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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
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Software component selection methods and techniques: a systematic review Ahmad Nabot
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.pp1802-1811

Abstract

Software component selection is critical in software engineering due to its vital role in reducing software development cost and time. This study analyzes software component selection research studies on methodologies, criteria, and multi-criteria decision-making (MCDM) techniques. The key study findings are: first, comprehensive standardized criteria for software component selection are lacking, with ambiguous terminology used in research. Second, current ad hoc selection processes need streamlining to reduce time, cost, and effort. Thus, an integrated approach is required to aid decision-makers. The review suggests developing automated tools or decision support systems combining multiple criteria decision methods to improve selection accuracy and efficiency. Standardized criteria catalogs can also assist software developers in the evaluation. The findings highlight that despite extensive academic research, component selection in practice remains sub-optimal. By informing future research and tool development, this review can benefit practitioners to systematically select the most appropriate software components meeting software requirements.
A study on microclimate monitoring and control inside greenhouse using fans automation Irfan Ardiansah; Endryaz Vergian Nusantara; Selly Harnesa Putri; Ryan Hara Permana
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp101-112

Abstract

Efficient microclimate management is crucial in enhancing crop yields in greenhouses. Factors like temperature, relative humidity, and ultraviolet (UV) index significantly impact crop quality. The absence of adequate ventilation mechanisms in greenhouses presents a challenge for temperature regulation. This study proposes a solution for tropical greenhouses by designing a system that automatically activates fans when temperatures rise above 30 °C. This system regulates temperature and cultivates optimal growth conditions for crops. It is supported by a web page that enables monitoring and adjustment of microclimate data. To accommodate individual crop requirements, the minimum temperature threshold for fan activation can be modified, enhancing the system's adaptability. The impact of the UV index on greenhouse temperature is also considered. The automation system decreases the temperature by around ±3 °C when the UV index hits 10. Nonetheless, its cooling impact wanes beyond the UV index of 10. A greenhouse automation system, equipped with fans and internet access, proves quite useful for agricultural environment management. It tackles the temperature control issue and offers varied solutions.
On-chip based power estimation for CMOS VLSI circuits using support vector machine Nagarajan, Sridevi; Mahadeviah, Prasanna Kumar
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.pp804-811

Abstract

Power estimation has a major impact on the reliability of very-large-scale integration (VLSI) circuits. As a results power estimation is highly needed in VLSI circuits at the early stages. One of the evident challenges in integrated circuit (IC) industry is development and investigation of techniques for the reduction of design complexity due to the growing process variations and reduction of chip manufacturing turnaround time. Under these conditions, the higher design levels of average power estimation before the chip manufacturing process is highly essential for the calculation of power budget and to take the necessary steps for the reduction of power consumption. Over the years, most of the approaches were designed to estimate the power usage, however, most of the conventional techniques are time consuming, resource-intensive and largely manual. Machine learning techniques have received much attention in many of the engineering applications and are capable for modelling the complex systems through historical data. Hence, in this work on chip-based power estimation for complementary metal-oxide-semiconductor (CMOS) VLSI using support-vector machine (SVM) is presented to estimate the power. The SVM is employed to estimate the usage of power at runtime. The performance of this model is evaluated in terms of Power usage, delay, data accuracy and error rate.
Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles Unni, Manu Vasudevan; S., Jeevananda; Kalapurackal, Jacob Joseph; Fatma, Saba
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.pp292-300

Abstract

Fake profile detection on social media is a critical task intended for detecting and alleviating the existence of deceptive or fraudulent user profiles. These fake profiles, frequently generated with malicious intent, could engage in different forms of spreading disinformation, online fraud, or spamming. A range of techniques is employed to solve these problems such as natural language processing (NLP), machine learning (ML), and behavioural analysis, to examine engagement patterns, user-generated content, and profile characteristics. This paper proposes an automated fake profile detection using the coyote optimization algorithm with deep learning (FPD-COADL) method on social media. This multifaceted approach scrutinizes user-generated content, engagement patterns, and profile attributes to differentiate genuine user accounts from deceptive ones, ultimately reinforcing the authenticity and trustworthiness of social networking platforms. The presented FPD-COADL method uses robust data pre-processing methods to enhance the uniformness and quality of data. Besides, the FPD-COADL method applies deep belief network (DBN) for the recognition and classification of fake accounts. Extensive experiments and evaluations on own collected social media datasets underscore the effectiveness of the approach, showcasing its potential to identify fake profiles with high scalability and precision.
Enhanced low voltage ride-through control of multilevel flying capacitor inverter based wind generation Younes El Khlifi; Abdelmounime El Magri; Adil Mansouri; Rachid Lajouad
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.pp854-861

Abstract

This paper introduces a cost-effective control method to enhance the low voltage ride-through (LVRT) capability and smooth the output power of a three-phase multilevel flying capacitor inverter (FCI) in wind turbine-based permanent magnet synchronous generator (PMSG). The proposed approach utilizes the energy storage capability of flying capacitors to mitigate wind power fluctuations and address short-duration outages and deep voltage sags. Additionally, a nonlinear controller based Lyapunov theory is developed to regulate capacitor voltages, improve power factors, and balance DC-link voltage. Numerical simulations are conducted in MATLAB/SimPower systems environment to validate the effectiveness of this comprehensive control strategy across different grid operation scenarios.
Optimization of the operations and maintenance for wind farm using genetic algorithms Rachid Mkhaitari; Yamina Mir; Mimoun Zazoui
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.pp1257-1266

Abstract

In Morocco, with the growing wind installed capacity it becomes crucial to perform further the O&M process, the wind assets are exposed to several constraints and raindom of maintenances for incidents. The target of this work is to optimize the gross and net production while simultaneously adhering to the minimum turbine unavailability and minimizing all contractual power curtailments due to unforeseeable factors. The proposed system is modeled using technical and mathematical formulas composed of the main function and the associated constraints. The project's modularization ends with a complex mathematical system that is non-linear, non-differentiable and requires the genetic algorithm for optimal resolution. Using Matlab to determine the optimized solution that represents the number of operational turbines per day, allowing for maximum production, minimizing curtailment, and reducing the unavailability of turbines. The 365-vector containing the numbers of turbines per day will opimally define the long-term O&M strategy.
Enhancing Hadoop distributed storage efficiency using multi-agent systems Rabie Mahdaoui; Manar Sais; Jaafar Abouchabaka; Najat Rafalia
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.pp1814-1822

Abstract

Distributed storage systems play a pivotal role in modern data-intensive applications, with Hadoop distributed file system (HDFS) being a prominent example. However, optimizing the efficiency of such systems remains a complex challenge. This research paper presents a novel approach to enhance the efficiency of distributed storage by leveraging multi-agent systems (MAS). Our research is centered on enhancing the efficiency of the HDFS by incorporating intelligent agents that can dynamically assign storage tasks to nodes based on their performance characteristics. Utilizing a decentralized decision-making framework, the suggested approach based on MAS considers the real-time performance of nodes and allocates storage tasks adaptively. This strategy aims to alleviate performance bottlenecks and minimize data transfer latency. Through extensive experimental evaluation, we demonstrate the effectiveness of our approach in improving HDFS performance in terms of data storage, retrieval, and overall system efficiency. The results reveal significant reductions in job execution times and enhanced resource utilization, there by offering a promising avenue for enhancing the efficiency of distributed storage systems.
Exploring public sentiments towards courier services in Malaysia through Twitter: a sentiment analysis approach Mohamed Imran Mohamed Ariff; Rasyidin Muhammad Zaharin Raja Salim; Azilawati Azizan; Samsiah Ahmad; Noreen Izza Arshad; Mohammad Nasir Abdullah
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.pp1966-1973

Abstract

This study delves into the sentiment analysis of courier services in Malaysia through the lens of tweets. With the escalating reliance on courier services for daily parcel deliveries, understanding public sentiment becomes imperative. Through a systematic methodology encompassing data gathering, preparation, alteration, analysis, modeling, and deployment, this study aims to furnish valuable insights and recommendations. A dynamic graphical representation of sentiment scores is presented for intuitive interpretation and informed decision-making. The study's significance lies in empowering Malaysians to make judicious choices regarding courier services, thereby fostering transparency in the industry. The study proposes several avenues for future enhancements, including the integration of autocorrect features, diversifying data sources and languages, incorporating deep learning techniques, and developing a user-friendly interface for enhanced visualization. This study serves as a guiding resource, illuminating sentiments surrounding courier services in Malaysia and paving the way for future advancements in sentiment analysis within this domain.
Elevating smart city mobility using RAE-LSTM fusion for next-gen traffic prediction Rafalia, Najat; Moumen, Idriss; Raji, Fatima Zahra; Abouchabaka, Jaafar
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.pp503-510

Abstract

The burgeoning demand for efficient urban traffic management necessitates accurate prediction of traffic congestion, spotlighting the essence of time series data analysis. This paper delves into the utilization of sophisticated deep learning methodologies, particularly long short-term memory (LSTM) networks, convolutional neural networks (CNN), and their amalgamations like Conv-LSTM and bidirectional-LSTM (Bi-LSTM), to elevate the precision of traffic pattern forecasting. These techniques showcase promise in encapsulating the intricate dynamics of traffic flow, yet their efficacy hinges upon the quality of input data, emphasizing the pivotal role of data preprocessing. This study meticulously investigates diverse preprocessing techniques encompassing normalization, transformation, outlier detection, and feature engineering. Its discerning implementation significantly heightens the performance of deep learning models. By synthesizing advanced deep learning architectures with varied preprocessing methodologies, this research presents invaluable insights fostering enhanced accuracy and reliability in traffic prediction. The innovative RD-LSTM approach introduced herein harnesses the hybridization of a reverse AutoEncoder and LSTM models, marking a novel contribution to the field. The implementation of these progressive strategies within urban traffic management portends substantial enhancements in efficiency and congestion mitigation. Ultimately, these advancements pave the way for a superior urban experience, enriching the quality of life within cities through optimized traffic management systems.
Multimodal approach for early prediction of COVID-19 disease using convolutional neural network Milind Ankleshwar; Pramod Chavan; Pratibha Chavan; Sushil K. Ambhore
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.pp1196-1204

Abstract

The latest human coronavirus is COVID-19. Chest radiography imaging is essential for screening, early detection, and monitoring COVID-19 infections since the virus resides in the lungs. Classical real time reverse transcriptase polymerase chain reaction (RT-PCR) data and chest X-ray pictures will become more important for COVID-19 identification as the pandemic spreads due to their affordability, wide availability, and infection control benefits, which reduce cross-contamination. This work presents multi-modal hybrid automated approaches to classify COVID-19 illness into three clinical categories: normal, pathogenic, and COVID-19 utilising RT-PCR test data and online chest X-ray datasets. The RT-PCR and chest X-ray image datasets were processed using supervised machine learning and convolutional neural networks (CNN). Together, these measures help us separate COVID-19 patients, those with similar symptoms, and healthy persons. The author improved detection times and classification accuracy with extra tree classifier’s feature selection and openCV’s image sharpening. The proposed approaches were tested using a research dataset. The proposed methods allowed reliable COVID-19 disease categorization for clinical decision-making, with random forest (RF) classifier global precision values of 91.58% on the RT-PCR dataset and CNN model accuracy of 95.46% on improved sharpened images.

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