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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 : -
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Articles 9,138 Documents
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Hardware implementation of type-2 fuzzy logic control for single axis solar tracker Krismanto, Awan Uji; Muhammad Davi Labib, Radimas Putra; Setiadi, Herlambang; Lomi, Abraham; Abdillah, Muhammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp102-112

Abstract

Solar tracker widely maximizes solar energy harvesting by maintaining a perpendicular relative position between the sun and the solar panel. Single and dual-axis solar tracker controllers are the most control mechanisms that are widely implemented. The single-axis solar tracker (SAST) is preferable between those two control mechanisms due to economic and simpler control algorithm features. Many control algorithms have been proposed to improve the performance of SAST. The conventional proportional integral derivative (PID) controller has major limitations mainly corresponding to slower response. Moreover, it cannot handle the uncertainties of the sunlight. To overcome the problem, type 2-fuzzy logic control (T2-FLC) is proposed. The single-axis solar tracker controller based on T2-FLC is applied in Arduino and implemented in the hardware environment. It was monitored that the T2-FLC provides much better responses than the conventional controllers in terms of better dynamic response and more efficiency in harvesting solar energy.
Spark-MLlib intrusion detection mechanism using machine learning models Asra Sarwath; Raafiya Gulmeher; Zeenath Sultana
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1235-1242

Abstract

Typically, a single method is employed in machine learning (ML) based intrusion detection to identify intrusion information. However, this approach lacks flexibility, has a low detection rate, and struggles to handle high-dimensional data. Consequently, it is not efficient in addressing these challenges. This study proposes a new intrusion detection architecture that utilizes Spark and ensures resilient data dissemination across the platform to improve its effectiveness. It consists of preprocessing module, a label encoder module, a feature extraction module, a classification module and a database module. The preprocessing module compresses information by utilizing the module for label encoding. This generates a lower-dimensional reconstruction and classification characteristic. The database module has the capability to store the compressed characteristics of all traffic. This enables the classifier to be tested and then returns these features back into the original traffic, facilitating retraining. In order to evaluate the efficacy of the framework, simulations were conducted using the CICIDS 2017 dataset to accurately replicate the network traffic. Based on the test findings, the accuracy of both multiclass and binary classification surpasses that of earlier studies. High precision was achieved for the traffic that was restored. The possible application of the proposed architecture for edge/fog networks is discussed in the conclusion.
Electromagnetism-like mechanism algorithm for hybrid flow-shop scheduling problems Smail Khaled; Djebbar Bachir
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1614-1620

Abstract

Given the interest and complexity, of the hybrid flow shops (HFS) problem in industry, he has been extensively considered, the HFS, is a complex combinatorial problem supported in many original world applications. We consider a hybrid flow shop FH4(P3, P2)||Cmax to applied in this paper. In this papers we attempt to optimize the makespan which refers to the last task completion time by an adequate meta-heuristic algorithm based on electromagnetism mechanism (EM). We also present analysis on the performance of the EM-algorithm adapted to HFS scheduling problems. The electromagnetism-like mechanism method gave us efficient and fair results comparing to particle swarm and genetic algorithm.
Web mining and sentiment analysis of COVID-19 discourse in online forum communities Masurah Mohamad; Suraya Masrom; Khairulliza Ahmad Salleh; Lathifah Alfat; Muhammad Nasucha; Nur Uddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1280-1287

Abstract

Recently, various discussions, solutions, data, and methods related to coronavirus disease 2019 (COVID-19) have been posted in online forum communities. Although a vast amount of posting on COVID-19 analytical projects are available in the online forum communities, much of them remain untapped due to limited overview and profiling that focuses on COVID-19 analytic techniques. Thus, it is quite challenging for information diggers and researchers to distinguish the recent trends and challenges of COVID-19 analytic for initiating different and critical studies to fight against the coronavirus. This paper presents the findings of a study that executed a web mining process on COVID-19 data analytical projects from the Stack Overflow and GitHub online community platforms for data scientists. This study provides an insight on what activities can be conducted by novice researchers and others who are interested in data analysis, especially in sentiment analysis. The classification results via Naïve Bayes (NB), support vector machine (SVM) and logistic regression (LR) have returned high accuracy, indicating that the constructed model is efficient in classifying the sentiment data of COVID-19. The findings reported in this paper not only enhance the understanding of COVID-19 related content and analysis but also provides promising framework that can be applied in diverse contexts and domains.
Investigating power scaling factor for pattern division multiple access Linda Meylani; Vinsensius Sigit Widhi Prabowo; Iswahyudi Hidayat; Nisa Alwiyah
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp370-382

Abstract

Pattern division multiple access (PDMA) is a one type of multi-domain non-orthogonal multiple access (NOMA) that support massive connectivity and can improve spectral efficiency. The unique pattern is used by each user to map its transmitting data into a group of resource, which consist of frequency, code and spatial domain or combination of these resources. Power scaling and phase shifting are used to resolve ambiguity as consequence non uniform distribution of the received combined constellation. In this paper, we propose investigation on power scaling factor for each user in PDMA matrix to increase sum rate transmission and propose combine successive interference cancellation (SIC) based on diversity order and power scaling factor for each user. The simulation results confirm that the proper implement power scaling factor in pattern type 2 show best performance in Rician fading channels.
Proposal for an e-learning system model based on the invocation and semantic discovery of web services Halim, Mohamed; Tahiri, Abdelmajid; El Ghzizal, Yassir; Adadi, Nouha; Chenouni, Driss
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp631-641

Abstract

Service-oriented computing (SOC) provides a new framework for designing distributed web applications and software in a flexible, scalable, and cost-effective manner. Its use is widespread to efficiently integrate existing Web services and create high value-added applications. This model, proven in various fields such as e-commerce, also shows significant advantages in the field of e-learning. This approach highlights the discovery and use of Web services listed in specialized directories. In fact, this paper proposes a framework for exploring web services associated with education. This approach is based on the application of a matching algorithm to select the services best suited to the needs of users of the online learning system, as well as the ontology of the e-learning domain and the semantic descriptions of the web services via web ontology language for web services (OWL-S).
Proposed algorithm base optimisation plan for feature selection-based intrusion detection in cloud computing Imane Laassar; Moulay Youssef Hadi; Arifullah Arifullah; Hassnae Remmach; Fawad Salam Khan
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1140-1149

Abstract

A crucial element in detecting unusual network system behavior is the network intrusion detection system (NIDS), which also helps to stop network attacks from happening. Despite the fact that a great deal of machine learning techniques has been used in intrusion detection, current solutions still struggle to provide accurate classification results. Furthermore, when dealing with imbalanced multi-category traffic data, a single classifier may not be able to produce a superior. Particularly, internet of things (IoT) gadgets is now a commonplace aspect of life. On the other hand, some problems are becoming worse and lack clear remedies. Convergence, communication speed, and security between various IoT devices are among the primary concerns. In order to achieve this goal, an enhanced artificial bee colony technique utilizing binary search equations and neural networks—known as the (BABCN) algorithm for intrusion detection in terms of convergence and communication speed—is presented in this study. The artificial bee is improved by the depth-first search framework and binary search equations upon which the BABCN method is based. The suggested approach has a good ability to detect intrusions in the network and enhances categorization, according to the findings obtained by using the NSL-KDD dataset.
Field programmable gate array implementation of edge detection system based on an improved sobel edge detector Duong Huu Ai; Cong Dat Vuong; Khanh Ty Luong; Viet Truong Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1378-1383

Abstract

Field programmable gate array (FPGA) is an integrated circuit consisting of internal hardware blocks with programmable link connections for users to customize operations for a particular application. Link connections can be easily reprogrammed, allowing the FPGA to adapt to changes to the design or even support a new application throughout the department's uptime. One of the important tasks in image processing is image edge detection image, with computer aided, image recognition is concerned with the recognition and classification of objects in an image, so edge detection is an important tool. In this paper, we design filter for edge detection in image processing using FPGA kit. We analysis and implementation of algorithm for image processing on FPGA, load the code and run the results. Comparative analysis with images processed by MATLAB software.
Integrated electronic system for FET biosensor assessment based on current-voltage curve tracing Achmad Arif Bryantono; Leonardo Kamajaya; Fitri Fitri; Sungkono Sungkono; Herwandi Herwandi; Agwin Fahmi Fahanani
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1463-1471

Abstract

Field-effect transistor (FET) biosensors are pivotal in diverse applications, from environmental monitoring to healthcare diagnostics. Current-voltage (I-V) curve tracing is a powerful method for evaluating FET biosensor behavior, enabling comprehensive analysis of their FET biosensor characteristics. Traditional I-V curve tracing methods often require complex and expensive equipment, limiting their accessibility and practicality for routine sensor assessment. This study aims to develop and demonstrate an integrated electronic system for assessing FET biosensors using I-V curve tracing. The integrated electronic system uses readily available components, including microcontrollers, analog circuitry, and user-friendly software. We developed a compact, low-cost device that generates I-V curves for the FET biosensor. The integrated electronic system successfully generated I-V curves for various FET biosensors. The system demonstrated consistent, reliable performance, portability, and ease of use, making it a practical solution for routine sensor assessment. The average error in measurements using bipolar junction transistors (BJT) and metal-oxide-semiconductor field-effect transistors (MOSFETs) results in 2.62%, and measurements at different pH levels have a sensitivity of 21.6 mV/pH and a linearity of 0.9892. This innovation contributes to the advancement of FET biosensor technology. In the future, the developments should focus on ensuring their accuracy and reliability in various sensor fields.
Automatic facial expression recognition under partial occlusion based on motion reconstruction using a denoising autoencoder Abdelaali Kemmou; Adil El Makrani; Ikram El Azami; Moulay Hafid Aabidi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp276-289

Abstract

Automatic facial expression recognition (FER) plays a valuable role in various fields, including health, road safety, and marketing, where providing feedback on the user’s condition is crucial. While significant progress has been made in controlled environments (such as frontal, unconcluded, and well-lit conditions), recognizing facial expressions in unconstrained environments (natural settings) remains challenging. The presence of occlusions poses a particular difficulty as they obscure parts of the facial information captured in the image. To address this issue, researchers have proposed different solutions, broadly categorized into two approaches: those focusing on visible regions of the face and those attempting to reconstruct hidden parts. Currently, most solutions rely on texture or geometry-based methods, with only a few utilizing motion-based approaches. However, incorporating motion appears to be particularly promising in adapting to occlusions due to its unique characteristics, such as close-range propagation and local coherence. In this paper, our focus lies on leveraging motion to overcome the challenges posed by occlusions in FER tasks.

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