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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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Image preprocessing analysis in handwritten Javanese character recognition Fetty Tri Anggraeny; Yisti Vita Via; Retno Mumpuni
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4172

Abstract

The handwriting produced by each person is unique, so each person has a different stroke, even though they write the same letter. Handwritten Javanese is an exciting topic to study, in addition to scientific purposes and preserving Indonesian culture. The Javanese character image dataset is aksara Jawa: aksara Jawa custom dataset from the Kaggle database consists of 2,154 train data and 480 evaluation data. This research proposed to analyze the impact of some preprocessing methods in recognizing handwritten Javanese characters. The preprocessing methods are dilation, skeletonization, and noise reduction. The first process is segmentation for region of interest (ROI) extraction, then various preprocessing is used, and finally, the recognition step neural network (NN) to measure the effectiveness of the preprocessing method. The experiment shows that all preprocessing methods (dilation, skeletonization, and noise reduction) give excellent results, especially on the black background color, reaching 98% accuracy. Other experimental findings show that in any preprocessing combination, the black background accuracy is better than the white one.
Anti-disturbance GITSMC with quick reaching law for speed control of PMSM drive Salah Eddine Halledj; Amar Bouafassa
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4222

Abstract

In this article, in order to minimize response time and enhance anti-disturbance performance of parmanent magnet synchronous motor, a global integral terminal sliding mode control based on improved quick reaching law (GITSMC-QRL) is developed. This novel reaching law has two terms which play a key role of bringing state trajectory to sliding surface as quick as possible whenever the system is close to or far from the manifold. The proposed controller cannot only speed up the convergence rate, but also has ability to suppress the chattering and ensure finite time stability. In order to avoid the chattering phenomenon caused by load disturbances and high switching gain of sliding mode control, an extended hyperbolic tangent state observer is designed as feedforward compensation compensator that is added to GITSMC. Finally, the novel scheme is validated on paremanent magnet synchronous motor (PMSM) drive through simulation, and the comparative results in various conditions show the robustness, the feasibility and the effectiveness of the proposed controller.
Radiation effect of M-slot patch antenna for wireless application Yousif Allbadi; Huda Ibrahim Hamd; Ilham H. Qaddoori
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3801

Abstract

Today, the specific absorption rate has become an important and necessary measurement when designing and implementing any type of antenna. In recent years, various devices have appeared that use different frequencies for wireless communication systems, which are a source of electromagnetic radiation. The M-slot antenna is designed in this paper to operate in multi-band frequencies for wireless communications using computer simulation technology (CST) software 2020. The radiation effect for this antenna is calculated for tissue mass of the human fingertips, which consists of three layers (skin, meat, and bone), over a mass of 1 g and 10 g according to the IEEE and International Commission on Non-Ionizing Radiation Protection (ICNIRP) organization. The results are shown three applications in the communication system, which are Wi-Fi, worldwide interoperability for microwave access (Wi-Max) and, satellite X-band and, the value of specific absorption rate (SAR) increase with increased frequency.
Dissolved oxygen control system in polishing unit using logic solver Totok Soehartanto; I Putu Eka Widya Pratama; Safira Firdaus Mujiyanti; Dwi Nur Fitriyanah; Putri Yeni Aisyah; Rico Pardona Pardosi; Nabiilah Azizah Tjandra
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4445

Abstract

The research consists of two parts, the first one is to design the dynamic plant model of polishing unit using artificial neural network (ANN) type backpropagation, and the second one is to design a simulation of a close loop control system on Simulink consisting of logic solver, control valve and ANN polishing unit. The ANN polishing unit was trained and obtained the best model structure 4-24-3 with four inputs chemical oxygen demand (COD) influent, oil in water (OIW) influent, urea, and triple superphosphate (TSP), twenty-four hidden layer nodes, and three outputs (OIW effluent, COD effluent and dissolved oxygen (DO)). The mean square error (MSE) and root mean square error (RMSE) from ANN trained were 0.00485 and 0.06964, obtained by the second iteration. From the simulation results on Simulink by giving several scenarios in the logic solver condition table, the action is brought in the form of urea and TSP nutrition issued by the control valve. The values are used to achieve the DO setpoint (2 mg/L), among others: when COD and OIW influent exceed the quality standard, COD exceeds the quality standard, and OIW does not exceed the quality standard, and the DO error is below zero.
Spoken language identification on 4 Indonesian local languages using deep learning Panji Wijonarko; Amalia Zahra
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4166

Abstract

Language identification is at the forefront of assistance in many applications, including multilingual speech systems, spoken language translation, multilingual speech recognition, and human-machine interaction via voice. The identification of indonesian local languages using spoken language identification technology has enormous potential to advance tourism potential and digital content in Indonesia. The goal of this study is to identify four Indonesian local languages: Javanese, Sundanese, Minangkabau, and Buginese, utilizing deep learning classification techniques such as artificial neural network (ANN), convolutional neural network (CNN), and long-term short memory (LSTM). The selected extraction feature for audio data extraction employs mel-frequency cepstral coefficient (MFCC). The results showed that the LSTM model had the highest accuracy for each speech duration (3 s, 10 s, and 30 s), followed by the CNN and ANN models.
Super-opposition spiral dynamic-based fuzzy control for an inverted pendulum system Ahmad Azwan Abdul Razak; Ahmad Nor Kasruddin Nasir; Nor Maniha Abd Ghani
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3825

Abstract

This paper presents a hybrid spiral dynamic algorithm with a super-opposition spiral dynamic algorithm (SOSDA) strategy. An improvement on the spiral dynamic algorithm (SDA), this method uses a concept centered on opposition-based learning, which is used to evaluate the fitness of agents at the opposite location to the current solution. The SDA is a simple-structured and deterministic type of algorithm, which also performs competitively in terms of solution accuracy. However, its deterministic characteristic means the SDA suffers premature convergence caused by the unbalanced diversification and intensification during its search procedure. Thus, the algorithm fails to achieve highly accurate solutions. It is proposed that adopting super-opposition into the SDA would enable the deterministic and random techniques to complement one another. The SOSDA was tested on four benchmark functions and compared to the original SDA. To analyze the result statistically, the Friedman and Wilcoxon tests were conducted. Furthermore, the SOSDA was applied to optimize the parameters of an interval type-2 fuzzy logic control (IT2FLC) for an inverted pendulum (IP). The statistical results produced by the SOSDA for both benchmark functions and the IP show that the proposed algorithm significantly outperformed the SDA. The SOSDA-based IT2FLC scheme also produced better IP responses than the SDA-based IT2FLC. 
Hybrid sliding neural network controller of a direct driven vertical axis wind turbine Bakou, Youcef; Abid, Mohamed; Saihi, Lakhdar; Aissaoui, Abdel Ghani; Hammaoui, Youcef
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4214

Abstract

This study aims to propose a robust hybrid sliding mode artificial neural network control (SM-ANN) scheme for controlling the stator power (active/reactive) of a doubly fed induction generator (DFIG)-based direct drive vertical axis wind turbine (VAWT) power system under a real-world scenario wind speed that will be installed in the Adrar region (Saharan zone) of Algeria. The SM-ANN scheme will control the stator power of the direct drive VAWT power. The chattering phenomenon is the most significant disadvantage associated with sliding mode control (SMC). In order to find a solution to this issue, the artificial neural network (ANN) method was applied to pick the appealing part of the SMC. MATLAB/Simulink is used to do an evaluation, after which the SM-ANN controller being suggested is compared to both traditional sliding mode (SM) and proportional-integral (PI) controllers. The results of the simulation demonstrated that the recommended SM-ANN controller has good performance in terms of enhancing the quality of energy that is delivered to the power network. This is in comparison to the traditional SM and PI controllers, which both have a long history of use. Notwithstanding the fact that there is DFIG parameter fluctuation present.
Hybrid security in AOMDV routing protocol with improved salp swarm algorithm in wireless sensor network Yousif Hardan Sulaiman; Sami Abduljabbar Rashid; Mustafa Maad Hamdi; Zaid Omar Abdulrahman Faiyadh; Abdulrahman Sabah Jaafar Sadiq; Ahmed Jamal Ahmed
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3696

Abstract

During these years the current trends shows a fast expansion in the field of wireless sensor network (WSN) based applications. Due to this much vulnerability are created and also coverage optimization becomes essential to improve overall performance. However, maximum of the model concentrates only on security or efficiency. In order to create a highly efficient protocol both concepts need to get concerted. So, we developed a protocol namely hybrid security in ad-hoc on-demand multipath distance vector (AOMDV) routing protocol with improved salp swarm algorithm (HSA-ISSA). This model is sub-divided into three sections. They are, wormhole attack and gray hole attack construction AOMDV protocol, improved salp swarm algorithm (SSA) model is used for weighted distance position updates which leads to improve the efficiency. And to secure the network from attacks we use hybrid security with the help of Diffie-Hellman key interchange algorithm and elliptic-curve cryptography (ECC) algorithm. During performance evaluation the proposed HS-ISSA protocol provide stable results in terms of message success rate (MSR), end to end delay (E2E_Delay), network throughput (NT), and average energy efficiency (AEE). Our HAS-ISSA protocol outperformed all the other earlier works by providing hybrid security, optimized coverage as well as energy efficiency to the wireless sensor networks.
Prediction of linear model on stunting prevalence with machine learning approach Mambang, Mambang; Marleny, Finki Dona; Zulfadhilah, Muhammad
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4028

Abstract

An increase in the number of residents should be anticipated including in the health sector, especially the problem of stunting. Stunting in children disrupts height and lack of absorption of nutrients. Information and data drive change in many areas such as health, entertainment, economics, business, and other strategic areas. The stages carried out in this study are initiating, developing linear models, and making prediction results on linear machine learning models. The results of testing with the scikit-learn linear model with a minimum variable of 19 get the best test results, namely the polynomial regression with pipeline model with mean absolute percentage error (MAPE) 0.02, root mean square error (RMSE) 3.32, and coefficient of determination (R2) 1,00. Testing with the scikit-learn linear model with a maximum variable of 48 gets the best test results, namely the polynomial regression with pipeline model with MAPE 0.00, RMSE 3.79 and R2 1.00. Testing with the scikit-learn linear model with an average variable of 32 gets the best test results, namely the polynomial regression model with MAPE 0.01, RMSE 3.32, and R2 1.00. The results of testing with the scikit-learn linear model with the minimum, maximum, and average variables get the best test results, namely the polynomial regression with pipeline model.
Integration of ontology with machine learning to predict the presence of covid-19 based on symptoms Hakim El Massari; Noreddine Gherabi; Sajida Mhammedi; Hamza Ghandi; Fatima Qanouni; Mohamed Bahaj
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4392

Abstract

Coronavirus (covid 19) is one of the most dangerous viruses that have spread all over the world. With the increasing number of cases infected with the coronavirus, it has become necessary to address this epidemic by all available means. Detection of the covid-19 is currently one of the world's most difficult challenges. Data science and machine learning (ML), for example, can aid in the battle against this pandemic. Furthermore, various research published in this direction proves that ML techniques can identify illness and viral infections more precisely, allowing patients' diseases to be detected at an earlier stage. In this paper, we will present how ontologies can aid in predicting the presence of covid-19 based on symptoms. The integration of ontology and ML is achieved by implementing rules of the decision tree algorithm into ontology reasoner. In addition, we compared the outcomes with various ML classifications used to make predictions. The findings are assessed using performance measures generated from the confusion matrix, such as F-measure, accuracy, precision, and recall. The ontology surpassed all ML algorithms with high accuracy value of 97.4%, according to the results.

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