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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 8 Documents
 1 
Search results for , issue "Vol. 19 No. 2 (2025)" : 8 Documents clear
Stabilization of a cart inverted pendulum system using LQR, Partial Feedback Linearization and LQG control Fitria, Nur; Rusli, Mochammad; Yudaningtyas, Erni
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.v19i2.1760

Abstract

Controlling of a cart inverted pendulum often becomes learning material for a control system. Several researchers have carried out about stabilization of inverted pendulum using numerous methodologies. However, the stabilization of the pendulum has just been conducted in the inverted area. The aim of this research was designing a control system to raise the pendulum from the lower area to the inverted area using Linear Quadratic Regulator (LQR) and Partial Feedback Linearization control in the Simulink MATLAB. Furthermore, the design was given a noise so that Linear Quadratic Gaussian (LQG) control was needed to make this system more robust. The model of cart pendulum with LQR control alone was unable to raise the pendulum from 0 degree to reach the angle at 180 degrees in an inverted area although the gain K and the voltage of DC motor were increased. For this reason, the Partial Feedback Linearization method was added to raise the pendulum by controlling the energy and slide mode on the cart. The strengthening of the system with the LQG design was carried out to reduce the noise signal that occurs in the DC Motor and sensor. The design was implemented in the simulation using Simulink MATLAB. It shows that the design of LQR and Partial Feedback Linearization control can make the pendulum stable at 180 degrees in 5.2 seconds and the cart is stable at 0 cm in 4.5 seconds. Whereas, the design of LQG control can reduce the noise well in the system.
Enhanced WKNN Indoor Positioning Algorithm with Dynamic AP Selection Strategy Isa, Abdurrhaman Ademola; Jimoh, Akanni; Abdulrahman, Amuda Yusuf; Alao, Atanda Rasaq; Omore, Abdufatai
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.v19i2.1761

Abstract

Indoor positioning systems (IPS) have become indispensable in modern smart environments, where Global Positioning Systems (GPS) fail due to signal obstruction. This study introduces an enhanced Weighted K-Nearest Neighbor (WKNN) algorithm incorporating a Dynamic Access Point Selection (DAPS) strategy to improve accuracy and reliability in indoor settings. By intelligently filtering access points (APs) based on signal quality and spatial relevance, the proposed method mitigates the effects of noisy signals and optimizes AP utilization. Experimental evaluations in real-world environments demonstrate that the DAPS-enhanced WKNN achieves up to 30% higher positioning accuracy compared to traditional WKNN methods, reducing average positioning errors to 1–2 meters. The findings highlight the algorithm’s potential in diverse applications such as healthcare, retail, and smart buildings. This research paves the way for scalable and cost-effective IPS solutions, emphasizing adaptability to varying environmental complexities.
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.

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