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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.
Arjuna Subject : -
Articles 380 Documents
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An Ensemble-Based Method for DDoS Attack Detection in Internet of Things Network Adedeji, Kazeem B.; Owojori, Adedotun O.; Shabangu, Thabane H.
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 2 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i2.1768

Abstract

This paper proposes an ensemble-based framework for Distributed Denial of Service (DDoS) attack detection in internet of thing (IoT) network. A combination of k-NN, MLP, and DNN classifiers are used to make an ensemble framework. Final predictions are determined by a weighted voting where weighted outputs of the best two classifiers are used. Experiments are executed on two recent IoT datasets: ToN-IoT and IoT23 datasets. To improve the classification accuracy, the datasets are subjected to a variety of pre-processing approaches and feature selection processes. The feature selection is handled through the combination of the Pearson correlation coefficient, entropy and mutual information to avoid redundant data and obtain improved feature sets. In comparison to other relevant research utilizing the same dataset, experimental results demonstrate that the ensemble method achieved more satisfactory results in terms of accuracy, precision, and the receiver operative characteristic (ROC) curve and provided a considerable improvement. The ensemble based model records a detection accuracy of 99.998% and ROC of 99.995% which shows that its ability to classify attack cases from benign is superb. The effect of feature selection methods on the performance of the ensemble model is also investigated and discussed.
Implementation of Feature Extraction Using BERT in Aspect Based Sentiment Analysis Turangan, Andreas Dwi Putra; Jacobus, Agustinus; Kambey, Feisy Diane
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 2 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i2.1770

Abstract

Aspect Based Sentiment Analysis (ABSA) is a sentiment analysis technique that not only identifies overall sentiment, but also reveals opinions on specific aspects of an entity. To facilitate computer processing, a numerical representation of words into vectors (word embedding) is used, where each word or phrase is mapped into a vector of dimension N. Although static embedding such as Word2Vec or GloVe has been widely used, these approaches have limitations in capturing the dynamic context essential for deep sentiment analysis. This research develops and tests several deep learning algorithms, namely CNN, Bi-LSTM, CNN+BiLSTM, and CNN+BiLSTM+Attention Mechanism, which initially use static embedding and then modified by integrating BERT as contextual embedding. The results show that the use of BERT improves sentiment prediction accuracy by 15% and aspect prediction accuracy by 11% compared to models with static embedding. In particular, the combination of BERT+CNN obtained the best accuracy, which was 94% for aspect prediction and the combination of BERT+CNN+BiLSTM+Attention Mechanism 87% for sentiment prediction. These findings demonstrate the significant potential of BERT integration in improving ABSA performance, which can be applied in social media opinion analysis and sentiment-based recommendation systems.
Design and Simulation of Vienna Rectifier for Harmonic Reduction in Electric Vehicle Charging Systems Ahmad Ramadhan; Taufik Iqbal Miftaks; Nihayatun Nafisah; Bontor Panjaitan
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 2 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i2.1772

Abstract

The development of electric-powered transportation has increased demand for electric vehicle chargers. Most chargers used are DC chargers since energy storage in electric vehicles relies on batteries. Therefore, a rectifier is required to convert AC power sources into DC. This study aims to develop a Vienna Rectifier for electric vehicle charging systems to achieve optimal power quality. The main focus is to enhance efficiency and power quality by implementing a PID control system to reduce Total Harmonic Distortion (THD) and improve the power factor. Simulations are conducted using MATLAB Simulink with the Clarke Transformation method to optimize voltage and current control. The simulation results show that the designed Vienna Rectifier achieves a THD value below 5%, in compliance with the IEEE-519 standard, with a power factor close to 1. This demonstrates that the application of PID control in the Vienna Rectifier can improve the efficiency of electric vehicle charging while reducing the negative impact of harmonics on the power grid.
Stochastic Load Modeling and Chance-Constrained Optimization of DG Placement and Sizing with Emission Incentives Bustanuddin, Ian Adrian; Syafaruddin; Said, Sri Mawar
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 2 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i2.1788

Abstract

This study proposes an optimization model for determining the placement and sizing of photovoltaic distributed generation (PV-DG) units in electrical distribution systems, accounting for load uncertainty and PV output variability. The model is formulated as a Mixed Integer Linear Programming (MILP) problem that incorporates stochastic load modeling based on normal distribution, and applies a chance-constrained programming approach to ensure supply reliability with a specified confidence level. Additionally, a clean energy-based incentive scheme is integrated into the objective function to enhance the economic feasibility of PV investments. Simulation results under various stochastic scenarios demonstrate that the system can reliably meet all load demands without supply deficits. PV contributions are particularly significant during daytime hours, leading to a noticeable reduction in overall system costs and carbon emissions. The proposed approach is proven effective in delivering adaptive, economical, and reliable solutions under distribution system uncertainty, while also highlighting the role of output-based incentives as a policy tool to accelerate the transition toward clean energy.
DE-Optimized Hybrid ARIMA-LSTM for Long Term Electricity Load Forecasting Mardotillah, Nanda Azizah; Hasanah, Rini Nur; Wijono
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 2 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i2.1813

Abstract

Accurate long-term electricity load forecasting was essential for efficient energy planning and infrastructure development. This study addressed forecasting challenges in rapidly growing regions, such as East Java, where electricity demand was influenced by both linear and non-linear patterns. Conventional forecasting models, such as the Autoregressive Integrated Moving Average (ARIMA) effectively captured linear trends but failed to model non-linear dynamics, whereas networks with Long Short-Term Memory (LSTM) excelled with non-linear data but were often less effective when used alone. This research developed and evaluated an ARIMA-LSTM hybrid model optimized with the Differential Evolution (DE) algorithm to forecast electricity load until 2026. The model was trained and validated using historical daily load data from 2021 to 2023 from PT PLN UP2B East Java. This hybrid methodology first used ARIMA to model the linear components of the time series. The resulting residual errors, which contained non-linear patterns, were then modeled using an LSTM network. The DE algorithm was used to automatically optimize hyperparameters for both the ARIMA (p, d, q) and LSTM (units, learning rate, drop out etc.) components. The suggested hybrid model's performance was contrasted with that of the independent LSTM and ARIMA models. The results showed that the DE-optimized hybrid model achieved higher accuracy, yielding a Mean Absolute Percentage Error (MAPE) of 3.97 %, which was significantly better than the ARIMA model (12.39 % MAPE) and the LSTM model (4.50 % MAPE) on the validation set. According to these results, the suggested hybrid model was a dependable and extremely accurate instrument for predicting long-term loads, offering a solid basis for strategic energy planning.
Design of a Capacitive Level Sensor for Nitroglycerin Synthesis A’inul Yaqin, Sa’id; Mochammad Rusli; Bambang Siswojo; Taufik
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 2 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v19i2.1822

Abstract

The synthesis of nitroglycerin requires extremely precise control of reactant volumes due to its highly sensitive and hazardous nature. This study evaluates the performance of a capacitive level sensor integrated with a Fuzzy-PID controller capable of adapting to error dynamics while utilizing an ESP32 microcontroller and a peristaltic pump. The sensor was tested for accuracy, precision, linearity, resolution, response time, hysteresis, stability, and sensitivity. The results demonstrated an accuracy of 99.4%, an R² value of 0.997, a resolution of 0.1 mL, and a response time of 4.98 seconds. The system exhibited high precision and stable performance, offering a potential solution for enhancing the reliability and safety of nitroglycerin production.
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.
Human Detection Aerial Imagery Using Convolutional Neural Network Method Pangemanan, Christofel; Mudjirahardjo, Panca
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
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

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