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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 72 Documents
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Search results for , issue "Vol 12, No 6: December 2023" : 72 Documents clear
Adaptation of stochasticity into activation function of deep learning for stock price forecasting Patrick Vincent, Assunta Malar; Salleh, Hassilah
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.4987

Abstract

Stock market is an example of a stochastic environment in the real world. multilayer perceptron (MLP) is often applied to forecast stock price. However, it is widely used to approximate the input-output mapping deterministically. Hence, this study aims to adapt stochasticity into MLP by introducing the Gaussian process into the sigmoid activation function. In addition, the adapted activation function incorporates Roger-Satchell and Yang-Zhang volatity estimators. Besides, the stochastic activation function was considered as a hyperparameter by applying it either only in training time or in both testing and training time. The stochastic multilayer perceptron (S-MLP) is then applied to forecast one day's highest stock price of eight constituents in FTSE Bursa Malaysia KLCI (FBMKLCI). The result shows that the proposed network is inferior in comparison to MLP except for several constituents. In addition, S-MLP with stochastic activation function during both the training and testing time performs better compared to the presence of stochastic activation function in S-MLP during training time only.
Anti-windup modified proportional integral derivative controller for a rotary switched reluctance actuator Md Ghazaly, Mariam; Tee, Siau Ping; Zainal, Nasharuddin
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.6027

Abstract

Over the last decade, industrial applications and promising research domains including robotics and automotive engineering have adopted the rotary switched reluctance actuator (SRA). SRA's fault tolerance, simple, strong structure, and high-frequency operation make it popular. However, the SRA's nonlinear magnetic flux flow and saturation operation negate its benefits. Several control systems have been developed; however, they often need extensive mechanism models and advanced control theory, making them impracticable. This paper proposes a modified proportional integral derivative (PID) controller to evaluate the control performance, which comprises of PID controller with an anti-windup, a linearizer unit, and switching mechanism to activate the SRA phases. The linearizer unit aids to compensate for the nonlinear current-displacement relationship. The anti-windup element helps to halt the integral action during the starting motion. At the fully aligned position, 60°, the modified PID reduced positioning steady-state error by 4.3 times at 76.9%, overshoot by 48.8%, and settling time by 25.3%. Both the modified PID and conventional PID showed zero steady-state error at intermediate position, 70°, however the modified PID controller depicted an improved percentage overshoot by 54.5% and settling time by 74.5%. The results show that the modified PID outperforms conventional PID in transient response, steady-state error, overshoot, and settling time.
Jumping particle swarm optimization algorithm framework for content-based image retrieval system Bassel, Atheer; Jameel, Mohammed; Saad, Mohammed Ayad
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5024

Abstract

Content-based image retrieval (CBIR) has been studied well in the last decades in numerous research fields such as medicine, journalism, and private life. Applications of CBIR have been widely employed in medical images due to their direct impact on human life. With continues growing of digital libraries, there is a need for an efficient method to retrieve images from large datasets. In this paper, a new method was developed for CBIR based on the jumping particle swarm optimization (JPSO) algorithm. The proposed algorithm represents a developed instant of particle swarm optimization (PSO). However, JPSO the approach does not consider the velocity components to guide particle movements in the problem space. Instead of relying on inertia and velocity, intermittently random jumps (moves) occur from one solution to another within the discrete search space. To test the performance of the proposed algorithm, three types of medical image databases were used in the experiment which are the endoscopy 100, dental 100, and 50 skull image databases. The results show that the proposed algorithm could achieve high accuracy in image extraction and retrieve the accurate image category compared with other research works.
Complexity prediction model: a model for multi-object complexity in consideration to business uncertainty problems Syah, Rahmad B. Y.; Satria, Habib; Elveny, Marischa; K. M. Nasution, Mahyuddin
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5380

Abstract

In a competitive environment, the ability to rapidly and successfully scale up new business models is critical. However, research shows that many new business models fail. This research looks at hybrid methods for minimizing constraints and maximizing opportunities in large data sets by examining the multivariable that arise in user behavior. E-metric data is being used as assessment material. The analytical hierarchy process (AHP) is used in the multi-criteria decision making (MCDM) approach to identify problems, compile references, evaluate alternatives, and determine the best alternative. The multi-objectives genetic algorithm (MOGA) role analyzes and predicts data. The method is being implemented to expand the information base of the strategic planning process. This research examines business sustainability along two critical dimensions. First, consider the importance of economic, environmental, and social evaluation metrics. Second, the difficulty of gathering information will be used as a predictor for making long-term business decisions. The results show that by incorporating the complexity features of input optimization, uncertainty optimization, and output value optimization, the complexity prediction model (MPK) achieves an accuracy of 89%. So that it can be used to forecast future business needs by taking into account aspects of change and adaptive behavior toward the economy, environment, and social factors.
An improved transient performance boost converter using pseudo-current hysteresis control Boutaghlaline, Anas; El Khadiri, Karim; Tahiri, Ahmed
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5835

Abstract

This paper introduces an enhanced low transient voltage and fast transient response boost converter. It uses a hysteresis-controlled circuit fed by a voltage signal from a rail-to-rail current sensor, resulting in improved efficiency, and transient response. The converter is designed using Taiwan semiconductor manufacturing company (TSMC) 0.18 µm CMOS 1P6M technology, delivers an output voltage of 1.8 V while operating with an input voltage range of 0.5 V to 1 V and supports an output load current range of 10 to 100 mA. The key contributions of this paper are: i) introducing a new boost converter architecture employing pseudo-current hysteresis-controlled (PCHC) techniques, ii) incorporating voltage and current loops into the proposed architecture, and iii) demonstrating superior transient performance. Experimental measurements reveal a peak power efficiency of 98.6% at 10 mA and transient times of 15.4 µs and 11.8 µs for a step load change from 10 to 100 mA and back to 10 mA, respectively, with transient voltages of 51 mV. The presented boost converter outperforms in terms of performance, compared to previous works using the figure of merit (FOM) formula.
Indoor light energy harvesting technique to energize a heat sensor using polycrystalline solar panel Syazana Izzati Razali, Nur; Hajar Yusoff, Siti; Liza Tumeran, Nor; Sharir Fathullah Mohd Yunus, Muhammad
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5357

Abstract

This paper presents the effect of using different illumination types between the polycrystalline solar panel and the light sources on energy harvesting performance for indoor low-power applications such as heat sensors. The main advantage of indoor energy harvesting is it makes good use of ambient energy from the environment and converts it directly to electricity for small power devices. In this paper, the maximum power of polycrystalline solar panels for four different light illuminations has been investigated under different distances of light sources from the polycrystalline solar panel. Implementation and test results of the effect of varying the distance and the power produced for different light illuminations are presented which highlights the practical issues and limitations of the system.
Implementation of a P&E management system for a dual-source EV powered by different batteries Thakre, Mohan P.; C. Tapre, Pawan; Somnath Kadlag, Sunil; Prakash Kadam, Deepak; S. Thorat, Jayawant; N. Nandeshwar, Rahul; S. Gaikwad, Rohit
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.4873

Abstract

There is an era started in the field of power energy (PE) management in the electric vehicles (EVs) application cloud, which impacts the smart grid in significant ways and necessitates the collaboration of multiple branches of engineering. Most of the problems with EVs stem from their limited range, which can be improved by incorporating additional forms of energy storage. This paper makes an effort to bring a fresh viewpoint to the description of the power and energy management problem facing EV, taking into account all the needs of such a vehicle. Using a novel power and energy management system, the proposed methodology enables a systematic approach to this multidisciplinary problem. Implementing a power and energy management system for a dual-source EV using lead-acid batteries and ultra-capacitors (UCs) exemplifies the novel framework's capabilities. The electronic power development architecture is outlined in detail, along with the implementing modular blocks that make up the entire system architecture.
Integration of storage technology oversight: power system and computer engineering analogy P. Thakre, Mohan; M. Thakre, Pranali; C. Tapre, Pawan; S. Pawase, Ramesh; Somnath Kadlag, Sunil; Prakash Kadam, Deepak; N. Bhadane, Satish
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.4879

Abstract

Energy storage, analogous to data storage in a computer system, is one of the enabling technologies that has emerged alongside the widespread use of renewable energy sources in the nation's power grid. This article shows that the underlying platforms for storing data and energy are quite similar. Batteries and hydrogen storage offer significant energy potential, much like a hard disk for storing vast amounts of data in a computer's central processing unit (CPU). A supercapacitor or flywheel storage device can be used to have emergency power on hand, with access times as fast as random access memory (RAM) in modern computers. In this study, we propose an energy-control scheme for caches that is akin to computer engineering and is used to coordinate the operation of multilevel storage systems that incorporate both capacity and access-oriented storage. By supporting the energy-management system, which in turn provides modern plug-and-play functionality, cache energy control helps optimise the system as a whole. Such an integrated system calls for renewable energy generation, local loads, fueling stations, and connections to gas and electric distribution grids. Distribution energy concepts with various storage systems can be easily grasped by drawing parallels between computer engineering and power system integration.
The performance of Naïve Bayes, support vector machine, and logistic regression on Indonesia immigration sentiment analysis Assiroj, Priati; Kurnia, Asep; Alam, Sirojul
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5688

Abstract

In recent years various attempts have been made to automatically mine opinions and sentiments from natural language in online networking messages, news, and product review businesses. Sentiment analysis is needed as an effort to improve service performance in the organization. In this paper, we have explored the polarization of positive and negative sentiments using Twitter user reviews. Sentiment analysis is carried out using the Naïve Bayes (NB), support vector machine (SVM), and logistic regression (LR) model then compares the results of these three models. The results of the experiment showed that the accuracy of LR was better than SVM and NB, namely 77%, 76%, and 70%.
Predicating depression on Twitter using hybrid model BiLSTM-XGBOOST Kamil, Rula; Abbas, Ayad R.
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5416

Abstract

Nowadays, depression is a common mental illness. Failure to recognize depression early or guarantee that a depressed individual receives prompt counseling can lead to serious issues. Social media allow us to monitor people's thoughts, daily activities, and emotions, including persons with mental illnesses. This study suggested novel hybrid models that combine one of the deep learning techniques with one of the machine learning approaches. This paper used a dataset from the Kaggle website to predict depression. Two deep learning techniques were chosen to conduct the experiments: bidirectional long short-term memory (Bi-LSTM), and convolutional neural network (CNN). Three machine learning techniques were also selected, which are support vector machine (SVM), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBOOST). Deep learning methods were applied to extract important features from input data and training, and then machine learning was utilized to predict the class. The performance of the hybrid models was compared against that of five single models. The results showed that Bi-LSTM-XGBOOST is better than single models and achieve the highest performance, with 94% for all evaluation metrics. The proposed model can improve the performance of machine learning techniques and increase the detection rate of depression.

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