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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 : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
 794   795   796   797   798 
A hybrid framework for routing and channel assignment in WMN’s Sheenam Sheenam; Raman Chadha
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.pp1226-1234

Abstract

Wireless mesh network (WMNs) can replace the physical existence of wired network; nevertheless, to solve the problem of traffic and congestion, they must carefully arrange radio resource assignment. In this paper, we focus on the major issues while transferring the data over the network and optimization of resources. WMN mainly suffers from congestion problem when numbers of channel transfer data at a same time which indirectly leads to overlapping channels and sometime data cannot be mitigated to destination. To overcome this problem and provide optimal solution we proposed hybrid channel assignment technique which offers four phase solution technique, in this technique firstly by mitigating the traffic than assigning the optimal path based on greedy and BFS. The purpose of this congestion control hybrid ant based greedy-BFS-load balancing (CCHAGBL) technique is to diminish the number of used slots, which is directly related to the overall resource assignment. We also provide optimal solution using Ant colony optimization technique with hybrid algorithm to achieve the network efficiency and resource utilization with randomly generated traffic. With the help of hybrid ant based greedy-BFS-load balancing (GBL) channel assignment algorithm throughput and packet delivery ratio is upgraded and delay is reduced to minimum.
Estimation of powerquality in distribution system using fuzzy logic theory Adeel Saleem; Kholiddinov Ilkhombek Khosiljonovich; Kholiddinova Mashkhurakhon Mutalibjon Qizi; Komolddinov Sokhib; Sharobiddinov MirzohidShakhobiddin Ugli; Soliev Sokhibjon Obidovich
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.pp1236-1245

Abstract

There are several methods are used to calculate the power quality indicators. These objectives are now served by the most recent soft computing theories, such as neural networks, fuzzy time  series, fuzzy logic theory, and other techniques. The fuzzy logic theory is one of the methods used to calculate the power quality indicators for the distribution electric network in this paper. It resolves the issue of forecasting the voltage values on distribution electrical networks' buses and enhances the quality of electricity in the face of fluctuating consumer loads and uncertainty. This paper proposed a device that has the ability for monitoring the quality of electricity domestically which leads to getting rid of many problems such as voltage deviations and asymmetrical  faults. The fuzzy logic theory is implemented to develop the algorithm for this purpose. The actual data is recorded at the distribution transformer and implement the fuzzy logic theory by using a mathematical model as well as the simulation on MATLAB for optimizing the power quality indicators with the help of fuzzy logic theory and results are discussed at the end of the paper.
Assessing actual usage and satisfaction factors of Microsoft Teams in online learning Dewi Arianti Wulandari; Tri Retnaningsih Soeprobowati; Dinar Mutiara Kusumo Nugraheni
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.pp991-1001

Abstract

This study examines the factors influencing students’ online learning using Microsoft Teams at the PLN Institute of Technology. Using primary data from 301 students, the study found that the use of Microsoft Teams improved student achievement and learning performance during the pandemic. The structural equation model (SEM) method tested the model with low validity, with all variables having an AVE value more than 0.5 and higher than the cross-loading factor valid, the outer model analysis demonstrated good convergent validity. Dependability and trustworthiness were shown by the composite reliability rating, which was over 0.70. Both perceived usefulness (PU) and perceived ease of use (PEOU) were shown to have a strong correlation, and the inner model analysis revealed positive path coefficient values without any weak variables. These results supported by hypothesis testing imply that lecturers can modify their curricula to enhance student performance in the event of a pandemic. There was shown to exist a strong relationship between the perceived ease of use and perceived usefulness.
Monte carlo simulation with bilstm for day-ahead stock portfolio management Zakir Mujeeb Shaikh; Suguna Ramadass
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1903-1914

Abstract

Predicting stock price movement and optimizing day-ahead stock portfolios are challenging tasks due to the inherent complexity and volatility of financial markets. This study proposes a novel approach that combines bidirectional long short-term memory (BiLSTM) neural networks with monte carlo simulation (MCS) to enhance day-ahead stock portfolio management. In the proposed methodology, historical data of the top-performing 10 stocks from different sectors of the National Stock Exchange of India (NSEI) is obtained from 1 January 2004 to 30 June 2023 and utilized to train a BiLSTM model. This model effectively extracts intricate patterns and trends from the time series, leading to more accurate and robust stock price predictions. MCS generates different scenarios, considering various market conditions and uncertainties. These scenarios provide a comprehensive view of the portfolio’s performance under different conditions, thus mitigating the risk of relying solely on a single prediction. The study evaluates the proposed framework and compares its performance against traditional portfolio management strategies. Results demonstrate that the MCS with the BiLSTM approach outperforms traditional methods in terms of risk-adjusted returns and portfolio stability.
Emotion detection using EEG: hybrid classification approach Kulkarni, Deepthi D.; Dixit, Vaibhav Vitthalrao; Deshmukh, Shweta Shirish
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.pp459-466

Abstract

The field of emotion research facilitates the development of several applications, all of which aim to precisely and swiftly identify emotions. Speech and facial expressions are the main focus of typical emotion analysis, although they are not accurate indicators of true feelings. Signal analysis, namely the electroencephalograph (EEG) of the brain signals, is the other area in which emotions are analyzed. When compared to other modalities, EEG offers precise and comprehensive data that facilitates the estimation of emotional states. In order to categories the emotions using an EEG signal, this work suggests a hybrid classifier (HC). The input EEG data is preprocessed using the wiener filtering approach to extract the original information from the noisy signal. The preprocessed signal is used to extract features, such as entropy and a new hybrid model that includes models such as Bi-directional long short-term memory (Bi-LSTM) and improved recurrent neural networks (IRNN), which trains using the retrieved features, is included as part of the classification process. Happy, sad, calm, and angry are the categorization findings; the suggested work demonstrates more accurate classification results than the traditional approaches. All these are done on DEAP dataset with 60%, 70%, 80%, and 90% training sets and also a new DOSE dataset is been created similar to DEAP dataset.
Unveiling visionary frontiers: a survey of cutting-edge techniques in deep learning for retinal disease diagnosis Rajatha Rajatha; Ashoka Davanageri Virupakshappa
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.pp1261-1272

Abstract

Retinal disorders impact millions of people globally. These disorders can be detected and diagnosed early enough to not only cure but also avoid permanent blindness. Manual identification of these diseases has always been tedious, time-consuming, and inconsistent. For ophthalmologists, retinal fundus images are a valuable source of information in diagnosing retinal diseases. Automatic identification of eye disorders using artificial intelligence (AI) based learning models has seen substantial development in the computer vision sector recently. Various models, particularly deep learning (DL) models are incredible in identifying and classifying diseases. In the presented review, we have performed an in-depth analysis of various existing DL models, involving preprocessing, classification, segmentation, and techniques to deal with data imbalance. We have also endeavored to gauge the effectiveness of these models by evaluating their performance using the metrics employed in their assessment. In addition, we explored various challenges along with the potential future scope in this domain. 
Detecting fake news spreaders on twitter Ali Ali Saber; Haider Khalil Easa; Arkan Raoof Ismael; Hindren Ali Saber; Aso Kamaran Omer
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.pp1648-1654

Abstract

Nowadays, fake news is prevalent and too simple to propagate through social media, particularly during elections and pandemics like COVID-19. Several fake news stories have appeared on social media sites like LINE, Facebook, and Twitter after the COVID-19 epidemic throughout the world. Also, a lot of older individuals simply forward these communications without checking their veracity, which speeds up the dissemination of fake information. So, our goal is to identify fake news using machine learning. In this paper, we describe a supervised method that automatically gathers a sizable but noisy training dataset made up of a significant number of tweets. We will categorize tweets during collection into trustworthy and untrustworthy sources, then using the dataset to train a classifier. The categorization of fake and real tweets is the next classification objective for which we apply that classifier. We first demonstrate that real news is larger in size, shared on Twitter for a longer length of time, and shared by people with more followers than following. Second, we employed machine learning models like support vector machine (SVM), random forest (RF), and decision tree (DT), and we found out that the SVM is the best of all the models due to its best results and 99% accuracy.
Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter Silmi Ath Thahirah Al Azhima; Dadang Lukman Hakim; Robby Ikhfa Nulfatwa; Nurul Fahmi Arief Hakim; Mariya Al Qibtiya
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.pp878-887

Abstract

Autonomous robots have gained significant attention in research due to their ability to facilitate human work. Navigation systems, particularly localization, present a challenge in autonomous robots. The inertial navigation system is a localization system that uses inertial sensors and a wheel odometer to estimate the robot’s relative position to the initial position. However, the system is susceptible to continuous error accumulation over time due to factors like sensor noise and wheel slip. To address these issues, external sensors are required to measure the robot’s position in the environment. The extended Kalman filter (EKF) method is utilized to estimate the robot’s position based on wheel odometer and light detection and ranging (LIDAR) sensor measurements. In the prediction stage, the input to the EKF is the position measurement from the wheel odometer, while the LIDAR sensor’s position measurement is used in the update stage to improve the prediction stage results. The test results reveal that the EKF’s estimated position has a lower average error compared to the position measurement using the wheel odometer. Therefore, it can be concluded that the EKF technique is effectively applied to the robot and can correct the wheel odometer's cumulative error with the assistance of the LIDAR sensor.
Remarks on a stochastic geometric model for interference-limited cellular communications Hamed Nassar; Gehad Mohamed Taher; El Sayed El Hady
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.pp1376-1388

Abstract

A plethora of stochastic geometry (SG) models have been developed for cellular communications, especially in the context of internet of things (IoT) applica tions. A typical assumption in such models is that base stations (BS) are de ployed in the Euclidean plane as a spatial poisson point process (PPP) of some density λ, with each communicating equipment transmitting at some power p. The usual objective of these models is to characterize the cellular coverage prob ability in both the downlink (DL) and uplink (UL) directions. In this article we expose, in the form of four remarks, the peculiar behavior of a baseline stochas tic geometric model of an interference-limited cellular system. Specifically, we reveal that under some assumptions, the coverage probability in both the UL and DL directions for this system is independent of both λ and p, flagrantly contra dicting intuition. The aim of the article is by no means to invalidate the use of SG in modeling communications systems, but rather to point out that such modeling may not be adequate all the time.
Assembling and testing optoelectronic system to record and process signals from fiber-optic sensors Kalizhanova, Aliya; Kozbakova, Ainur; Wojcik, Waldemar; Kunelbayev, Murat; Amirgaliyev, Beibut; Aitkulov, Zhalau
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp812-822

Abstract

The given research presents assembling and testing optoelectronic system to record and process signals from fiber-optic sensors. The main optoelectronic systems to record and process the signals from fiber-optic sensors are light source controller and optical power detector. There was assembled controller diagram, which apart from light source includes current source for its adequate operation, as well as the systems necessary for stabilizing its working point. The scheme was modelled for specifying nominal and maximum operation criteria. Construction has been designed in the way, that light source controller includes structures of the current regulation and stabilization super luminescent diode (SLED) and temperature stabilization. Apart from that, there was assembled the microsystem of optical power detector additionally to the light detector, which includes the microsystems of intensification and filtration of the signal measured, processing analog data into digital form, microcontroller, used for preliminary data analysis. Data of optoelectronic systems diagram to record and process the signals from fiber-optic sensors has high response speed, low noise level and sufficient progress.

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