Infant body temperature stability is paramount, especially for preterm newborns unable to maintain their own thermal equilibrium. Here, we explore a Proportional-Integral-Derivative (PID) control algorithm implemented directly on a Nextion Human–Machine Interface (HMI) to regulate infant warmer temperature. Unlike typical systems where the microcontroller holds the major PID calculation and the HMI acts as a display only, this method integrates the PID logic into the HMI itself, with possible reductions of microcontroller load, minimization of communication delays, and hardware architecture simplification. Three trials at a constant setpoint of 37 °C with varying combinations of PID gains were used with a fixed experimental setup. Temperature response indicators like rise time, settling time, percent overshoot, and steady-state error were measured and compared. Results indicate that with gains of Kp = 1.50, Ki = 0.05, and Kd = 1.50, the system reached a steady state of 36.97 °C with just 2.16 % of an overshoot and a settling time of about 7 minutes and satisfied neonatal warmer requirements. The results confirm that PID control executed directly on the Nextion HMI can achieve temperature regulation performance comparable to conventional microcontroller-based implementations while improving system simplicity and code efficiency. It presents a good alternative choice of low-power and portable infant warmer and also of other embedded hot and cold control systems.
The growing demand for sustainable materials for flexible electronics and energy storage applications has driven the development of biodegradable composite films with enhanced electrochemical functionality. This study systematically investigates the effect of MXene loading on the structure, morphology, and electrochemical performance of biodegradable PVA/ZnO/MXene/CNC composite films fabricated by aqueous solution casting. The main contribution of this work is the explicit establishment of a relationship between loading, structure, and electrochemical performance for this multicomponent biodegradable film system under controlled processing conditions. Films containing 20%, 25%, and 30% MXene were prepared with constant ZnO and CNC contents and characterized by X-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM), and cyclic voltammetry in 1 M KOH. The crystallinity increased from 20.06% to 27.58% and 44.74% with increasing MXene loading, while FESEM revealed progressively more homogeneous morphology and improved filler dispersion. These structural changes were accompanied by a marked enhancement in electrochemical response, with current density increasing from 425.18 to 876.71 and 1480.25 A/m², and specific capacitance rising from 0.921966 to 1.682536 and 2.860035 F/g for 20%, 25%, and 30% MXene, respectively. The 30% MXene film exhibited the best overall performance, indicating that higher MXene loading within the investigated range promotes more continuous conductive pathways and greater electroactive surface accessibility. These findings provide useful insight for designing biodegradable composite films for sustainable flexible energy-storage applications.
The utilization of renewable energy through rooftop photovoltaic (PV) systems serves as a strategic solution for mitigating climate change; however, their performance in tropical climates often exhibits a deviation between theoretical predictions and field reality. This study aims to evaluate the technical performance and economic viability of an on-grid 1.82 kWp rooftop PV system in Indonesia. The research employs a comparative quantitative approach by validating PVsyst simulation results against actual measurement data recorded from April to July 2024. The findings indicate a simulation overestimation, where actual energy production was 30.3% to 40.5% lower than PVsyst projections. A significant discrepancy was also observed in the Performance Ratio (PR), with the actual PR reaching only 55-59%, substantially lower than the simulated 81-82%. Despite these technical inconsistencies, the economic analysis confirms the project's financial feasibility. Under a 5.25% interest rate scenario, the study yielded a Net Present Value (NPV) of IDR 15.88 million, a Benefit-Cost Ratio (BCR) of 1.50, a Payback Period of 9.8 years, and a Levelized Cost of Electricity (LCOE) of IDR 974.88/kWh, more competitive than the national utility (PLN) tariffs. In conclusion, although tropical environmental factors such as high temperatures and dust accumulation reduce technical efficiency, rooftop PV investment in Indonesia maintains strong profitability and remains viable for implementation.
Carbon monoxide (CO) is a toxic, odorless gas commonly present in industrial processes and poses serious risks to occupational safety and health. This study proposes an optimized machine-learning-based approach to predict CO concentration using metal-oxide semiconductor (MOS) sensor arrays. The model was trained and evaluated on a public dataset comprising 650 time-series measurements from 14 thermally modulated MOS sensors, tested across CO concentrations ranging from 0 to 8.9 ppm under dynamic relative humidity (15%–75%). To optimize computational efficiency and mitigate multicollinearity, a multi-method feature selection strategy that combines Random Forest importance, Recursive Feature Elimination (RFE), and Mutual Information (MI) was implemented, successfully isolating sensors R10, R11, and R13 as the most robust predictors. A Random Forest Regression model, optimized via grid search and validated through five-fold cross-validation, was subsequently developed. The proposed framework demonstrated high predictive accuracy, achieving an R² of 0.884, Root Mean Square Error (RMSE) of 2.189 ppm, Mean Absolute Error (MAE) of 1.215 ppm, and Symmetric Mean Absolute Percentage Error (SMAPE) of 34.27%. These results highlight the potential of combining low-cost, feature-optimized MOS sensor arrays with ensemble machine learning for accurate, real-time gas monitoring. The framework provides a computationally efficient decision-support tool for the early detection of hazardous CO levels, contributing to safer process environments.
This paper investigates the impact of transformer grounding configurations on Transient Recovery Voltage (TRV) during fault current interruption in high-voltage power systems. The study evaluates three grounding schemes: Solid–Solid, Solid–Floating, and Solid–Resistance, applied on a step-down transformer located at the Tello substation. Each configuration was modeled and simulated using ETAP 19 software to observe TRV behavior under three-phase fault conditions. The results demonstrate significant variations in TRV profiles depending on the grounding type. The Solid–Solid configuration exhibits the highest TRV peak (22,500 V) and the steepest Rate of Rise of Recovery Voltage (RRRV), reaching 833.33 kV/ms, indicating severe stress on circuit breaker insulation. In contrast, the Solid–Floating configuration yields a moderate TRV peak (19,800 V) with less consistent waveform stability due to the absence of a defined secondary reference. Meanwhile, the Solid–Resistance scheme, using a 20 Ω resistor, produces the most damped TRV waveform with the lowest peak (17,100 V) and RRRV (approximately 589.66 kV/ms), offering improved insulation coordination. The comparative analysis confirms that controlled grounding through resistance effectively mitigates TRV magnitude and oscillation, making it a viable approach to enhance circuit breaker performance and reliability. These findings can be used to guide grounding system design in high-voltage substations to reduce the risk of re-ignition or insulation failure.
The rapid advancement of Internet of Things (IoT) technology continues to open new opportunities in the development of automation and monitoring systems, particularly for household applications and small-scale industries. This study presents the design and implementation of an IoT-based cassava slicing machine equipped with smartphone monitoring, where the ESP32 microcontroller serves as the main control unit. The proposed system enables real-time operation and supervision of the machine through a Wi-Fi–connected mobile application. Its key features include remote power control, adjustable slicing motor speed, and direct monitoring of machine status. Experimental results demonstrate that integrating ESP32 with IoT ensures fast, stable, and reliable performance in responding to user commands. Moreover, the monitoring capability enhances energy efficiency and supports operator safety during the slicing process. Therefore, the IoT-enabled cassava slicing machine with smartphone monitoring is highly promising for adoption in small and medium enterprises (SMEs) as part of the transition toward smart and integrated automation technologies.
This research aims to design and implement a Proportional Integral Derivative (PID) control system to regulate the speed of a DC motor using the Ziegler Nichols tuning method, focusing on improving the stability and responsiveness of the system for industrial automation applications. The research uses a mathematical model of a DC motor derived from its electrical and mechanical components for simulation and analysis of the system behavior. The PID controller is implemented with the Ziegler Nichols tuning method (open and closed loop) to determine the optimal parameters (Kp, Ki, Kd). The system was tested using Arduino, L298N motor driver, and MATLAB for simulation and analysis. Performance is evaluated based on response characteristics such as rise time, settling time, overshoot, and steady state error. The Ziegler-Nichols method successfully tuned the PID controller with optimal parameters Kp = 11.7, Ki = 1, and Kd = 0.25. Analysis of the system response shows rise time = 0.4866 s, settling time = 2.5829 s, overshoot = 19.6194%, and steady state error = 0.0861%. This PID-controlled system provides fast response and good stability, with significant improvement in reducing steady state error and overshoot compared to systems without controllers or those using trial-and-error tuning. The Ziegler-Nichols tuning method is effective for optimizing PID control in DC motor speed regulation. The proposed system offers a reliable and efficient solution for industrial applications that require precision motor control.
Twitter is one of the most dynamic social media platforms that provides real-time information through its trending topics feature, which reflects the most talked about issues among users. However, in Indonesia, trending topics are often dominated by entertainment, celebrity gossip or light-hearted viral content, and are not used to highlight or analyze more substantial social issues. This study aims to classify Twitter trending topics in Indonesia using three clustering algorithms: K-Means, DBSCAN, and Latent Dirichlet Allocation (LDA). Data was collected over a certain period and processed through a text preprocessing stage before applying the clustering algorithms. The results show that LDA without keyword filtering provides the most relevant and dominant topic classification, the bar chart results tend to be dominant in topic 0 there are as many as 160 topics with the main cluster relating to the Indonesian presidential election. These findings suggest that LDA outperforms K-Means and DBSCAN in identifying latent topic structures in Twitter data. This study contributes to a better understanding of trending topics and supports data-driven public opinion analysis and decision-making.
This study addresses the performance limitations of conventional hybrid couplers used in sub-6 GHz 5G infrastructure, targeting the N1 band (1.92–2.17 GHz), and integrates Defected Ground Structure (DGS) technology. The objective is to enhance bandwidth, reduce return and isolation losses, and optimize phase coupling while maintaining cost-effectiveness using FR-4 epoxy substrates. A quadrature hybrid coupler was designed and optimized using microstrip line technology with DGS modifications. The study employed advanced electromagnetic simulation software to evaluate key performance parameters, including return loss, isolation loss, bandwidth, insertion loss, and phase coupling. The DGS-modified design was compared with a conventional coupler to quantify performance improvements. The DGS-modified coupler achieved significant enhancements across all performance metrics. Return loss improved to −23.17 dB, isolation loss to −44.39 dB, and bandwidth increased by 34%, reaching 693.2 MHz. Phase coupling also approached the ideal 90° with a deviation of only 2.56°, significantly outperforming the conventional design. However, the insertion loss increased slightly to −4.34 dB, reflecting a trade-off between bandwidth enhancement and efficiency that must be considered in practical implementations. Overall, the integration of DGS into hybrid coupler designs provides a practical and effective means of enhancing RF component performance for reliable 5G networks while maintaining low-cost fabrication. These results underscore the potential of DGS technology for developing scalable, application-oriented solutions for next-generation wireless communications.
This study successfully designed and implemented a LabVIEW-based nickel laterite ore leaching process control system on a laboratory scale. This system integrates key hardware components such as Arduino Mega 2560, temperature sensor, volume sensor, jet bubble reactor, and LabVIEW-based user interface that allows automatic and real-time monitoring and control of process parameters. The calibration results showed high accuracy, with temperature measurement error values of 0.04% and 0.015% compared to the calibrator. Volume measurements under five test conditions produced error values ranging from 0.023% to 0.066%, with the best accuracy shown by readings via the LabVIEW HMI. Leaching process testing was carried out using variations in citric acid concentrations. The resulting filtrate volume showed a decrease from 173 mL at a concentration of 0.5 mol to 8 mL at a concentration of 2 mol, indicating that the higher the solution concentration, the greater the viscosity of the solution, thereby inhibiting mass transfer. The application of jet bubble technology has been shown to increase the efficiency of mixing and contact between the leaching solution and the ore, which accelerates the leaching process. Overall, the system shows high stability, accuracy, and reliability for laboratory scale applications. This system is considered suitable for use as a learning medium, an initial simulation tool for the APAL (Atmospheric Pressure Acid Leaching) process, and a means of supporting research in the development of efficient, energy-saving, and environmentally friendly nickel extraction technology.
Spoken digit recognition (SDR) plays a critical role in biometric authentication and human–computer interaction, yet existing approaches often rely on small datasets, limited feature representations, or architectures prone to overfitting. To address these limitations, this study proposes a robust end-to-end pipeline that integrates Wavelet Time Scattering (WTS), Mel-Frequency Cepstral Coefficients (MFCC), and a 2D Deep Convolutional Neural Network (2D-CNN) to enhance the accuracy and generalization of SDR systems in realistic environments. The Free-Spoken Digit Dataset (FSDD), consisting of 3000 audio samples from speakers with diverse accents, was pre-processed using zero-padding normalization and transformed into high-resolution time–frequency spectrograms via WTS. The proposed CNN architecture, optimized through systematic experimentation on batch size and learning rate, demonstrated stable convergence and superior discriminative capability. Using a learning rate of 0.001 and a batch size of 50, the model achieved the highest performance with 99.2% accuracy, outperforming established methods including SVM, MFCC-LSTM, and Multiple RNN architectures. Comparative evaluations further revealed that the combined WTS–MFCC feature extraction significantly enhances spectral–temporal representation quality, contributing to improved classification precision across all digit classes. These findings demonstrate that the proposed WTS-MFCC-CNN framework not only advances SDR accuracy but also provides a scalable and computationally efficient approach suitable for real-world biometric, financial, and voice-controlled applications. The results highlight the potential of hybrid time–frequency representations integrated with deep architectures to set a new benchmark for robust spoken digit recognition.
Efficient and flexible control systems for remote real-time monitoring are highly needed by modern industries. Supervisory Control and Data Acquisition (SCADA) has become a core technology in automation. Most commercial SCADA systems, however, are costly and complex, thus limiting their adoption in small industries and educational institutions. Therefore, this study develops an integrated SCADA–PLC–HMI model for low-cost control and real-time monitoring of three-phase AC motors. The model is designed as an economical, portable, and user-friendly system that supports various control configurations, including Direct On-Line (DOL), Forward-Reverse, and Star-Delta. Adopting the ADDIE (Analyze, Design, Develop, Implement, and Evaluate) model, the development process involves needs analysis, system design, hardware/software development, system implementation, and performance evaluation through testing and data logging. Experimental results show that the system can alternately operate two three-phase motors, present real-time monitoring through the HMI, and automatically record operational data. The log file records system validity, operating time, and Start/Stop button responses, with timers operating at the configured 5-second delay. Motor 1 and Motor 2 run as programmed, with Motor 2 operating for 14 seconds before shutting down. These findings confirm that the proposed SCADA–PLC–HMI system is reliable, efficient, and cost-effective, thus providing a practical solution for motor control that is both affordable and adaptable. This study contributes to supporting competency development in engineering education and offers a viable automation alternative for small- and medium-scale industries.
This paper investigates the optimization of the Global Maximum Power Point (GMPP) and the simulation of Total Harmonic Distortion of Current (THDI) from an inverter connected to a nonlinear load. THDI variations are analyzed with respect to ambient temperature (T) and solar irradiance (G). The study also highlights how harmonic components negatively affect steady-state voltage stability in photovoltaic (PV) systems. The Queen Honey Bee Migration (QHBM) algorithm is applied to optimize GMPP while minimizing THDI. An off-grid PV-inverter system is modeled in MATLAB/Simulink. The model extracts THDI as a function of temperature and irradiance. Simulations cover irradiance from 794.8 to 994.2 W/m² and temperature from 20.0°C to 32.3°C, based on daily measurements from 08:25 to 16:50. The QHBM algorithm tracks GMPP effectively under fluctuating irradiance. Results show a 17.3% improvement in power extraction efficiency and a 32.8% reduction in THDI compared to conventional methods. The highest THDI occurs during low irradiance, particularly in the early morning and late afternoon. The algorithm converges in 0.18 seconds, outperforming other techniques. THDI increases during rapid irradiance and temperature changes. The proposed method ensures stable performance and lower THDI. Combining QHBM with active harmonic filters under low irradiance conditions is recommended to improve power quality and enhance system protection.
This study aims to analyze the insulation resistance value of a 555 WP monocrystalline solar module under the influence of solar irradiation through outdoor testing and insulation assessment. The primary focus is to understand the impact of solar exposure on insulation durability, a crucial factor in the long-term performance and safety of solar modules. The testing method follows the SNI/IEC 61215 standard, involving initial and final measurements using a calibrated insulation tester at the Energy Conversion Laboratory, BRIN. The results indicate a 19.54% degradation in insulation resistance after 15 days of solar exposure. Despite this decline, the module still meets the IEC 61215 criteria for insulation resistance, maintaining a resistance value above 40 MΩ for a module with a surface area of 2.583 m². A comparison of initial and final data reveals a decrease in resistance from 3.470 GΩ in the initial test to 2.792 GΩ in the final test. This reduction underscores the importance of paying closer attention to maintenance and routine testing to ensure the module's long-term reliability. This study provides new empirical evidence on the dynamics of short-term insulation degradation under tropical solar conditions, a topic that has been rarely quantified in field-based PV reliability research. In addition, this study makes significant contributions to the development of industry standards that aim to enhance the reliability of solar modules and manage renewable energy systems.
This study presents a comprehensive, simulation-based validation of a Luenberger Observer (LO) specifically designed for fault detection in storage tank systems. It commences with the development of a nonlinear storage tank model, which is subsequently linearized to streamline the observer design process. The LO estimates critical system states and produces residual signals that enable reliable fault detection. The observer gain is meticulously chosen using pole placement techniques to ensure rapid convergence of estimates and overall stability. To evaluate the effectiveness of this approach, three distinct fault scenarios—ramp, square pulse, and inverted ramp signals—are introduced to simulate various types of abnormal conditions that could occur in real-world operations. Simulation results demonstrate that the LO accurately estimates the liquid level states with a mean absolute error of approximately 0.02 meters, equivalent to about 2.6%. Furthermore, the observer detects faults with an average delay between 5 and 9 seconds following fault injection, indicating its prompt response capability. Notably, even with sensor noise levels reaching 6%, the observer maintains stable tracking performance, demonstrating strong robustness against disturbances. Across all tested scenarios, the residual signals show rapid increases during fault conditions and swiftly return near zero once the system reverts to normal operation, with no false alarms observed. Collectively, these results suggest that the Luenberger Observer provides an accurate, rapid, and disturbance-tolerant method for fault detection in storage tank systems. Such an approach offers a practical alternative to data-driven fault detection methodologies, as it relies less on extensive training datasets and can be more readily implemented for real-time industrial monitoring applications.
This paper presents a novel optimization algorithm, the NExIWAC (Natural Exponential Inertia Weight and Acceleration Coefficient) variant of Particle Swarm Optimization (PSO), for tuning PID controllers in DC motor speed control systems. The proposed NExIWAC algorithm improves control performance by dynamically adjusting the inertia weight and acceleration coefficients during optimization. To evaluate its effectiveness, the NExIWAC-tuned PID controller was compared against five established metaheuristic algorithms: Atomic Search Optimization (ASO), Sand Cat Swarm Optimization (SCSO), Grey Wolf Optimization (GWO), Invasive Weed Optimization (IWO), and Stochastic Fractal Search (SFS). The system's step response was analyzed under a reference speed demand of 1 p.u., with performance metrics including steady-state error, rise time, settling time, overshoot, and Integral of Time-weighted Absolute Error (ITAE). The NExIWAC algorithm demonstrated superior performance, achieving the fastest rise and settling times, zero steady-state error, and the lowest ITAE value among the tested algorithms. A robustness analysis was conducted by varying motor parameters, such as armature resistance and motor constant, by ±50%. The NExIWAC-PID controller exhibited stable and reliable performance under all conditions. Stability analysis through Bode plots and pole-zero mapping further confirmed the system's robust behavior, with a high phase margin and poles located in the left half of the complex plane. The results indicate that the NExIWAC algorithm is a powerful and reliable optimization tool for tuning PID controllers in DC motor applications, offering significant advantages in terms of precision, stability, and adaptability.
The effective operation of the IEEE 30 Bus power system requires economic dispatch optimization to minimize production costs, align energy supply with demand, and ensure system stability. This economic dispatch problem is complex due to its non-linear characteristics, interdependence between generators, and the need to combine cost minimization with power loss reduction. Conventional optimization techniques often struggle to find global solutions, easily get stuck in local optima, and require significant computational time. This study introduces the Orca Predation Algorithm (OPA) as a new approach to address these challenges. Inspired by the hunting behavior of orcas, OPA balances exploration and exploitation through two distinct phases: pursuit and attack. Evaluated on the IEEE 30-Bus system using power loss computation with coefficient B, the algorithm ensures that generator output power allocation meets demand at the lowest cost. OPA's performance is comprehensively compared with Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Bat Algorithm. The results consistently show that OPA achieves the lowest total cost of $772,754 while maintaining superior system stability and effectively minimizing power losses among the evaluated algorithms. These findings highlight the significant potential of OPA to enhance energy management and advance power system optimization.
Joper (Jowo Super) chicken parents are generally unable to incubate eggs or provide adequate care for their offspring, making the use of a specialized incubator essential from the hatching phase to Day-Old Chick (DOC) rearing. One of the primary factors contributing to DOC mortality is improper temperature adjustment in the heating system. To ensure optimal early-age development, Joper DOC requires a stable thermal environment within the range of 32°C to 35°C, depending on the growth stage. This study aims to develop an incubator capable of maintaining a constant temperature of 32 °C using Cohen-Coon PID (C-C PID) control while also regulating humidity levels. The proposed incubator integrates an axial fan and an L298N driver, with the temperature and humidity sensors calibrated prior to use. The calibration results show measurement errors of 0.59% for temperature and 5.02% for humidity, indicating high reliability. The application of C-C PID control demonstrates strong performance, characterized by a short rise time (approximately 225 seconds), an acceptable settling time (around 510 seconds), minimal overshoot (1.56%), and a steady-state error approaching 0%. During a 30-minute evaluation period, the system successfully maintained a stable temperature at the 32 °C set point and controlled humidity at below 50% automatically. Furthermore, the incubator design proved effective in practical use, achieving a 0% mortality rate for Joper DOC.
Quadrature Phase Shift Keying (QPSK) is a widely adopted digital modulation technique that encodes two bits of information in each symbol by utilizing four distinct phase states separated by 90 degrees. This approach offers high spectral efficiency, making it especially suitable for modern communication systems that demand robust data transmission with limited bandwidth. This investigation details the design process and LTspice-based simulation of a QPSK demodulator constructed entirely from discrete electronic components. This work addresses a gap in previous research, which has largely relied on integrated circuits or software-based algorithms, by focusing on circuit-level implementation using basic analog and digital components. The demodulator was assembled on a prototype PCB, combining fundamental operational amplifiers, mixers, filters, and digital logic gates to perform the required signal processing functions. The evaluation involved testing the demodulator's ability to accurately recover the transmitted data and its operational stability. Simulation results demonstrated reliable performance across all stages, with the demodulator successfully maintaining phase detection accuracy and reconstructing the original 8-bit test sequence with high fidelity. Under test conditions with a 1 MHz carrier frequency and a data transmission rate of 500 kHz, the recovered signal showed an approximate delay of 4.5 microseconds attributable to the sequential parallel-to-serial conversion process. Despite the delay, the demodulator maintained full symbol-level correlation with the transmitted data stream. These findings confirm that a discrete component-based QPSK demodulator can effectively support reliable digital communication, highlighting its practicality for educational purposes, low-cost prototyping, laboratory training, and preliminary hardware development in the field of wireless and wired communication systems.
Oil palm plantations in Indonesia demand efficient weed control methods, particularly for large-scale operations in remote areas. Manual pesticide sprayers are still commonly used, but they require high labor, long operating time, and are not energy-efficient. This study presents the design and development of a solar-powered pesticide sprayer using the ADDIE method—Analysis, Design, Development, Implementation, and Evaluation. The prototype consists of a 50 Wp monocrystalline solar panel, two 12V 24Ah VRLA batteries, a DC pump with variable pressure levels, and an Automatic Transfer Switch (ATS) for alternating battery use. The system is mounted on a frame suitable for motorcycle transport to improve field mobility and adaptability in plantation environments. Development followed all ADDIE phases and was validated through real-world field testing. Results showed a 75% reduction in spraying time—from 8 hours (manual) to 2 hours—with a maximum pressure of 70 PSI and a spray reach of 3.5 meters. The ATS allowed uninterrupted operation under varying sunlight conditions. This design offers greater energy efficiency, continuous usability, and flexible deployment compared to similar systems. The findings demonstrate the feasibility of applying solar energy to support sustainable weed management in off-grid agricultural settings, highlighting its potential for broader agricultural mechanization.
