% discount rate and a 20-year project lifetime, key metrics including Net Present Value (NPV), Levelized Cost of Electricity (LCOE), and payback period have been analysed. Results demonstrate that system economics improve significantly with scale: while small systems (2.75 kWp) yield negative NPV (-Rp 9.71 million) and 12.4-year payback, larger installations (>7.7 kWp) achieve positive NPV (up to Rp 89.95 million for 19.8 kWp systems) and sub-10-year payback periods. The LCOE ranges from Rp 1,082-1,205/kWh, representing 11-36% cost savings compared to PLN's tiered tariffs (Rp 1,352-1,699.53/kWh). Monthly savings scale proportionally with system size, from Rp 323,378 (2.75 kWp) to Rp 2.84 million (19.8 kWp). A critical 7.7 kWp capacity threshold emerges for self-sustaining viability without subsidies, with 3500+ VA customers benefiting most due to higher avoided tariffs. These findings provide policymakers with evidence to design tiered incentive programs targeting underperforming market segments (<6 kWp systems), while confirming the commercial readiness of larger residential-commercial hybrid systems in Indonesia's solar transition.
Rooftop Photovoltaic, Techno-Economic, Solar power Plant
Design and Analysis of a Hexagonal Patch Antenna Operating at 3.5 GHz for Wireless Communication Applications
Microstrip antennas are widely recognized for their compact structure, low profile, and ease of fabrication, making them highly suitable for modern wireless communication systems. Traditionally, these antennas incorporate a rectangular metallic patch as the radiating element. In this study, a novel microstrip antenna design featuring a hexagonal metal patch is proposed, specifically optimized to resonate at 3.5 GHz, a frequency band allocated for 5G wireless communication applications. The antenna is constructed on an F4BMX220 substrate with a thickness of 1.5 mm, chosen for its favorable dielectric properties and mechanical stability. The feeding mechanism employs an inset-fed microstrip line, enabling better impedance matching and improved power transfer. A full ground plane is used on the underside of the substrate to enhance isolation and minimize back radiation. The complete design, simulation, and optimization processes are carried out using CST Studio Suite, a professional electromagnetic simulation tool. Key performance parameters such as return loss (S11), directivity, and gain are thoroughly analyzed. The design aims to achieve an S11 value below -10 dB, ensuring efficient radiation at the target frequency. With its optimized structure and favorable performance, the proposed antenna serves as a promising candidate for integration into next-generation 5G communication systems. Based on the fabricated prototype, the antenna demonstrates a gain of 4.5 dBi and a bandwidth of 24 MHz.
Microcontroller-based Design a Supporting System for Irradiance, Temperature and Tilt Angle Conditioning of PV Performance Measurement
Several factors influence PV output, including irradiance, temperature, and panel tilt angle. Irradiance and tilt angle affect the Photovoltaic (PV) current, while temperature affects the PV voltage. To accurately measure the performance of PVs in the field, it is necessary to condition these three parameters. Therefore, a system should be created to condition these parameters. This research aims to develop a control and monitoring system based on the set point to condition irradiance, temperature, and tilt angle. This research started with system design, fabrication, and functionality testing with the black box method, sensor reading accuracy, and PV performance testing. The functionality testing results indicate that each test item can perform as expected. The sensor testing revealed 1%, 0.26%, and 1.8% errors for MLX90614, BH1750, and MPU6050, respectively. The system's performance was tested, and it was discovered that the input value, which is the setpoint of the three variables, i.e., irradiance, temperature, and tilt angle of the PV, can produce outputs that are almost the same as the input value and settling time is different to reach the setpoint of each parameter. PV performance measurement using this system can represent PV performance by conducting direct testing using direct sunlight.
Rotor Angle Analysis on Power Proportion in Generator and Photovoltaic Hybrid Low Voltage System
Electrical energy is essential in modern society, and with the growing demand, all available energy resources are being utilized to meet this need. Insufficient planning and calculation in energy provision can affect the stability of electricity generated by power plants, especially in low-voltage microsystems directly connected to loads. This study investigates the behavior of synchronous microgenerators and photovoltaic systems, focusing on the rotor angle of the generator. Changes in the photovoltaic contribution can cause shifts in the generator's rotor angle, necessitating an analysis of these shifts. An experimental method was used to measure and analyze the power distribution by recording current and voltage in a micro synchronous generator and a grid-tie inverter under a 300-watt linear load. The results show that the generator’s rotor angle remains stable and the grid-tie inverter remains synchronized, with an average angle of 40.2° at a photovoltaic contribution of 32.26% and a synchronous microgenerator contribution of 67.74%. However, when the photovoltaic supply exceeds 37.00%, the rotor angle shifts further, leading to a loss of synchronization in the grid-tie inverter. Additionally, irradiance was found to have a linear effect on photovoltaic distribution. The findings of this study contribute to a better understanding of rotor angle behavior and grid synchronization, providing insights for the development of more reliable and efficient renewable energy systems that maintain electrical stability in low-voltage applications.
State of Charge Balancing Analysis Using Droop Control on Energy Storage System
The growing energy crisis has driven the global shift toward renewable energy development. Governments are taking significant steps by promoting diverse sources such as photovoltaic (PV), wind turbines, and battery systems. Among these, Battery Energy Storage Systems (BESS) play a crucial role in decentralized energy generation, especially in DC microgrids. Within BESS, Battery Energy Storage Units (BESUs) are vital components, where monitoring the State of Charge (SOC) is essential. The Coulomb Counter (CC) method is widely used due to its reliability in SOC estimation. This study introduces a dynamic SOC balancing strategy using droop control, aiming to maintain uniform SOC across multiple BESUs. The proposed method regulates BESU discharge behaviour to achieve SOC parity and optimize energy distribution throughout the microgrid. Simulations under various operating scenarios charging and discharging modes with different SOC levels were conducted using the MATLAB/SIMULINK® environment. The results show that the control approach effectively equalizes SOC levels under non-uniform initial conditions. The balancing duration varied according to the initial SOC difference, highlighting the controller's adaptability. Although the study did not directly measure battery lifespan or energy efficiency, enhanced SOC uniformity is expected to reduce current imbalances and operating stress, potentially improving long-term system reliability. This research offers valuable insights into the control and management of BESS, supporting the stable integration of renewable energy in modern microgrid applications.
Land Cover Classification from Hyperspectral Images Using Regularized Hybrid CNN and ADAM
The utilization of hyperspectral imagery offers enhanced detail and accuracy for environmental monitoring and natural resource management, particularly through land cover classification. Hyperspectral data capture spectral signatures across numerous wavelengths, allowing precise differentiation of various surface materials and land types. While numerous approaches have been proposed for hyperspectral image classification, many suffer from overly complex model structures and suboptimal performance, limiting their practical application. This study introduces a simplified yet effective architecture by implementing a Regularized Hybrid Convolutional Neural Network (CNN) optimized using Adaptive Moment Estimation (ADAM). The proposed model is evaluated on the widely used Pavia Center hyperspectral dataset to assess its performance in land cover classification tasks. The model achieves a notable Overall Accuracy of 99.25% and Average Accuracy of 97.50%, demonstrating its capability in handling high-dimensional hyperspectral data with reduced model complexity. Additionally, a comparative analysis with conventional CNN architectures is conducted, highlighting the superior performance and efficiency of the proposed approach. These findings underscore the potential of regularized hybrid CNNs as a reliable and scalable solution for hyperspectral image classification, especially in applications requiring high precision and reduced computational overhead.