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Jurnal EECCIS
Published by Universitas Brawijaya
ISSN : 19783345     EISSN : 24608122     DOI : -
Core Subject : Engineering,
Engineering
EECCIS is a scientific journal published every six month by electrical Department faculty of Engineering Brawijaya University. The Journal itself is specialized, i.e. the topics of articles cover electrical power, electronics, control, telecommunication, informatics and system engineering. The languages used in this journal are Bahasa Indonesia and English.
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Articles 11 Documents
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Search results for , issue "Vol. 19 No. 3 (2025)" : 11 Documents clear
A Review of the Development of Power Converters and Controls for Fast Charging Electric Vehicles Wahono, Tri; Tole Sutikno; Ardiansyah; Hendril Satrian Purnama
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i3.1776

Abstract

The review explores the development of advanced power converter topologies and control strategies for high-power fast-charging systems in electric vehicles (EVs). Fast-charging systems are crucial for EV charging infrastructure, requiring intricate design considerations and optimization procedures. Understanding hierarchical control systems and exploring power converter topologies and control technologies is essential for efficient and reliable charging. EV fast-charging systems evaluate the effectiveness of various topologies with power converters, optimizing efficiency and performance. Control strategies are crucial for the efficient and sustainable operation of fast-charging infrastructure for EVs. Integrating these strategies into fast-charging systems improves charging efficiency and grid stability, contributing to the sustainable development of EV technologies. In conclusion, the review of power converters and controls for fast-charging electric vehicles is crucial for sustainable transportation infrastructure. Fast charging technology for EVs is advancing rapidly, enhancing charging performance and promoting sustainable transportation. Innovative charger designs and control techniques are crucial for efficient charging. However, the review acknowledges limitations due to the evolving landscape of fast-charging infrastructure and the complexities of grid integration. Future research should focus on overcoming challenges, advancing technologies, and integrating renewable energy sources, energy storage solutions, and grid connectivity. This will contribute to the widespread adoption of electric mobility.
Enhancing Human Detection In Aerial Imagery Using CNN With Data Augmentation Pangemanan, Christofel; Mudjirahardjo, Panca; FX. Arinto Setyawan
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i3.1811

Abstract

In disaster scenarios, accurate and efficient human detection is essential to support timely search and rescue operations. This study explores the performance of deep learning models YOLOv8n for human detection using datasets before and after data augmentation. The evaluation focuses on key metrics including Precision, Recall, F1-score, inference time, and mean Average Precision (mAP). Experimental results indicate that the model trained on the original dataset achieves Precision (0.9623), Recall (0.9464), and F1-score (0.9357), highlighting better accuracy in minimizing false positives and false negatives. Conversely, the augmented dataset leads to improvements in mAP (95.8 vs. 94.5) and inference speed (8.2 ms vs. 9 ms), demonstrating increased robustness and efficiency. These findings suggest that while training on unaugmented data slightly better detection accuracy, data augmentation enhances the model's overall performance, speed and perform well to detect object in occluction scenario, making the YOLOv8n model more suitable for real-time usage in disaster response scenarios.
Fuzzy Tahani Model for Selection Journal Computer Sciences and Technology (Sinta 2): Across Indonesia Wantoro, Agus; Hari Soetanto; Dikpride Despa; Tahta Herdian Andika
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i3.1787

Abstract

Publication of scientific articles in the national journal Science and Technology (Sinta) has become an obligation for academics such as students and lecturers. For students, publication is one of the requirements for graduation. For lecturers, scientific publication is a requirement for promotion or academic level (JA). Several levels of JA are Assistant Expert, Lecturer, Senior Lecturer and Professor. Lecturers who will apply for promotion to Senior Lecturer are required to publish in the Sinta 2 Journal. To be able to find a suitable journal, there are many considerations, if you choose the wrong journal, the article can be rejected. In choosing a journal, authors generally look at information such as publication costs, number of articles published, number of article publication frequencies, and review process time. Sometimes authors need ambiguous information such as the number of articles with many categories, low costs, high publication frequencies, and fast review times. The approach to this problem can use the Tahani fuzzy model database. This study applies the Tahani fuzzy model to model Sinta 2 journal data in the computer field to provide new findings and facilitate the selection of journals that match the criteria. This research needs to be done to provide useful information for several parties. For the author, this research is useful as a reference for selecting journals according to the criteria sought. For journal managers or editors, this information can increase the number of articles to be published, and for further researchers, this research will be information and reference material regarding journal selection research
A Digital Temperature Control Design for High Quality Virgin Coconut Oil Production Sukowati, Azizah Dian; Yudaningtyas, Erni; Sulistiyanto, Nanang; Milala, Ebenezer
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i3.1825

Abstract

Virgin Coconut Oil (VCO) has health benefits because it contains compounds that are good for the body. This study aims to control the temperature in the coconut milk extraction chamber into VCO to maintain the stability of these fatty acids. With a setpoint temperature of 34°C, PI and PID controllers are used whose parameters are determined through the Ziegler-Nichols and Cohen-Coon tuning methods. This research was modeled and simulated in MATLAB. The results show that the PID controller with the Cohen-Coon method provides a better system response compared to Ziegler-Nichols, with a settling time of 8757.4 seconds, an overshoot of 9.6518%, and a steady state error of 0%. Meanwhile, the PI controller with the Cohen-Coon method has a settling time of 8901.7 seconds, an overshoot of 7.1398%, and a steady state error of 0%. Tests show that the system can achieve overshoot below 3 and steady-state error below 1, according to the expected specifications
A Systematic Literature Review on Machine Learning Techniques for Enhancing Embedded Hardware Reliability Desy Natalia; Cahya Renita Pulse; Rizal Ramadhan; Rama Fahrizal Kusuma; Rizky Ajie Aprilianto; Feddy Setio Pribadi
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i3.1790

Abstract

Embedded systems (ES) have played a vital role in industrial automation and critical infrastructure, but their reliability has often been compromised by hardware faults, leading to downtime and safety concerns. Traditional threshold-based fault detection methods have frequently failed to adapt to dynamic environments and have struggled to identify early-stage failures. This study reviewed the effectiveness of artificial intelligence (AI), specifically machine learning (ML) models, for fault detection in ES. A systematic review methodology was employed to analyze the diagnostic performance of several deep learning (DL) architectures, including hybrid convolutional neural network-long short-term memory (CNN-LSTM) models, when implemented on resource-constrained edge devices. The results showed that optimized AI models achieved higher diagnostic accuracy and earlier fault identification compared to conventional approaches. Furthermore, these models enabled real-time, energy-efficient operation on platforms such as Raspberry Pi and ESP32 microcontrollers. It was concluded that AI-driven solutions significantly enhanced predictive maintenance and operational reliability in embedded system applications, demonstrating their transformative potential for future industrial systems.
Monitoring and Controlling System for Ammonia and Methane Gas in Broiler Chicken Farms Using Fuzzy Mamdani-Based Hybrid Junus, Mochammad; Saptono, Rachmad; Putri Nabila, Anggraeni
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i3.1805

Abstract

The broiler poultry industry significantly contributes to food security by supplying animal protein; however, it also generates harmful gases such as ammonia (NH?) and methane (CH?) from accumulated waste. These gases not only endanger poultry health but also contribute to environmental pollution and climate change. This research proposes the development of an Internet of Things (IoT)-based monitoring and control system for ammonia and methane gas levels in broiler chicken farms. The system employs MEMS NH? and MQ4 gas sensors integrated with an ESP32 microcontroller, and applies the Mamdani fuzzy logic method to classify gas levels into safe, unhealthy, or dangerous categories. Based on the fuzzy output, a water pump powered by a hybrid solar energy system is activated automatically to reduce gas concentrations. Data is transmitted in real-time to a Firebase database and can be accessed via an Android application supporting both manual and automatic control modes. Experimental results demonstrate the system's effectiveness in detecting gas levels accurately and responding efficiently to maintain a healthy farm environment while utilizing renewable energy sources.
Smart Biogas Control for Communities Using Gaussian Naïve Bayes Junus, Mochammad; Koesmarjanto; Ria Amanda Salsabella
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i3.1808

Abstract

The design and implementation of an intelligent biogas quality monitoring and control system that combines machine learning, actuator automation, and Internet of Things (IoT) technology is presented in this research. The system uses a thermocouple type K, MPX5700, MQ-4, and MQ-135, among other environmental sensors, to measure temperature, pressure, CO?, and CH? in real time. An ESP32 microcontroller processes sensor data using the Gaussian Naïve Bayes algorithm to categorize biogas quality into three classification, namely Good, Moderate, and Poor. A servo motor is utilized to control a valve that either permits or prohibits the flow of biogas to a generator based on the classification output. Through the Blynk IoT platform, the system has the capacity to be remotely monitored. Results from experiments with 40 biogas data demonstrated that the system had good precision and recall in each category and an overall accuracy of 92.5%. The approach exhibits dependability, affordability, and suitability for community-based biogas management in rural and semi-urban evironments.
Design of a Monitoring System and Automatic Power Circuit Breaker in Flood Prone Areas Based on the Internet of Things Ferdyanto; Wildan Hakim; Muhamad Alif Razi; Muhammad Raihan Fadhlurrahman
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i3.1879

Abstract

Flooding is a natural disaster that occurs due to overflowing water and can cause electrical short circuits in residential areas, endangering the safety of residents and damaging electronic devices. This study aims to design an automatic system for breaking and connecting electrical currents based on the Internet of Things (IoT) that is able to respond to water levels in real-time. The system uses an HC-SR04 ultrasonic sensor and an analog water level sensor connected to an ESP32 microcontroller. When the water reaches a certain limit, the ESP32 will activate the servo motor to cut off the electricity flow to the MCB and send a notification via Telegram. The method used is Research and Development (R&D), including hardware and software design, sensor integration, and system testing. The test results show that the system has a detection accuracy of 98% and is able to cut off or connect the current in less than 2 seconds. Notification delivery runs 100% on a stable network. This system is effective in increasing electrical safety during floods.
Design and Simulation of a 12-Pulse D-STATCOM for Voltage Sag Mitigation and Power Quality Improvement in Unbalanced Distribution Systems Masdi, Hendri; Hasanah, Rini Nur; Bin Abdul Wahab , Noor Izzri; Husin, Muhammad
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i3.1937

Abstract

The research introduces a 12-pulse D-STATCOM model developed and simulated to address voltage sag mitigation and power quality enhancement in unbalanced distribution feeders. It tackles critical power quality problems such as voltage depressions, harmonic disturbances, and declining power factor, all of which negatively influence system reliability and efficiency. The 12-pulse arrangement was realized by integrating two 6-pulse Voltage Source Converters (VSCs) through a phase-shifted transformer, which enabled the suppression of dominant low-order harmonics. The overall system was constructed and analyzed in MATLAB/Simulink, where an SRF-based control method was employed to ensure precise reactive power support and rapid voltage restoration. The simulation outcomes indicate notable performance gains: THD decreased from 8.5% in the conventional 6-pulse unit to 3.2%, the power factor increased from 0.85 to 0.98, and the network voltage recovered within approximately 0.02 seconds following a 30% voltage dip. These findings demonstrated that the 12-pulse D-STATCOM provided superior harmonic mitigation, faster dynamic response, and improved system stability compared to the conventional 6-pulse design. The novelty of this study lies in proving that the 12-pulse configuration offers a technically practical yet practically feasible alternative, making it highly relevant for real-world power distribution applications.
Implementation of Semantic Similarity for Book Search in Digital Library Daffa, M. Royhan; Yaqin, M. Ainul
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i3.1796

Abstract

This study discusses improving book search relevance in digital libraries by implementing semantic similarity. This study aims to improve search relevance and contextual understanding by integrating the TF-IDF method. This process includes text preprocessing, TF-IDF weighting, and semantic similarity calculation using the Wu-Palmer method with the aid of WordNet. Testing was carried out with 25 different queries on 1000 digital books in five scenarios. The test results show: (1) The 3-term TF-IDF scenario produces the highest average similarity, ranging from 71.83% (1-word query) to 40.08% (5-word query or more), with a standard deviation increasing from 15.82% to 28.76%; (2) The 5-term TF-IDF scenario shows an average similarity from 61.36% to 35.69% and a standard deviation from 19.91% to 28.64%; (3) The 10-term TF-IDF scenario provides an average similarity of 53.6% to 31.77%, with a relatively stable standard deviation in the range of 27-28%; (4) The scenario without TF-IDF has the lowest average similarity, 42.14% to 25.6%, with a standard deviation decreasing from 30.31% to 27.13%; (5) The non-semantic scenario yields the highest average similarity of 80% (1-word query) and decreases to 38.25% for long queries, with no standard deviation due to the absence of score variation. This study proves that the combination of semantic similarity with the top 3 TF-IDF terms is an optimal approach in increasing the relevance of search results compared to other scenarios, especially for short to medium queries. These results contribute to developing a more effective and contextual digital library search system.

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