cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 66 Documents
 3   4   5   6   7 
Search results for , issue "Vol 24, No 1: October 2021" : 66 Documents clear
Study of designing regulator for temperature electrical resistance furnace using Kalman stochastic reconstructor Benyekhlef Kada; Abdelkader El Kebir; Mohammed Berka; Hafida Belhadj; Djamel Eddine Chaouch
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp134-143

Abstract

Electric resistance furnaces are the most popular and widely used industrial electro thermal equipment which continues to be the subject of many improvements. The aim of this paper is to control the temperature of electrical furnace for noisy thermocouple sensors. It can be assessed by observing some variables, which are very difficult to observe. Due to limitations, mainly the location of thermal sensors and their noises. In this case, the temperature measurement is trained with centered Gaussian white noise. The problem of accurate temperatures estimation for such sensors is solved using Kalman filter, which is an optimized estimator that provides a computationally efficient way to estimate system state. Thus, variables that are not directly measurable can be reconstructed from the algorithm. Kalman stochastic reconstructor (KSR). We cannot use with fixed parameters to control the temperature. For this reason, this paper comes up with a KSR approach based pole placement (PL) hybrid controller to realize an algorithm for the temperature control electrical furnace. Results based on Matlab simulation show that the improved algorithm has well produced an optimal estimate of the temperature. Evolving over time from noisy measurements. Hybrid algorithm KSR approach based PL give good performance compared to PL controllers.
Optimal integration of photovoltaic distributed generation in electrical distribution network using hybrid modified PSO algorithms Nasreddine Belbachir; Mohamed Zellagui; Adel Lasmari; Claude Ziad El-Bayeh; Benaissa Bekkouche
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp50-60

Abstract

The satisfaction of electricity customers and environmental constraints imposed have made the trend towards renewable energies making them more essential due to their advantages as reducing power losses and ameliorating system’s voltage profiles and reliability. This article addresses the optimal location and size of multiple distributed generations (DGs) based on solar photovoltaic panels (PV) connected to electrical distribution network (EDN) using the various proposed hybrid particle swarm optimization (PSO) algorithms based on chaotic maps and adaptive acceleration coefficients. These algorithms are implemented to optimally allocate the DGs based PV (PV-DG) into EDN by minimizing the multi-objective function (MOF), which is represented as the sum of three technical parameters of the total active power loss (TAPL), total voltage deviation (TVD), and total operation time (TOT) of overcurrent relays (OCRs). The effectiveness of the proposed PSO algorithms were validated on both standards IEEE 33-bus, and 69-bus. The optimal integrating of PV-DGs into EDNs reduced the TAPL percentage by 56.94 % for the IEEE 33-bus and by 61.17 % for the IEEE 69-bus test system, enhanced the voltage profiles while minimizing the TVD by 37.35 % and by 32.27 % for two EDNs, respectively.
Development of ontology-based model to support learning process in LMS Hussein Ali Ahmed Ghanim; László Kovács
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp507-518

Abstract

E-Learning is an important support mechanism for educational systems to increase the efficiency of the education process including students and teachers. The current e-learning systems typically lack the level of metacognitive awareness, adaptive tutoring, and time management skills and have not always met the expectations of the learners as required. In this study, we introduce a novel ontological model for the learning process in the e-learning domain. In the framework, we have built a domain ontology that represents knowledge of the learning, the outcome domain ontology covers the whole learning process. We focused on the learning process ontology model conceptualizing knowledge constructions, such as learning courses, and we present the created course and learning process ontology in detail. In this work, we considered three layers of learning process. The top layer defines a general framework of learning process, conceptual model layer, defines the framework of the actual process of the learning process and course ontology model contains the knowledge unit of the learning process. The prototype ontology is constructed in protégé and managed by Java web ontology language-application programming interface (OWL-API). As a result, our model can solve the problems of current e-tutor systems. Also, it can be used for different domain in e-tutor systems. It can reach the characteristics of standardization, reusability, flexibility, and open knowledge. By applying this model, we can avoid applying isolated databases. The constructed ontology can be used in the future to control adaptive intelligent e-tutor frameworks.
A small footprint printed cross-dipole antenna with wide impedance bandwidth and circular polarization Mustafa Hasan; Nasr Alkhafaji; Hussam AlAnsary; Azhar R. Mohsin
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp347-356

Abstract

Wideband circularly polarized (CP) cross-dipole antennas with flat, cavity and artificial magnetic conductor (AMC) reflectors are proposed. Each proposed antenna consists of a pair of driven dipoles, a pair of vacant-quarter printed rings, and a 50Ω coaxial probe. The boomerang shape has been adopted in the crossed-dipole. This shape makes the design more compact, so it can be a good candidate in the antenna array because of reducing the mutual coupling. All numerical simulation works have been done using the ANSYS electromagnetic (EM) software based on the finite element method (FEM) algorithm. The presented crossed-dipole with a cavity has the best performance compared to ones with conventional flat and AMC grounds. However, the crossed-dipole with the AMC ground is a low-profile structure. Thus, the paper investigates and discusses the results of the proposed strctures thoroughly. The obtained impedance bandwidth (IBW) is 42% (5.1-7.85 GHz) and the axial-ratio bandwidth (ARBW) is 7.72% (5.86-6.32 GHz) for the crossed-dipole with the conventional flat ground (i.e., reflector). Furthermore, the IBW and ARBW for the antenna with the cavity reflector are 50.37% (5.08-8.5 GHz) and 26.4% (5.72-7.46 GHz), respectively. The antenna with the AMC ground has the characterstics of the IBW and ARBW as 38.16% (5.36-7.89 GHz) and 15.16% (5.79-6.74 GHz), respectively. All structures are designed to operate for the C-band and wireless local area networks (WLAN) applications.
A hybrid model based on convolutional neural networks and fuzzy kernel K-medoids for lung cancer detection Glori Stephani Saragih; Zuherman Rustam; Jane Eva Aurelia
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp126-133

Abstract

Lung cancer is the deadliest cancer worldwide. Correct diagnosis of lung cancer is one of the main tasks that is challenging tasks, so the patient can be treated as soon as possible. In this research, we proposed a hybrid model based on convolutional neural networks (CNN) and fuzzy kernel k-medoids (FKKM) for lung cancer detection, where the magnetic resonance imaging (MRI) images are transmitted to CNN, and then the output is used as new input for FKKM. The dataset used in this research consist of MRI images taken from someone who had lung cancer with the treatment of anti programmed cell death-1 (anti-PD1) immunotherapy in 2016 that obtained from the cancer imaging archive. The proposed method obtained accuracy, sensitivity, precision, specificity, and F1-score 100% by using radial basis function (RBF) kernel with sigma of {10­­-8, 10­­-4, 10­­-3, 5x10­­-2, 10­­-1, 1, 10­­4} in 20-fold cross-validation. The computational time is only taking less than 10 seconds to forward dataset to CNN and 3.85 ± 0.6 seconds in FKKM model. So, the proposed method is more efficient in time and has a high performance for detecting lung cancer from MRI images.
Combined fuzzy PID regulator for frequency regulation of smart grid and conventional power systems Smrutiranjan Nayak; Sanjeeb Kumar Kar; Subhransu Sekhar Dash
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp12-21

Abstract

In continually increasing area and structure of modern power system having burden demand uncertainties, the use of knowledgeable and vigorous frequency power strategy is essential for the satisfactory functioning of the Power system. A combined fuzzy proportional-integral-derivative (CFPID) controller is suggested for frequency supervision of the power system. To optimize the controller parameters, a review of sine and cosine work adjusted improved whale optimization algorithm (SCiWOA) has been utilized. The next practical application of power-system frequency control is performed by designing a CFPID controller using the proposed SCiWOA technique for a smart grid system having inexhaustible sources like sun oriented, wind, photovoltaic and capacity gadgets like a battery, flywheel just as module electric vehicles. The first advantages of the SCiWOA tuned CFPID controller over hybrid-particle-swarm-optimization and pattern-search (hPSO-PS) adjusted fuzzy proportional-integral (FPI) controller, hybrid bacterial foraging optimization algorithm-particle swarm optimization (hBFOA-PSO) adjusted proportional-integral (PI) controller, genetic algorithm (GA) tuned proportional and integral (PI) controller, BFOA adjusted PI controller, jaya algoritm (JA) tuned PID with derivative filter (PIDN) controller and teaching learning based optimization (TLBO) tuned proportional-integral-derivative (PID) controller are demonstrated for the two-area non-reheat thermal power system. The second advantages of the SCiWOA tuned CFPID controller over artificial-bee-colony (ABC) tuned PID controller, SOSA tuned PID controller and Firefly algorithm (FA) tuned PID controller are demonstrated for two-area reheat thermal power system. It is seen that SCiWOA based CFPID controller is more effective in controlling the recurrence comparative with PID regulator.
Kenaf plant pest and disease detection using faster regional based convolutional neural network Alfita Rakhmandasari; Wayan Firdaus Mahmudy; Titiek Yulianti
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp198-207

Abstract

Kenaf plant is a fibre plant whose stem bark is taken to be used as raw material for making geo-textile, particleboard, pulp, fiber drain, fiber board, and paper. The presence of plant pests and diseases that attack causes crop production to decrease. The detection of pests and diseases by farmers may be a challenging task. The detection can be done using artificial intelligence-based method. Convolutional neural networks (CNNs) are one of the most popular neural network architectures and have been successfully implemented for image classification. However, the CNN method is still considered a long time in the process, so this method was developed into namely faster regional based convolution neural network (RCNN). As the selection of the input features largely determines the accuracy of the results, a pre-processing procedure is developed to transform the kenaf plant image into input features of faster RCNN. A computational experiment proves that the faster RCNN has a very short computation time by completing 10000 iterations in 3 hours compared to convolutional neural network (CNN) completing 100 iterations at the same time. Furthermore, Faster RCNN gets 77.50% detection accuracy and bounding box accuracy 96.74% while CNN gets 72.96% detection accuracy at 400 epochs. The results also prove that the selection of input features and its pre-processing procedure could produce a high accuracy of detection. 
An improved Kohonen self-organizing map clustering algorithm for high-dimensional data sets Momotaz Begum; Bimal Chandra Das; Md. Zakir Hossain; Antu Saha; Khaleda Akther Papry
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp600-610

Abstract

Manipulating high-dimensional data is a major research challenge in the field of computer science in recent years. To classify this data, a lot of clustering algorithms have already been proposed. Kohonen self-organizing map (KSOM) is one of them. However, this algorithm has some drawbacks like overlapping clusters and non-linear separability problems. Therefore, in this paper, we propose an improved KSOM (I-KSOM) to reduce the problems that measures distances among objects using EISEN Cosine correlation formula. So far as we know, no previous work has used EISEN Cosine correlation distance measurements to classify high-dimensional data sets. To the robustness of the proposed KSOM, we carry out the experiments on several popular datasets like Iris, Seeds, Glass, Vertebral column, and Wisconsin breast cancer data sets. Our proposed algorithm shows better result compared to the existing original KSOM and another modified KSOM in terms of predictive performance with topographic and quantization error.
Portable gas leak detection system using IoT and off-the shelf sensor node Marwan Ihsan Shukur Al-Jemeli; Maythem Kamal Abbas Al-Adilee
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp491-499

Abstract

In companies that use toxic gases in vast amounts for a range of procedures, there are a host of high-risk concerns to address. People will not be able to track or control the emission of these gases on a routine basis until it becomes harmful. Sensors are expected to actively detect leaks and alert users to any potential hazards. Gas leakage may occur at multiple locations within a single installation. As a result, sensors are implanted as close to the suspected leak site as possible, enabling them to track leakage and relay signals to a base station that is situated far away. Many sensor values are received and analyzed using a microcontroller. The generated data is encoded in the wireless module and sent to the base through the internet of things link, where it is decoded and viewed by another microcontroller. When leaks are detected, the device sends an audio and visual alert, and since the detection period is very limited due to high-speed processing, leakage situations are brought under control with minimal or no effect. Using the new IoT technology and tracking from anywhere on the network, this project offers a cost-effective and reliable solution for mitigating leakage risk.
Raga classification based on pitch co-occurrence based features Vibhavari Rajadnya; Kalyani R. Joshi
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp157-166

Abstract

Analysis and classification of raga is the need of time especially in music industry. With the presence of abundance of multimedia data on internet, it is imperative to develop appropriate tools to classify ragas. In this work, an attempt has been made to use occurrence pattern of pitch based svara (note) for classification. Sequence of notes is an important cue in the raga classification. Pitch based svara (note) profile is formed. This pattern presents in the signal along with its statistical distribution can be characterized using co-occurrence matrix. Proposed note co-occurrence matrix summarizes this aspect. This matrix captures both tonal and temporal aspects of melody. Ragas differ in terms of distribution of spectral power. K-nearest neighbor (KNN) has been used as the classifier. Publicly available database consisting of 300 recordings of 30 Hindustani ragas consisting of 130 hours of audio recordings stored as 160 kbps mp3 fileswhich is part of CompMusic project is used. Leave one out validation strategy is used to evaluate the performance. Experimental result indicates the effectiveness of the proposed scheme which is giving accuracy of 93.7%.

Page 5 of 7 | Total Record : 66

 3   4   5   6   7 

Filter by Year

2021 2021


Reset
Filter By Issues
All Issue Vol 41, No 2: February 2026 Vol 41, No 1: January 2026 Vol 40, No 3: December 2025 Vol 40, No 2: November 2025 Vol 40, No 1: October 2025 Vol 39, No 3: September 2025 Vol 39, No 2: August 2025 Vol 39, No 1: July 2025 Vol 38, No 3: June 2025 Vol 38, No 2: May 2025 Vol 38, No 1: April 2025 Vol 37, No 3: March 2025 Vol 37, No 2: February 2025 Vol 37, No 1: January 2025 Vol 36, No 3: December 2024 Vol 36, No 2: November 2024 Vol 36, No 1: October 2024 Vol 35, No 3: September 2024 Vol 35, No 2: August 2024 Vol 35, No 1: July 2024 Vol 34, No 3: June 2024 Vol 34, No 2: May 2024 Vol 34, No 1: April 2024 Vol 33, No 3: March 2024 Vol 33, No 2: February 2024 Vol 33, No 1: January 2024 Vol 32, No 3: December 2023 Vol 32, No 1: October 2023 Vol 31, No 3: September 2023 Vol 31, No 2: August 2023 Vol 31, No 1: July 2023 Vol 30, No 3: June 2023 Vol 30, No 2: May 2023 Vol 30, No 1: April 2023 Vol 29, No 3: March 2023 Vol 29, No 2: February 2023 Vol 29, No 1: January 2023 Vol 28, No 3: December 2022 Vol 28, No 2: November 2022 Vol 28, No 1: October 2022 Vol 27, No 3: September 2022 Vol 27, No 2: August 2022 Vol 27, No 1: July 2022 Vol 26, No 3: June 2022 Vol 26, No 2: May 2022 Vol 26, No 1: April 2022 Vol 25, No 3: March 2022 Vol 25, No 2: February 2022 Vol 25, No 1: January 2022 Vol 24, No 3: December 2021 Vol 24, No 2: November 2021 Vol 24, No 1: October 2021 Vol 23, No 3: September 2021 Vol 23, No 2: August 2021 Vol 23, No 1: July 2021 Vol 22, No 3: June 2021 Vol 22, No 2: May 2021 Vol 22, No 1: April 2021 Vol 21, No 3: March 2021 Vol 21, No 2: February 2021 Vol 21, No 1: January 2021 Vol 20, No 3: December 2020 Vol 20, No 2: November 2020 Vol 20, No 1: October 2020 Vol 19, No 3: September 2020 Vol 19, No 2: August 2020 Vol 19, No 1: July 2020 Vol 18, No 3: June 2020 Vol 18, No 2: May 2020 Vol 18, No 1: April 2020 Vol 17, No 3: March 2020 Vol 17, No 2: February 2020 Vol 17, No 1: January 2020 Vol 16, No 3: December 2019 Vol 16, No 2: November 2019 Vol 16, No 1: October 2019 Vol 15, No 3: September 2019 Vol 15, No 2: August 2019 Vol 15, No 1: July 2019 Vol 14, No 3: June 2019 Vol 14, No 2: May 2019 Vol 14, No 1: April 2019 Vol 13, No 3: March 2019 Vol 13, No 2: February 2019 Vol 13, No 1: January 2019 Vol 12, No 3: December 2018 Vol 12, No 2: November 2018 Vol 12, No 1: October 2018 Vol 11, No 3: September 2018 Vol 11, No 2: August 2018 Vol 11, No 1: July 2018 Vol 10, No 3: June 2018 Vol 10, No 2: May 2018 Vol 10, No 1: April 2018 Vol 9, No 3: March 2018 Vol 9, No 2: February 2018 Vol 9, No 1: January 2018 Vol 8, No 3: December 2017 Vol 8, No 2: November 2017 Vol 8, No 1: October 2017 Vol 7, No 3: September 2017 Vol 7, No 2: August 2017 Vol 7, No 1: July 2017 Vol 6, No 3: June 2017 Vol 6, No 2: May 2017 Vol 6, No 1: April 2017 Vol 5, No 3: March 2017 Vol 5, No 2: February 2017 Vol 5, No 1: January 2017 Vol 4, No 3: December 2016 Vol 4, No 2: November 2016 Vol 4, No 1: October 2016 Vol 3, No 3: September 2016 Vol 3, No 2: August 2016 Vol 3, No 1: July 2016 Vol 2, No 3: June 2016 Vol 2, No 2: May 2016 Vol 2, No 1: April 2016 Vol 1, No 3: March 2016 Vol 1, No 2: February 2016 Vol 1, No 1: January 2016 Vol 16, No 3: December 2015 Vol 16, No 2: November 2015 Vol 16, No 1: October 2015 Vol 15, No 3: September 2015 Vol 15, No 2: August 2015 Vol 15, No 1: July 2015 Vol 14, No 3: June 2015 Vol 14, No 2: May 2015 Vol 14, No 1: April 2015 Vol 13, No 3: March 2015 Vol 13, No 2: February 2015 Vol 13, No 1: January 2015 Vol 12, No 12: December 2014 Vol 12, No 11: November 2014 Vol 12, No 10: October 2014 Vol 12, No 9: September 2014 Vol 12, No 8: August 2014 Vol 12, No 7: July 2014 Vol 12, No 6: June 2014 Vol 12, No 5: May 2014 Vol 12, No 4: April 2014 Vol 12, No 3: March 2014 Vol 12, No 2: February 2014 Vol 12, No 1: January 2014 Vol 11, No 12: December 2013 Vol 11, No 11: November 2013 Vol 11, No 10: October 2013 Vol 11, No 9: September 2013 Vol 11, No 8: August 2013 Vol 11, No 7: July 2013 Vol 11, No 6: June 2013 Vol 11, No 5: May 2013 Vol 11, No 4: April 2013 Vol 11, No 3: March 2013 Vol 11, No 2: February 2013 Vol 11, No 1: January 2013 Vol 10, No 8: December 2012 Vol 10, No 7: November 2012 Vol 10, No 6: October 2012 Vol 10, No 5: September 2012 Vol 10, No 4: August 2012 Vol 10, No 3: July 2012 More Issue