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ELKHA : Jurnal Teknik Elektro
Published by Universitas Tanjungpura
ISSN : 18581463     EISSN : 25806807     DOI : http://dx.doi.org/10.26418
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering
The ELKHA publishes high-quality scientific journals related to Electrical and Computer Engineering and is associated with FORTEI (Forum Pendidikan Tinggi Teknik Elektro Indonesia / Indonesian Electrical Engineering Higher Education Forum). The scope of this journal covers the theory development, design and applications on Automatic Control, Electronics, Power and Energy Systems, Telecommunication, Informatics, and Industrial Engineering.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 302 Documents
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Integration of Matlab and LabVIEW for the Simulation of an AVR in a 1.1 kVA Synchronous Generator Mulyanto, Widodo Pudji; Leci, Geraldi; Sulistiawati, Irrine Budi
ELKHA : Jurnal Teknik Elektro Vol. 17 No.2 October 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i2.97417

Abstract

Automatic Voltage Regulator (AVR) in the system functions to regulate and stabilize the output voltage of the synchronous generator to remain constant, even in the event of load changes or system conditions. This research uses a software-based approach by integrating LabVIEW data acquisition to measure the actual input and output voltages of the generator and to create the transfer function along with its simulation using Matlab. The Automatic Voltage Regulator used is designed for a 1.1 kVA synchronous generator, Delorenzo DL 1026A. Simulation in Simulink is performed to analyze the system response towards changes in the load using PID as the controller. With the auto-tuned values of Kp = 1.815, Ki = 288.290, and Kd = 0, the system maintained voltage stability under disturbances of 0.1 pu, 0.25 pu, and 0.5 pu. The developed AVR was able to respond to load changes quickly and maintain the stability of the generator"™s output voltage by showing relatively small steady-state error indicated with steady state conditions for 0.00018 pu, 0.00024 pu, and 0.00033 pu in every load change. This phenomenon proves the PID controller resulting from the proposed method is capable of maintaining the voltage magnitude according to the load changes that occur. This research is expected to serve as a basis development of a more reliable and adaptive voltage control system for small-scale generators.
Application of Anti-Collision Visual Detection Algorithm in Warehouse Management System Using Raspberry Pi Hidayati, Qory; Sari, Danar Retno; Prastya, Muhammad Ramadhan
ELKHA : Jurnal Teknik Elektro Vol. 17 No.2 October 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i2.93069

Abstract

Ensuring safety and efficiency at warehouse intersections has become increasingly vital in the era of automation and intelligent logistics. This study proposes a vision-based anti-collision traffic management system tailored to the dynamic warehouse environment. By combining YOLOv5 object detection with a real-time microcontroller-based actuation system, the system detects and prioritizes movement between forklifts and pedestrians. Four webcams positioned at warehouse intersections transmit visual data to a Raspberry Pi 4, which performs object detection and decision-making based on predefined priority rules. Actuation is executed via Arduino Uno and Nano for signaling "GO" or "STOP" using running text displays and buzzers. The system achieved a mean Average Precision (mAP) of 94.7% and a response latency below 500 milliseconds, enabling safe, real-time operation. Experimental results demonstrated high detection accuracy and effective prioritization logic in four operational scenarios. Compared to traditional sensor-based systems, this approach is more cost-effective, scalable, and adaptable to real-world warehouse conditions. The novelty of this research lies in its integration of modular computer vision, decentralized microcontroller-based actuation, and intelligent traffic prioritization within a low-cost architecture"”features rarely combined in prior industrial safety solutions. Beyond warehouse environments, the proposed system is highly adaptable to other industrial settings such as factories, loading docks, and construction zones, where dynamic human"“machine interactions demand similar real-time visual monitoring and signaling. This work lays a foundation for smart industrial ecosystems, with future extensions toward IoT integration, predictive analytics, and reinforcement learning"“based decision-making.
Analysis of the Effect of Temperature on Performance Efficiency in Three Phase Transformers Nugraha, Yoga Tri; Pangestu, Adam; Wardani, Sumita; Irwanto, Muhammad; Pasaribu, Faisal Irsan; Evalina, Noorly
ELKHA : Jurnal Teknik Elektro Vol. 17 No.2 October 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i2.92879

Abstract

Transformers are essential components in electrical power distribution systems, and their performance is significantly influenced by operating temperature. High temperatures can lead to increased power losses, particularly copper losses, which reduce transformer efficiency. This research examines the impact of temperature on the efficiency of three-phase transformers, focusing on copper losses and the role of cooling systems in maintaining optimal performance. Using a combination of Thermovision infrared imaging and MATLAB simulations, this research introduces a novel integrated approach to correlate real-time thermal data with theoretical modeling of transformer losses. Unlike previous research that relies solely on either simulation or temperature sensors, the use of Thermovision provides spatially resolved, non-invasive temperature measurements that validate and enhance the accuracy of MATLAB-based thermal-electrical models. The results reveal that the operational temperature of 52.9 °C, as detected by Thermovision, is within safe limits; however, higher temperatures significantly decrease efficiency. The efficiency drops from 92.8% at 25 °C to 90.4% at 120 °C. The exponential trend in copper losses with temperature rise underscores the critical role of effective cooling and temperature monitoring systems. While the magnetic flux remains constant, maintaining lower operating temperatures is essential to prevent premature damage and extend transformer lifespan. Thermovision results were used to validate the simulations. Despite small discrepancies, the consistent pattern provides confidence that the simulation model is sufficiently accurate for performance prediction.
Evaluation of RSSI-Based Distance Estimation with ESP32 BLE Modules for Indoor Asset Tracking Al-Maktary, Omar; Susanto, Misfa; Mardiana, Mardiana
ELKHA : Jurnal Teknik Elektro Vol. 17 No.2 October 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i2.97739

Abstract

Bluetooth Low Energy (BLE) is a technology used for asset tracking, offering low power consumption and compatibility with embedded systems such as the ESP32. This paper evaluates the accuracy and reliability of Received Signal Strength Indicator based distance estimation using ESP32 BLE modules in three environmental conditions: clear line-of-sight, wall obstruction, and mobile tracking. It presents an empirical analysis of ESP32-specific RSSI limitations across these scenarios. The log-distance path loss model was employed, using a reference RSSI of -47 dBm at 1 meter and a path loss exponent of 2. Experiments were conducted with a BLE tag device (Asset_Tag_01) broadcasting BLE signals, while an ESP32 reader device collected RSSI data via Arduino IDE. Results indicate reliable estimation within 4 meters with under 25% error in line-of-sight conditions. However, beyond 5 meters, particularly in obstructed environments, RSSI values fluctuated significantly, causing distance overestimation. Wall obstructions resulted in an immediate 6 dBm signal degradation at just 1 meter. Packet loss increased from 0% at short distances to 50% at 8.5 meters. In mobile tracking, signal strength showed sudden jumps, complicating movement detection. These findings highlight that RSSI alone is not reliable for precise tracking. To improve accuracy, particularly in real-world settings like healthcare or industrial environments, further studies should explore advanced methods like Kalman filtering combining data from multiple sensors.
Optimal Hybrid Renewable Energy Integration for Reliable and Cost Efficient of Isolated System Ardhyantoro, Novan Iman; Husnayain, Faiz
ELKHA : Jurnal Teknik Elektro Vol. 17 No.2 October 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i2.93891

Abstract

Nusmapi Island is one of Indonesia's isolated islands that rely on Diesel Power Plant (PLTD) with a capacity of 20 kW to meet the electricity needs of its 50 customers. However, this PLTD only operates 12 hours a day due to high operational costs, reaching IDR 215,240,980/year with fuel consumption of 20,889 liters of diesel, equivalent to 17.56 tons with a COE of IDR 7,132/kWh in 2023. The reliance on diesel generators exacerbates ecological harm by releasing COâ‚‚ emissions"”a critical contradiction to Indonesia"™s nationally determined contribution (NDC) under the Paris Agreement, which mandates carbon neutrality by 2060. This research seeks to determine the most effective hybrid energy system design and configuration for Nusmapi Island, evaluating both technical feasibility and economic viability. The technical feasibility was assessed based on the unmet electric load, while the economic feasibility was evaluated using operational costs and the Cost of Energy (COE). The analysis indicates that the optimal system configuration comprises a 8,1-kW solar photovoltaic array, a 20-kW diesel generator, a 12-kW inverter, and five battery units housed within a single compartment. This configuration in real implementation will be able to produce 61,193 kWh/year, thereby increasing the power hours to 24 hours and reducing the unmet electric load to 0%. It will have a COE of IDR 3,280/kWh and will result in a fuel consumption reduction of 3,661 liters/year and operational costs of IDR 30,692,119/year. In addition, this configuration has environmental advantages with a renewable fraction reaching 18.3%
Mathematical Modeling of Solar Power Generation Systems with Cross-Flow Cooling Pipes Based on Fuzzy Inference Systems DA, Shazana; Tsalits, Askhaarina Aulia; Anshory, Izza; M, M. Syahrul; Jamaaluddin, Jamaaluddin; Darmansyah, Darmansyah
ELKHA : Jurnal Teknik Elektro Vol. 17 No.2 October 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i2.97228

Abstract

The utilization of solar energy in Indonesia remains relatively low despite its high potential in terms of solar irradiation and geographical advantage. One of the main challenges in photovoltaic (PV) systems, especially in tropical climates, is the decline in performance caused by high operating temperatures. Unlike previous studies that primarily focused on passive cooling methods or basic active cooling without intelligent control, this research introduces a cross-mounted water pipe cooling system integrated with a Fuzzy Inference System (FIS) for dynamic water flow regulation, enabling optimal temperature control and dual-output (electrical and thermal) efficiency. Experimental testing on two 100 Wp monocrystalline solar panels—one with cooling and one without—under identical conditions revealed that the cooled panel achieved an average maximum power of 85.2 W, compared to 43.6 W for the non-cooled panel, with efficiency improvements of up to 7.4% over the observation period. A linear regression model was developed to predict PV performance under varying temperature conditions, demonstrating a slower decline in efficiency and more stable power output in the cooled system. The proposed hybrid PV/T configuration effectively dissipates heat while simultaneously recovering thermal energy, thus enhancing total energy utilization. These results highlight the system’s capability to mitigate thermal degradation, extend module lifespan, and promote sustainable renewable energy adoption in tropical regions. The integration of intelligent control with thermal management presents a scalable and energy-efficient approach for future photovoltaic applications.
A Body Mass Index Measuring Tool with Ultrasonic Sensor and Load Cell (OBEMETER) rahman, Syaifur; Suryadi, Dedy; Aula, Abqori
ELKHA : Jurnal Teknik Elektro Vol. 17 No.2 October 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i2.92483

Abstract

Body Mass Index (BMI) is a parameter often used to evaluate the health status based on someone"™s height and weight ratio. However, the current existing BMI measurement and calculation are done manually. This research aims to develop an automatic BMI measuring tool called Obesity Meter (OBEmeter), by displaying the BMI score and body weight status. OBEMeter utilizes two sensors, namely ultrasonic (HC-SR04) to measure body height and load cell (HX711) to measure body weight. The use of ultrasonic sensors (HCSR04) and load cells (HX711) provides consistent height and weight measurements under various user conditions compared to other types of sensors. The height and weight data are then processed by Arduino in which the BMI calculation algorithm is already embedded. The BMI value is then converted into five-level body weight statuses, i.e. "ceking (underweight), kurus (slim), normal, kelebihan (overweight), and obese". This instrument is equipped with buzzer as an indicator if the BMI value is within the obese category. Our approach improves the measurement and calculation process by performing body and height measurement, then displaying the BMI in very short time automatically. Experimental results show that the reading accuracy of the ultrasonic sensor and load cell sensor are above 95%. Then, test results on 15 users show that the OBEmeter can produce the BMI measurement with 95,79% accuracy, which indicates that it can be utilized as a self-assist device to monitor the user"™s health status practically.
4G Network Optimization Based on Hybrid Clustering, Physical Tuning, and Genetic Algorithm Arridho, Rajwa Jilan; Sulistyawan, Vera Noviana
ELKHA : Jurnal Teknik Elektro Vol. 17 No.2 October 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i2.91704

Abstract

Optimization of resource allocation in fourth-generation cellular networks is critical to meet increasing demands for high data rates and low latency. The novelty of this research lies in the combination of network-spatial clustering with genetic algorithm-based physical tuning, which has not been jointly applied in the prior optimization of cellular networks. The clustering component partitions network zones based on spatial characteristics and traffic density, enabling localized parameter adjustment. The genetic algorithm performs physical tuning by iteratively selecting parameter sets that maximize network performance metrics. Experimental results demonstrate a significant enhancement in average data throughput, with observed increases of over twenty percent, and a reduction in latency by approximately twenty milliseconds compared to conventional tuning methods. These improvements translate into a more consistent user experience and better resource utilization under varying traffic conditions. The proposed approach also shows robustness across diverse urban scenarios, indicating its applicability to real-world deployments. By adapting to dynamic traffic patterns and environmental factors, the proposed solution ensures sustained network quality during peak demand and in challenging propagation environments. Future research will explore integration with machine learning"“based predictive models to further enhance tuning precision and proactive optimization. In conclusion, the hybrid network-spatial clustering and genetic algorithm"“based physical tuning method outperforms traditional optimization techniques by delivering higher performance gains and adaptability, offering a practical framework for enhancing fourth-generation network efficiency and laying the foundation for extending the methodology to emerging wireless standards.
Design and Implementation of a Kalman-Bucy Filter for Fault Detection in DC Motor Systems Mursyitah, Dian; Faizal, Ahmad; Safitri, Elfira; Pebriani, Sovi; Alfadri, Ramadhan
ELKHA : Jurnal Teknik Elektro Vol. 17 No.2 October 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i2.96853

Abstract

This study presents the design and implementation of a Kalman-Bucy filter for fault detection in DC motor systems, which are widely used in industrial drives and automation. Accurate state estimation is essential for ensuring reliable operation, particularly in the presence of measurement noise and parameter uncertainties. The proposed observer exhibits rapid convergence in speed estimation (less than one second) and strong robustness to measurement noise, achieving a Root Mean Square Error (RMSE) of 24.38 rad/s, closely matching the noise standard deviation (σᵥ = 23.01 rad/s). This close agreement indicates that the Kalman-Bucy filter operates near its theoretical optimal performance under Gaussian noise assumptions. Fault detection is carried out through residual analysis under three fault scenarios: ramp, inverse ramp, and square wave. Each scenario generates distinct residual patterns, providing clear indicators of both gradual and abrupt anomalies. Quantitative evaluation demonstrates high sensitivity (97.0% for ramp and inverse ramp, 94.1% for square), perfect specificity (100%), and a zero false alarm rate across all scenarios. These findings highlight the potential of the Kalman-Bucy filter as a reliable and computationally efficient approach for state estimation and fault indication using data representative of a real DC motor system. The results provide a valuable basis for developing predictive maintenance strategies and improving system reliability. Future work will focus on experimental implementation and validation to confirm its performance under real-world operating conditions.
Mitigating Class Imbalance in DDoS Detection: The Impact of Random Over Sampling on Machine Learning Performance Ghozi, Wildanil; Hussein, Jasim Nadheer; Sani, Ramadhan Rakhmat; Rafrastara, Fauzi Adi; Paramita, Cinantya; Supriyanto, Catur
ELKHA : Jurnal Teknik Elektro Vol. 17 No.2 October 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i2.95037

Abstract

Distributed Denial of Service (DDoS) attacks are a major cybersecurity threat, involving malicious traffic generated from numerous compromised sources to overwhelm and disable targeted services. Although machine learning (ML) has shown promise in detecting DDoS attacks through network traffic analysis, a key challenge remains: the class imbalance in datasets such as UNSW-NB15, where normal traffic significantly outweighs attack instances. This imbalance leads to biased predictions and degraded detection performance for minority attack classes. To address this issue, our study investigates the impact of Random Over Sampling (ROS), a simple yet effective balancing technique on improving detection accuracy in multi-class DDoS classification tasks. While prior works have primarily focused on ensemble algorithms or feature selection, our approach is distinct in emphasizing the effect of data balancing on macro evaluation metrics such as macro precision, macro recall, and macro F1-score. ROS was selected over more complex alternatives, such as SMOTE or ADASYN, due to its computational efficiency and ability to establish a performance baseline without introducing synthetic noise. We evaluate four machine learning algorithms: Decision Tree, Naïve Bayes, Random Forest, and XGBoost, using the UNSW-NB15 dataset. The results show that Decision Tree combined with ROS yields the highest improvement in macro F1-score, increasing by 36%. However, this improvement is accompanied by a moderate reduction in accuracy for certain algorithms. These findings highlight the critical role of class balancing in enhancing the reliability of DDoS detection models, especially in imbalanced multi-class scenarios.

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