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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 63 Documents
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Search results for , issue "Vol 33, No 1: January 2024" : 63 Documents clear
Optimization of speed droop governor operation at the gas turbine cogeneration unit Benriwati Maharmi; Ilham Cholid; Syafii Syafii; Engla Harda Arya
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp20-30

Abstract

Variations in customer demand for active power can impact frequency levels, potentially leading to instability within the electrical power system. To uphold system stability, it becomes essential to control the provision of active power to ensure the frequency remains consistent. This research aims to develop a simulation model for optimizing of the operation of the speed droop governor at the gas turbine cogeneration unit. This research used the quantitative method and descriptive statistical analysis techniques. The simulation model was employed as a simulator for operating the speed droop governor for frequency regulation in the electrical system. The gas turbine cogeneration unit 2 operational data of the speed droop values was used to analyze the influence of the generating unit’s response to changes in frequency. The analysis and simulation results revealed the gas turbine cogeneration unit 2 speed droop value of 4%, which was considered ideal for maintaining the stability of the 60 Hz nominal frequency required by customers.
Mathematical models for resolving the nonlinear formula for solar cell Mohammed Rasheed; Iqbal Alshalal; Arshad Abdula Ashed; Mohammed Abdelhadi Sarhan; Ahmed Shawki Jaber
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp653-660

Abstract

Accurate representation of a photovoltaic solar cell requires a comprehensive assessment of modeling factors that are unique to the individual device being studied. In the context of the single diode model, it is necessary to ascertain five distinct parameters, namely Rs, Rsh, Iph, Io, and n. In general, analytical or numerical methods may be used to calculate these values. In this paper, two alternative iterative approaches to solving nonlinear problems in solar cells without temperature are described and analyzed. The new iterative approach has several instances that have been quantitatively tested. This novel approach can be seen as a potential option for solving nonlinear equations. Additionally, a comparison between the suggested method, classic chord formula (CCM), and predictor-corrector type reveals that it is better and has the lowest evaluation. This is supported by an examination of accuracy and efficiency (as evaluated by function evaluations) false position method (FPM).
Stereo object matching for mobile robot path planning using artificial fish algorithms Andi Besse Firdausiah Mansur
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp661-670

Abstract

The popularity of robots is on the rise, not only in industrial settings but increasingly in daily venues such as airports. Recently, some organizations have carried out experiments utilizing robots specifically created to improve airport hygiene, security, and passengers’ overall satisfaction. Furthermore, the utilization of the artificial fish (AFs) algorithm in path planning for mobile robots yielded exceptional outcomes. The robot can replicate the prey behavior of the AFs algorithm, as evidenced by the prevalence of pos one in the simulation. The robot exhibits another behavior, which is the subsequent behavior. The behavior of the AFs algorithm is influenced by the available food sources. Simultaneously, mobile robots are influenced by the stimulation of their neighboring responses. Afterwards, the three primary classifiers are employed to perform stereo-object matching on different objects. The recognition rate achieved by the AdaBoost classifier is promising, with an accuracy rate of 92.4%. This result shows excellent potential for improving the path planning of mobile robots equipped with visual surveillance systems for their surroundings.
A hybrid spectral-spatial fusion technique for hyperspectral object classification Radhakrishna Mani; Manjunatha Raguttapalli Chowdareddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp361-369

Abstract

In the field of object classification, hyperspectral imaging (HSI) has been widely used, due to its spectral-spatial, and temporal resolution of larger areas. The HSI is generally used to identify the objects physical properties in accurate manner and as well as to identify similar object with acceptable spectral signatures. Thus, the HSI has been widely used for object identification applications in different fields such as precision agriculture, environmental study, crop monitoring, and surveillance. However, the object classification is time consuming due to extremely large size; thus, the feature fusion of both spectral and spatial have been done. The current feature fusion method fails to retain semantic object intrinsic feature; further, current classification technique induces higher misclassification. In addressing the research issues this paper introduces a hybrid spectral-spatial fusion (HSSF) technique to reduce feature size and retains object intrinsic properties. Finally, in reducing misclassification a soft-margins kernel is introduced in support vector machine (SVM). Experiment is conducted on standard Indian Pines dataset; the result shows the HSSF-SVM model attain much higher accuracy and Kappa coefficient performance.
Power flow analysis in a distributed network for a smart grid system Thangavel Jothi; Manoharan Arun; Murugesan Varadarajan
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp42-52

Abstract

This article presents the implementation of a hybrid renewable energy-based smart grid in a distributed system. Photovoltaic (PV) and wind generation are variable and time-dependent, yet they are very efficient and correlated, making them perfect for a two-source hybrid system. To maximize the generated power, using the maximum power point tracker (MPPT) technique, the incremental conductance (IC) algorithm is employed. The proportional integral (PI)-based MPPT controller is chosen to improve the efficiency of conventional MPPT controllers. A battery system is implemented as an energy management system (EMS) to aid in transferring or managing the high load throughout peak and off-peak hours. The proposed system uses an optimization technique called genetic algorithm (GA) to control the inverter voltage. The GA-tuned PI controller performs efficiently and has less harmonic distortion than the traditional sinusoidal pulse width modulation (SPWM) control method. The designed system uses real-time measurable parameters as inputs and is simulated in Matlabtool. The system generates 42 kW of solar power and 250 kW of wind power; the total harmonic distortion (THD) value is 5% less than the SPWM technique. For future work, flexible alternating current transmission system (FACTS) devices can improve the power quality and lower the oscillations.
A rest tremor detection system based on internet of thing technology Safira Faizah; Dian Nugraha; Mohammed N. Abdulrazaq; Brainvendra Widi Dionova; Muhammad Irsyad Abdullah; Leni Novianti
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp476-484

Abstract

This article outlines the creation of a health detection system designed to identify rest tremors in Parkinson's disease (PD). The system leverages internet of things (IoT) technology to measure frequencies derived from human activities, excluding other symptoms such as heartbeat and voice recording. The core components include the Arduino Nano microcontroller and the Node ESP MCU 8266 V3 for data processing. The system employs an accelerometer sensor positioned at the body's center axis to gauge the frequency of motor symptoms associated with resting tremors, particularly when the hands are at rest in the lap. The findings indicate that 9 samples displayed symptoms of rest tremor. The recorded p-value, standing at 0.884, signifies a robust correlation between the two variables at a significant threshold of 0.01 or 1%.
Review of learning management systems: history, types, advantages, and challenges Fahad Taha Al-Dhief; Ali Al Nasser; Shafazawana Mohamed Tharikh; Hassan Al Nasser; Ali Abdul Ghaffar Al-Mosleh; Musatafa Abbas Abbood Albadr; Majid Razaq Mohamed Alsemawi
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp350-360

Abstract

As technological advancements continue in the higher education domain, the systems of higher learning management in universities and institutions have witnessed significant attention from researchers and developers. The technologies of these systems can present extremely useful tools and provide many advantages to enhance learning and increase the students’ performance. However, there are some issues and challenges that face these systems and need to be highlighted. Moreover, there is a need to review the advantages of such systems in order to motivate other countries to adopt, use, and develop the learning systems. Therefore, this paper gives an overview of the learning management systems (LMSs) in universities and institutes. Furthermore, it presents the history of LMSs, usages, systems types, advantages, challenges and issues.
A modified type of Fletcher-Reeves conjugate gradient method with its global convergence Amna Weis Mohammed Ahmad Idress; Osman Omer Osman Yousif; Abdulgader Zaid Almaymuni; Awad Abdelrahman Abdalla Mohammed; Mohammed A. Saleh; Nafisa A. Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp425-432

Abstract

The conjugate gradient methods are one of the most important techniques used to address problems involving minimization or maximization, especially nonlinear optimization problems with no constraints at all. That is because of their simplicity and low memory needed. They can be applied in many areas, such as economics, engineering, neural networks, image restoration, machine learning, and deep learning. The convergence of Fletcher-Reeves (FR) conjugate gradient method has been established under both exact and strong Wolfe line searches. However, it is performance in practice is poor. In this paper, to get good numerical performance from the FR method, a little modification is done. The global convergence of the modified version has been established for general nonlinear functions. Preliminary numerical results show that the modified method is very efficient in terms of number of iterations and CPU time.
A review of wireless body area networks Wan Haszerila Wan Hassan; Darmawaty Mohd Ali; Juwita Mohd Sultan; Murizah Kassim
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp167-178

Abstract

The widespread adoption of wireless body area network (WBAN) in healthcare presents opportunities to meet the increasing demand for medical services. WBAN enables continuous real-time (RT) monitoring through biomedical sensor nodes positioned in or around a patient's body, collecting vital physiological data. In addition, WBAN imposes stringent criteria for energy efficiency and reliability throughout data collection and transmission. This review paper places significant emphasis on the fundamental concept and essential characteristics of WBAN technology. First, the WBAN features, including architecture, sensor nodes types and network topology is presented. Then, the study explores a wide variety of communication standards and multiple access (MA) mechanism deployed in this technology. Moreover, it discusses open research and challenging issues such as heterogeneous traffic, quality of services (QoS), energy consumption, reliability, interference management, and human body movements effect. Finally, the paper is concluded, and future directions are identified in this evolving field of human health monitoring technology.
A proposed model for detecting defects in software projects Alia Nabil Mahmoud; Ahmed Abdelaziz; Vitor Santos; Mario M. Freire
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp290-302

Abstract

Defective modules that cause software execution failures are common in large software projects. Source code for a significant number of modules may be found in several software repositories. This software repository includes each module’s software metrics and the module’s faulty status. Software companies face a considerable problem detecting defects in sizeable and complex programming code. In addition, many international reports, such as the comprehensive human appraisal for originating (CHAOS) report, have mentioned that there are countless reasons for the failure of software projects, including the inability to detect errors and defects in the programming code of those projects at an early stage. This research employs a statistical analysis technique to reveal the characteristics that indicate the faulty status of software modules. It is recommended that statistical analysis models derived from the retrieved information be merged with existing project metrics and bug data to improve prediction. When all algorithms are merged with weighted votes, the results indicate enhanced prediction abilities. The proposed statistical analysis outperforms the state-of-the-art method (association rule, decision tree, Naive Bayes, and neural network) in terms of accuracy by 9.1%, 10.3%, 13.1%, and 13.1%, respectively.

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