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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 783 Documents
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Application of Three-Phase Power Flow Analysis to the Nigerian Distribution Networks Samson Oladayo Ayanlade; Abdulrasaq Jimoh; Funso Kehinde Ariyo; Adedire Ayodeji Babatunde; Abdulsamad Bolakale Jimoh; Fatina Mosunmola Aremu
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3921

Abstract

Single-phase power flow analysis is used to study most distribution networks in Nigeria. The use of single-phase-power flow analysis assumes that the network is balanced and that the conductor phases act identically. However, Nigerian distribution networks are highly imbalanced because of untransposed lines, irregularly distributed loads in conductor phases, mismatched conductor sizes, and spacing. Consequently, single-phase modeling of the networks fails to reflect actual network behavior, resulting in an incorrect power flow solution. This research presents the three-phase modeling of radial distribution networks for a three-phase-power flow study of Nigerian distribution networks. Olusanya's 54-bus and Ajinde's 62-bus distribution networks in Nigeria were evaluated, both of which were very imbalanced. Without making any assumptions about the network components, these two distribution networks were properly modeled. Each network's three-phase power flow study was carried out in the MATLAB environment. The power flow solutions for each network demonstrated unevenness in the voltage profile for each network phase, as well as inequality in the real and reactive power losses in each phase, indicating that the deployed three-phase-power flow analysis properly mirrored the underlying network characteristics. Therefore, applying three-phase power flow analysis to distribution networks is critical for proper assessment of distribution network performance.
Fruits Disease Classification using Machine Learning Techniques Yassine Benlachmi; Aymane El Airej; Moulay Lahcen Hasnaoui
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3907

Abstract

Due to increased population, there is a high demand for agricultural products these days and therefore, effective growth and increased fruit production have become critical. Consequently, for better fruit yield cultivators employ traditional methods for monitoring fruit yield from harvest till ripening of fruit. However, manual monitoring and visual inspection doesn’t always bring the actual identification of fruit disease due to variety of reasons, such as less knowledge about pathogens, requiring more time for disease analysis and that too with less accuracy and so on, consequently, leaving for the need of a professional assistance and expertise. Moreover, the task also becomes difficult as various fruits demonstrate their gesticulation by changing the colour of their skin which can come from nature and resulting in various black or dark brown spots on the fruit skin indicating various diseases. As a result, it is necessary to propose an efficient smart farming strategy that will aid in increased productivity while at the same time involving less human effort. The proposed research work attempts to classify the fruit disease at its early stage by using machine learning techniques. For this purpose, fruit’s texture, and skin colour have been utilized. The approach fundamentally employs three machine learning classifier algorithms - KNN, Decision Tree, and Random Forest. Whereas the features have been determined by using three prominent feature extractors - Haralick, Hu Moments and colour histogram. Finally, the system has been evaluated by utilizing the k-fold cross validation method. Specimen dataset was divided into two groups — the training subset and the test subset. As a rule, four-fold cross-validation, three-fourths of the images were used for training the models whereas, the remaining one-fourth were used for testing purposes. Assessment results for suggested methodology after conducting experimentation on publicly available dataset and drawn confusion matrix and learning cure shows that Random Forest classifiers achieves accuracy about 99% while for K-Means accuracy statistics stands at 98.67% and for Decision trees it is about 97.75% - for colour histogram features.
Cryptanalysis the SHA-256 Hash Function using Rainbow Tables Olga Manankova; Mubarak Yakubova; Alimjan Baikenov
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4247

Abstract

The research of the strength of a hashed message is of great importance in modern authentication systems. The hashing process is inextricably linked with the password system, since passwords are usually stored in the system not in clear text, but as hashes. The SHA-256 hash function was chosen to model the attack with rainbow tables. An algorithm for constructing a rainbow table for the SHA-256 hash function in the Java language is proposed. The conditions under which the use of rainbow tables will be effective are determined. This article aims to practically show the process of generating a password and rainbow tables to organize an attack on the SHA-256 hash function. As research shows, rainbow tables can reveal a three-character password in 3 seconds. As the password bit increases, the decryption time increases in direct proportion.
An S-Band Microstrip Patch Antenna Design and Simulation for Wireless Communication Systems Md Sohel Rana; Sk Ikramul Islam; Sharif Al Mamun; Laltu Kumar Mondal; Md. Toukir Ahmed; Md. Mostafizur Rahman
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4141

Abstract

In this paper, a 3.5 GHz microstrip patch antenna for the future of wireless communication is designed and studied. As a substrate, Rogers RT/Duroid5880 is utilized. This material has a thickness of 0.077mm and a dielectric loss of 2.2. The proposed antenna layout is simulated using the CST studio suite of software programs. This research aimed to achieve a lower return loss, higher gain, lower VSWR, directivity, and improved efficiency. The simulation revealed that the return loss, gain, VSWR, and directivity were correspondingly -13.772 dB, 7.55 dB, 1.5152, and 8.43dBi. The efficiency was 89.56%. This antenna has been developed and assessed for use in various wireless communication applications with a 3.5 GHz operating frequency, which is used as a reference antenna in communication satellites, weather radar, surface ship radar, wireless LAN-802.11b and 802.11g, multimedia applications in mobile TV and satellite radio, optical communications at 1460 to 1530 nm wavelength, and is utilized for other wireless fidelity applications.
The Impact of Telemetry Received Signal Strength of IMU/GNSS Data Transmission on Autonomous Vehicle Navigation Muhammad Khosyi'in; Sri Arttini Dwi Prasetyowati; Bhakti Yudho Suprapto; Zainuddin Nawawi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3901

Abstract

This paper presents the effect of received signal strength on IMU/GNSS sensor data transmission for autonomous vehicle navigation. A pixhawk 2.1 flight controller is used to build the navigation system. Straight lines with back-and-forth routes were tested using two types of SiK telemetry: Holybro and RFD. The results of the tests show that when the RSSI value falls close to the receiver's sensitivity value, the readings of the gyro sensor data, accelerometer, magnetometer, and GNSS compass data are disturbed. When the RSSI signal collides with noise, the radio telemetry link is lost, affecting the accuracy of speed data and the orientation of autonomous vehicles. According to Cisco's conversion table, the highest RSSI on Holybro telemetry is -48 dBm, and the lowest is -103 dBm, with a receiver sensitivity of -117 and data reading at a distance of about 427 meters. While the highest RSSI value on RFD telemetry is -17 dBm and the lowest is -113 dBm, even the lowest value is above the receiver's sensitivity limit of -121 dBm with data readings at a distance of approximately 749.4 meters. RFD outperforms Holybro in terms of RSSI and sensitivity at low data rates. When reading distance data to reference distance data using Google Earth and ArcGIS, RFD telemetry has a higher accuracy, with an average accuracy of 98.8%.
Weightless Neural Networks Face Recognition Learning Process for Binary Facial Pattern Ahmad Zarkasi; Siti Nurmaini; Deris Stiawan; Bhakti Yudho Suprapto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3957

Abstract

The facial recognition process is normally used to verify and identify individuals, especially during the process of analyzing facial biometrics. The face detection algorithm automatically determines the presence or absence of a face. It is, however, theoretically difficult to analyze the face of a system with limited resources due to the complex pattern of a face. This implies an embedded platform scheme which is a combination of several learning methods supporting each other is required. Therefore, this research proposed the combination of the Haar Cascade method for the face detection process and the WNNs method for the learning process. The WNNs face recognition Algorithm (WNNs-FRA) uses facial data at the binary level and for binary recognition. Moreover, the sample face data in the binary were compared with the primary face data obtained from a particular camera or image. The parameters tested in this research include detection distance, detection coordinates, detection degree, memory requirement analysis, and the learning process. It is also important to note that the RAM node has 300 addresses divided into three face positions while the RAM discriminator has three addresses with codes (00), (10), and (10). Meanwhile, the largest amount of facial ROI data was found to be 900 pixels while the lowest is 400 pixels. The total RAM requirements were in the range of 32,768 bytes and 128 bytes and the execution time for each face position was predicted to be 33.3% which is an optimization because it is 66.67% faster than the entire learning process
ANFIS multi-tasking algorithm implementation scheme for ball-on-plate system stabilization Oussama Hadoune; Mohamed Benouaret
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4216

Abstract

This paper presents the design and realization of a ball-on-plate system using a 3-degree-of-freedom parallel robot controlled by an adaptive neuro-fuzzy in-ference system. The ball-on-plate system is nonlinear, multivariable, with an under-actuated feature. Initially, the parallel robot is designed using SolidWorks and mechanized using a computer numerical control machine. Followed by the presentation of the ball-on-plate system mathematical model and the simplified model obtained. Afterwards, the inverse kinematics are performed to derive the appropriate angle for each servomotor. Eventually, the controller is designed and implemented in a double loop feedback scheme. A comparison between the proposed controller and a conventional proportional–integral–derivative controller in terms of time response, overshoot, and steady-state error is carried out. Furthermore, a comparison between sequential and asynchronous parallel processing is conducted for two different scenarios. The first scenario is when moving the ball to the origin while the second is for disturbance rejection. Simulation and experimental results show that the adaptive neuro-fuzzy inference system implemented using asynchronous parallel processing improves the real-time system stability by considerably decreasing oscillations as well as enhancing the ball movement smoothness with a small stead-state error.
A Comparison Between CCCV and VC Strategy for the Control of Battery Storage System in PV installation Achraf Nouri; Aymen Lachheb; Lilia El Amraoui
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4040

Abstract

To meet demand with unpredictable daily and seasonal variations, the power grid faces significant hurdles in transmission and distribution. Electrical Energy Storage (EES), in which energy is stored in a specific state, depending on the technology utilized, and is converted to electrical energy, is acknowledged as a technology involved with significant potential for solving these difficulties. This paper deals with the modeling and control of a renewable energy production system based on solar panel. To improve the performance of the investigated power generation system, a lithium-ion battery storage system and bidirectional converter are associated to a solar panel that is unable to compensate for rapid variations in load power demand. In this situation, to meet load power demand, a rule-based energy management algorithm is used to share energy between the grid and the energy production system. Furthermore, two solutions are developed and compared: VC (Variable Current) and CC-CV (Constant Current Constant Voltage). The VC approach is used in conjunction with an energy management and protection system, whereas the CC-CV method is used in conjunction with an artificial neural network (ANN). The simulation results show that the VC control strategy give greater energy performance and installation stability compared to the CC-CV strategy, but not improved safety and protection of lithium-ion batteries.
Performance Investigation of Software Agents in Artificial Intelligence and Document Object Model Domain Abhijit Bora; Tulshi Bezboruah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.3984

Abstract

Assessing the performance impact of Artificial Intelligence on questionnaires of pharmacological unit is necessary from the perspective of medical practitioners as well as patient’s perspectives.  The proposed study ascertained that software agents based on Artificial Intelligence and Document Object Model domain can deliver better service in medical units in contrast to its other deployment methodologies. So, the proposed work, a prototype is developed by using dialog control class file which accesses the system resources through the kernel objects. We call the software agent as MBot (prototype bot for medical unit). The prototype is deployed for pharmacological unit where the clinical instruction against the disease can be suggested. The experimental arrangement, deployment architecture of MBot, performance metrics and the statistical analysis for the observed data sample are discussed here. The novelty of the proposed work highlights the performance aspects of MBot against its counterpart. It reveals that better response time and validates that the dialog controller class of MBot can process questionnaires through its intelligence.
Efficient Pavement Crack Detection and Classification Using Custom YOLOv7 Model Arselan Ashraf; Ali Sophian; Amir Akramin Shafie; Teddy Surya Gunawan; Norfarah Nadia Ismail; Ali Aryo Bawono
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4362

Abstract

It is crucial to detect and classify pavement cracks as part of maintaining road safety. The inspection process for identifying and classifying cracks manually is tedious, time-consuming, and potentially dangerous for inspectors. As a result, an efficient automated approach for detecting road cracks is essential for this development. Numerous issues, such as variations in intensity, uneven data availability, the inefficacy of traditional approaches, and others, make it challenging to accomplish. This research has been carried out to contribute towards developing an efficient pavement crack detection and classification system. This study uses state of the art deep learning algorithm, customized YOLOv7 model. Data from two sources, RDD2022, a publicly available online dataset, and the second set of data gathered from the roads of Malaysia have been used in this investigation. In order to have balanced data for training, many image preprocessing techniques have been applied to the data, such as augmentations, scaling, blurring, etc. Experimental results demonstrate that the detection accuracy of the YOLOv7 model is significant, 92% on the RDD2022 dataset and 88% on our custom dataset. This study reports the outcomes of experiments conducted on both datasets. RDD2022 achieved a precision of 0.9523 and a recall of 0.9545. On the custom dataset, the resulting values for precision and recall were 0.93 and 0.9158, respectively. The results of this study were compared to those of other recent studies in the same field in order toestablish a benchmark. Results from the proposed system were more encouraging and surpassed the benchmarking ones. 

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