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Vita Lystianingrum
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JAREE (Journal on Advanced Research in Electrical Engineering)
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : -     EISSN : 25796216     DOI : https://doi.org/10.12962/j25796216.v4.i2.116
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
JAREE is an Open Access Journal published by the Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya – Indonesia. Published twice a year every April and October, JAREE welcomes research papers with topics including power and energy systems, telecommunications and signal processing, electronics, biomedical engineering, control systems engineering, as well as computing and information technology.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 187 Documents
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OPTIMAL DESIGN OF BLDC MOTOR USING ABO (AFRICAN BUFFALO OPTIMIZATION) ALGORITHM TO MINIMIZE COGGING TORQUE Irfan Ubaidillah; Feby Agung Pamuji; Hery Suryoatmojo
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 1 No. 10 (2026): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v1i10.546

Abstract

The use of BLDC motors in recent years has shown rapid development. This is motivated by efforts to reduce pollution with renewable energy. On the other hand, the advantages of BLDC motors have their own appeal so that they are widely applied not only in the automotive, health but also in the fields of aerospace and household appliances. The BLDC motor design that uses permanent magnets makes it produce good characteristics in power distribution, torque, and efficiency. However, it is not uncommon for the BLDC motor design to also create ripples in torque caused by cogging torque, causing unsmooth torque distribution, vibration and noise. The stator design in BLDC motors is the main factor causing the high cogging torque that occurs. This is due to the difference in flux density in the air gap and the interaction of magnets with the stator surface. This research focuses on reducing cogging torque by optimizing these parameters using the African Buffalo Optimization (ABO) method. The research was conducted by modeling the phenomenon of cogging torque through mathematical calculations and optimization through MATLAB. The optimized parameters are then simulated with MOTOR-CAD software to be analyzed and evaluated.
A 3x3 Phased Array Antenna for Ground Station Application Hidayah Hidayah; Gamantyo Hendrantoro; Wahyudi Hasbi; Fannush Shofi Akbar
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 1 No. 10 (2026): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v1i10.547

Abstract

The utilization of satellite technology has undergone a rapid expansion, particularly in the domain of Low Earth Orbit (LEO) satellites. The necessity of antennas at ground stations for the purposes of mission control and data communication is of paramount importance. However, the implementation of antennas with large reflectors is hindered by several factors, including their high manufacturing cost, operational maintenance requirements, and inherent complexity. Consequently, a solution is required to employ microstrip antennas that are more compact, cost-effective, and capable of automation in tracking LEO satellites. In order to facilitate communication with LEO satellites, it is imperative that the designed antenna exhibit high gain value, narrow bandwidth, and effective scanning capability. The proposed research delineates a uniform rectangular array design with half-wavelength spacing as the fundamental configuration. The array employs a cavity-supported patch antenna and an X-slot-shaped deformed ground structure, a configuration designed to enhance bandwidth and generate circular polarization. The 3x3 array employed in this study exhibits circular polarization with a gain level of 10.08 dB and a beamwidth of 66.18° at a frequency of 2.22 GHz. The antenna has been engineered for implementation at ground stations within the domain of satellite technology.
The Governor Predictive Controlled Based on LSTM for Optimizing Cofiring Power Generator Operation Addien Wahyu Wiranata; Dimas Anton Asfani; Daniar Fahmi
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 1 No. 10 (2026): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v1i10.549

Abstract

The renewable energy with cofiring technology has a significant impact on the use of Biomass. The use of biomass with different qualities greatly affects the performance of a plant. Deep Learning Time Series Forecasting is designed for predicting two control parameters cofiring powerplant operation consist of governor control and output generator. Long Short-Term Memory (LSTM) combined with Multilayer Perceptron, Convolutional, and Adaptive Moment Estimation (ADAM) optimizer algorithms are utilized to optimize the process governor control and predict generating power output. Correlation analysis is used to determine the input variables and resulting input parameters of governor control prediction consist of Temperature Steam, Pressure Steam, Output Generator, Coal Flow, Flow Steam. Moreover, the input variable for prediction generation power output are steam flow, steam temperature, coal flow, and steam pressure. The combination of Deep Learning Forecasting is successfully to predict both operation parameter percentage errors of 5.33%.
Life Cycle Cost Analysis of PV-Wind-Battery Hybrid Power System at PLN Nusantara Power Brantas Generation Unit Tulungagung Alfian Budiarmoko; Heri Suryoatmojo; Feby Agung Pamuji
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 1 No. 10 (2026): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v1i10.550

Abstract

Design and Simulation of a Micro-Inverter for Microgrid Applications Using a Recurrent Neural Network-Based Control Strategy Atsila Shalsabila; Feby Agung Pamuji
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 1 No. 10 (2026): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v1i10.554

Abstract

This study presents the design and simulation of a micro-inverter system for photovoltaic (PV) energy conversion in microgrid applications using a Recurrent Neural Network (RNN)-based Maximum Power Point Tracking (MPPT) control strategy. The objective is to enhance power conversion efficiency and system responsiveness under dynamic solar conditions. The proposed system integrates a boost converter, micro-inverter, LCL filter, and Phase Locked Loop (PLL) for grid synchronization. The RNN model is trained using variations of irradiance and PV electrical characteristics to determine the optimal duty cycle for controlling the converter. System modeling and simulations were conducted in MATLAB to assess performance. Simulation results demonstrate that the RNN-MPPT algorithm significantly improves the system’s ability to track the maximum power point accurately and rapidly, with improved output power stability under fluctuating irradiation. The boost converter and inverter responses confirm effective voltage regulation and reduced current ripple, while the overall system achieved high efficiency across different irradiance levels. These findings highlight the potential of AI-based control techniques in advancing renewable energy technologies and contribute to the development of intelligent energy management systems suitable for smart grid integration.
Battery Equalization Strategy Based On Cuk Converter Using Fuzzy-PI Control Bima Dwiki Prasetia; Dedet Candra Riawan; Vita Lystianingrum
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 1 No. 10 (2026): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v1i10.555

Abstract

Inconsistency in series-connected batteries is one of the factors that affect the performance while reduce lifetime battery. To ensure that the cells in a series battery pack have the same energy charge rate and maximize the overall full capacity of the battery, a battery balancing component is required. However, conventional balancing techniques that still use voltage as the control variable have a weakness, when the difference or voltage gap between batteries is small, the balancing current provided is also small, as a result this condition affects the balancing speed. To overcome deficiencies, This research proposes the design of a Cuk converter-based active balancing circuit with a bidirectional energy transfer mechanism using State of Charge (SOC) as a control variable with a Fuzzy Logic Controller (FLC) - Proportional Integral (PI) strategy. The test simulation results show that compared to the conventional technique, the proposed balancing topology is able to shorten balancing time significantly, guarantee safe balancing operation, and a dynamic balancing current regulation mechanism that is adaptive to battery conditions.
A Computer Vision Approach for Non-contact Psychophysiological Assessment: Speaking-Aware Video-Based Stress Detection Using Extended TSST Protocol Ahmad Rafiqan; Rachmad Setiawan; Tri Arief Sardjono
JAREE (Journal on Advanced Research in Electrical Engineering) Vol. 1 No. 10 (2026): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v1i10.556

Abstract

The assessment of psychological stress through non-contact computer vision methods faces significant challenges when facial expressions are affected by motions caused by speech. In this paper, we introduce a novel video-based stress detection system with speaking awareness that dynamically processes facial features according to online detection of speech activities. The system employs an extended 41-minute Trier Social Stress Test protocol with a controlled baseline, moderate stress induction, peak stress increase, and recovery stages to comprehensively record the dynamics of stress development. Facial landmark detection is handled by MediaPipe Face Mesh and provides 468 three-dimensional landmarks with sub-pixel accuracy, while the speaking-aware processing strategy dynamically selects the most appropriate feature sets: seven robust ones during speech-containing intervals like face movement, eye aspect ratio, and forehead wrinkles, and nine full-featured ones during silent intervals with additional mouth and jaw metrics. The adaptive strategy addresses the intrinsic limitation of traditional facial analysis, which treats speaking and non-speaking states homogeneously. Deployment on the Raspberry Pi CM4 edge computing device enables real-time operation with privacy preservation via localized processing. Empirical testing with 71 participants illustrates strong performance with an F1-score of 83.16%, outperforming conventional methods by 4.24%. Cross-population validation affirms good generalization potential across various populations for the entire 41-minute protocol, thereby demonstrating the system's applicability to real-world applications in stress tracking across healthcare, educational, and workplace well-being contexts.

Page 19 of 19 | Total Record : 187

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