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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 2,901 Documents
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Betel leaf classification using color-texture features and machine learning approach Novianti Puspitasari; Anindita Septiarini; Ummul Hairah; Andi Tejawati; Heni Sulastri
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5101

Abstract

The existence of machine learning has been exploited to solve difficulties in various fields, including the classification of leaf species in agriculture. Betel leaf is one of the plants that provide health advantages. The objective of using a machine learning approach is to classify the betel leaf species. This study involved several processes: image acquisition, region of interest (ROI) detection, pre-processing, feature extraction, and classification. The feature extraction used the combination features of color and texture. Furthermore, the classification applied four classifiers, including artificial neural network (ANN), K-nearest neighbors (KNN), Naive Bayes, and support vector machine (SVM). The evaluation in this study implemented cross-validation with a K-fold value of 5. The method performance produced the highest accuracy value of 100% using the color and texture features with the SVM classifier.
Novel annular seven tooth antenna compare its gain and return loss with circular patch antenna for mobile navigation Raja Meganathan; Saravanakumar Rengaraj
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5325

Abstract

The aim of the study involves the design of a novel annular seven tooth antenna and comparing its gain, return loss with a circular patch. Totally 38 samples are considered for the analysis which is obtained using the G power tool by fixing the pretrained power and the error rate as 0.811 and 0.051 respectively. The total 38 samples are grouped into two namely novel annular seven tooth antenna with 19 samples and circular patch with 19 samples. The performance of the antennas is analyzed using return loss and gain. The design of novel annular seven tooth antenna produced a high gain of 7.7 dB which is more significant than the gain of circular patch antennas (CPA) for navigation application. It is found that the gain, return loss of the designed novel annular seven tooth antenna is 7.7 dB, -18.1 dB. The gain and return loss of the novel annular seven tooth antenna is improved and it is compared with circular patch. It is more significant as it has a p value less than 0.05.
A PSO optimized RBFNN and STSMC scheme for path tracking of robot manipulator Atheel K. Abdul Zahra; Turki Y. Abdalla
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5018

Abstract

This article presents the design of super twisting sliding mode control (STSMC) based on radial basis function neural network (RBFNN) for path tracking of two link robot manipulator. The proposed controller is utilized to guarantee and achieve that the surface of sliding can be in equilibrium point within a short time and avoid the problem of chattering at the output. The Lyapunov theory is used in presenting a new convergence proof. Also, the particle swarm optimization (PSO) algorithm is employed to give the optimal parameter values of the proposed controller. Simulation results explain the goodness of the proposed control method for trajectory tracking of 2-link robot manipulator when compared with SMC strategy. Results demonstrate that the the percentage improvement in mean square error (MSE) of using STSMC when compared with the standard SMC are 15.36%, 16.94% and 12.92%, for three different cases respectively.
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.
Stacking ensemble learning for optical music recognition Francisco Calvin Arnel Ferano; Amalia Zahra; Gede Putra Kusuma
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5129

Abstract

The development of music culture has resulted in a problem called optical music recognition (OMR). OMR is a task in computer vision that explores the algorithms and models to recognize musical notation. This study proposed the stacking ensemble learning model to complete the OMR task using the common western musical notation (CWMN) musical notation. The ensemble learning model used four deep convolutional neural networks (DCNNs) models, namely ResNeXt50, Inception-V3, RegNetY-400MF, and EfficientNet-V2-S as the base classifier. This study also analysed the most appropriate technique to be used as the ensemble learning model’s meta-classifier. Therefore, several machine learning techniques are determined to be evaluated, namely support vector machine (SVM), logistic regression (LR), random forest (RF), K-nearest neighbor (KNN), decision tree (DT), and Naïve Bayes (NB). Six publicly available OMR datasets are combined, down sampled, and used to test the proposed model. The dataset consists of the HOMUS_V2, Rebelo1, Rebelo2, Fornes, OpenOMR, and PrintedMusicSymbols datasets. The proposed ensemble learning model managed to outperform the model built in the previous study and succeeded in achieving outstanding accuracy and F1-scores with the best value of 97.51% and 97.52%, respectively; both of which were achieved by the LR meta-classifier.
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%.
Performance enhancement of mobile ad hoc network life time using energy efficient techniques Guruprasath Rengarajan; Nagarajan Ramalingam; Kannadhasan Suriyan
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5184

Abstract

Due to the dynamic topology and limited resources in mobile ad hoc networks (MANETs), multicast routing and quality of service (QoS) provisioning are difficult issues. This study introduces an agent-based QoS routing method that uses fuzzy logic to choose the best route while taking into account a variety of independent QoS indicators, including buffer occupancy rate, remaining mobile node battery capacity, and hop count. On the other hand, finding such pathways requires a lot of work in terms of efficiency and security. This study continues to test the security of weak models, and it has been shown that it may be challenging to accept various sorts of assaults. A distributed approach is given that may be used to determine the best resource distribution at each node. Additionally, the least energy-intensive directed acyclic network network flow is selected from a group using the embedded sleep scheduling algorithm. The process of choosing the flow and allocating the resources for each video frame is adjusted to the characteristics of the network connection channel. Results show that the suggested resource allocation and flow selection algorithms provide considerable performance benefits with minimal optimality gaps at a reasonable computational cost when applied to various network topologies.
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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