Jurnal Nasional Teknik Elektro

IoT-Based Cassava Slicing Machine Using ESP32 with Smartphone Monitoring

Dara Sawitri — 2026-03-28

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.

Implementation of a PID-Based Temperature Control System on a Nextion HMI for Infant Warmer Applications

Farit Ardiyanto — 2026-03-29

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.

Predictive Modeling of Carbon Monoxide with MOS Sensors and Machine Learning: A Potential Tool for Process Safety Improvement

Hermin Kartika Sari — 2026-03-29

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.

Performance and Techno-economic Analysis of a 1.82 kWp Rooftop PV System in the Tropical Climate of Indonesia: A Simulation vs Reality Approach

Rifaldi Wahyu Santoso — 2026-03-29

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.

Impact of Transformer Grounding On TRV During Inductive Load Switching at Transmission and Substation Service Unit (ULTG) Maros

Muhammad Khaidir — 2026-03-29

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.

Effect of MXene Loading on the Structure and Electrochemical Performance of Biodegradable PVA/ZnO/MXene/CNC Composite Films

Rudy Fernandez — 2026-03-29

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.