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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 57 Documents
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Search results for , issue "Vol 39, No 3: September 2025" : 57 Documents clear
Simulation of reactive flow over a parabolic vertical plate using MATLAB Pushparaj, Sivakumar; Ramalingam, Balaji; Adhimoolam, Ramesh; Mohan Reddy, P. Venkata; Srinivasan, Andal; Rajamanickam, Muthucumaraswamy
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1673-1682

Abstract

This article examines how fluid flows around an infinitely large, parabolic-shaped vertical plate, which is heated at an exponentially accelerating rate and undergoes a chemical reaction with the fluid. The plate’s temperature increases at an exponential rate, adding complexity to the heat transfer process. Additionally, the fluid undergoes a chemical reaction in this environment, impacting both the flow and concentration of chemical species. The article includes graphs that show how different parameters such as the rate of temperature increase, strength of thermal radiation, and reaction rate, effect the flow, heat, and concentration profiles. This graphical analysis provides a visual understanding of how each parameter influences the behavior of the fluid.
Pairing mobile users using K-means algorithm on PD-NOMA-based mmWaves communications system Abdelkhaliq, Litim; Yassine, Bendimerad Mohammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1595-1607

Abstract

In this research, we study the effectiveness of the K-means machine learning (ML) clustering approach for pairing mobile users on a power domain nonorthogonal multiple access (PD-NOMA) single input single output (SISO) downlink-based millimeter-wave (mmWave) communication system. The basic concept is to pair the mobile users by using a data set that contains essential information about the mobile users in the micro cell base station (BS) (e.g., the SNR, the distance between the mobile users and the BS, the channel gain, and the data rate of each mobile user). The study conducted in this paper demonstrates that the proposed K-means clustering-based scheme achieves a balance between computational complexity and performance metrics. It outperforms single carrier NOMA (SC-NOMA), the conventional NOMA pairing scheme, and time division multiple access (TDMA), offering an effective trade-off between system efficiency and implementation feasibility.
An innovative approach to Raga pattern identification Chakrabarty, Sudipta; Rai, Prativa; Islam, Md Ruhul; Deva Sarma, Hiren Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1865-1876

Abstract

Raga is a fundamental element of Indian classical music (ICM), crucial for identifying the unique characteristics of a given song. Recognizing the embedded Raga allows for various applications, including music therapy, and leveraging the therapeutic effects of different Ragas. The use of mathematical techniques such as fast fourier transform (FFT) and fundamental frequency measurement (FFM) in calculating note values has proven effective for Raga pattern recognition. Both methods yield nearly identical results, facilitating accurate identification of Ragas. Once identified, these Ragas can be used for specific therapeutic purposes, harnessing their healing potential.
Prediction of broiler shear force using near infrared spectroscopy with second derivative linear modeling Ghazali, Rashidah; Rahim, Herlina Abdul; Zulkifli, Syahidah Nurani
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1787-1794

Abstract

This study explores the use of linear predictive models, specifically principal component regression (PCR) and partial least squares (PLS), in combination with a cost-effective near infrared spectroscopy (NIRS) system to noninvasively assess the texture of raw broiler meat. The findings demonstrate that appropriate pre-processing techniques, such as excluding the visible spectrum and applying the second-order Savitzky-Golay (SG) derivative with an optimal filter length (FL), enhance model performance. Notably, the PLS model outperformed PCR, requiring fewer latent variables (LVs) to achieve accurate predictions. This suggests that PLS more effectively captures key spectral features associated with meat texture, making it a promising approach for assessing raw broiler meat quality in a practical, cost-efficient, and non-invasive manner. These results highlight the potential of integrating linear predictive models with NIRS technology for reliable texture analysis in the poultry industry.
Sentiment analysis resource of Libyan dialect for Libyan Airlines Ebrahem, Hassan Ali; Touati, Imen; Belguith, Lamia
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp2001-2011

Abstract

Arabic lacks extensive corpora for natural language processing (NLP) when compared to other languages, namely in the Libyan dialect (LD). Therefore, this study proposes the first corpus of Arabic sentiment analysis (ASA) of the Libyan Dialect for the Airline Industry (ASALDA). It comprises 9,350 comments and tweets, annotating them manually depending on text polarity into three labels: positive, negative, and neutral, and utilized aspect-based sentiment analysis (SA) to annotate opinions regarding fifteen aspects. Also constructs a simple sentiment lexicon of the LD. The solution is based on the idea that the corpus and lexicon can be helpful models to improve classification for the LD. The approach has notable merits, namely creating a corpus and sentiment lexicon for the LD from comments and tweets of airline companies. A comprehensive verification using a statistical technique called the chi-square test is carried out with the corpus to determine if two aspects are related to one another. Based on the statistical work, we found that airlines should focus on improving their services in aspects where they are performing poorly, such as late flights, customer service, or price. The corpus and lexicon that we proposed can be utilized to perform many opinion mining and SA experimentations using machine learning and deep learning.
Machine learning approach for cost estimation in software project planning Jaiswal, Ajay; Raikwal, Jagdish
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1724-1735

Abstract

Successful organizing and handling of software projects depends extensively on accurate cost estimation. This study explores the effectiveness of machine learning models in estimating software project costs using datasets like Desharnais, Maxwell, and Kitchenham, aiming to prevent project delays and resource misallocation. It shows how model selection has a major impact on forecast accuracy through thorough assessment. An R-squared value (R2) of 0.804 indicates that the support vector machine (SVM) model performs exceptionally well in the Desharnais dataset. On the Maxwell dataset, linear regression (LR) stands out with a minimum mean absolute error (MAE) of 0.483 and the greatest R2 value of 0.607, while SVM has the lowest root mean squared error (RMSE) of 0.537. Similarly, on the Kitchenham dataset, LR and SVM are the top performers, with MAE of 0.201 and RMSE of 0.274, respectively, and R2 values of around 0.929. These findings highlight the importance of tailored model selection for accurate cost prediction, as LR and SVM continuously demonstrate reliability across varied datasets. ML techniques like LR and SVM can enhance software project planning and management by providing accurate cost estimation, with future research exploring ensemble learning and deep learning methodologies.
Energy-efficient and reliable data transmission to enhance the performance of wireless sensor networks using artificial intelligence Ch, Swapna; Budyal, Vijayashree R.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1946-1954

Abstract

For many years, the area of wireless sensor networks (WSN) has been popular for its wide range of time-critical and potential applications. However, it has many challenges that require more attention from the research communities to improve the network’s operational efficiency. However, with consistently rising concerns for energy efficiency and optimized data transmission performance, most current research emphasises minimum power consumption and reliable data transmission aspects. The critical analysis and study of related works exhibit the shortcomings in existing data transmission schemes, which fail to cope with the dynamic conditions of WSNs on a larger scale and do not retain considerable energy performance. The study thereby introduces a unique approach to an energy-efficient and reliable data transmission framework that formulates machine learning-driven functional components to ensure effective data gathering, aggregation, and routing and dissemination strategies to properly balance energy and data transmission performance in WSN under dynamic conditions. The proposed framework's performance evaluation considers multiple metrics, such as analysis of network lifetime, Energy Consumption, Throughput, and Latency performance. The experimental outcome shows that the proposed system outperforms the existing baselines for the above performance metrics.
Constructing dynamic XOR charts for block ciphers using hadamard matrices Phuong, Truong Minh; Luong, Tran Thi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1642-1651

Abstract

Block ciphers are vital for modern encryption, ensuring the security of digital communications. Currently, powerful attacks target block ciphers, prompting researchers to propose ideas to enhance their cryptographic strength. One notable concept involves making components dynamic and dependent on a secret key, with limited attention given to the dynamic AddRoundKey operation. In this article, we introduce the definitions of some Hadamard matrix forms like B_had, N_had, and NB_had matrices. Subsequently, we present an algorithm for generating key-dependent XOR charts to create a key-dependent AddRoundKey operation based on these matrices. We then construct a dynamic AES block cipher by applying the proposed AddRoundKey operation to AES. We implement the dynamic AES algorithm, assess its security, and evaluate AES and the advanced AES using NIST’s statistical standards. The dynamic AES algorithm exhibits improved resistance against strong block cipher attacks compared to conventional AES.
Core machine learning methods for boosting security strength for securing IoT Pavithran, Sneha Nelliyadan; Gorabal, Jayanna Veeranna
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1891-1899

Abstract

Internet-of-things (IoT) revolutionized the mechanism of larger scale of network system offering more engaged, automated, and resilient data dissemination process. However, the resource-limited IoT devices potentially suffers from security issues owing to various inherent weakness. Artificial intelligence (AI) and machine learning (ML) has evolved more recently towards boosting up the security features of IoT offering a secure environment with higher privacy. Till date, there are various review papers to discuss elaborately security aspect of an IoT; however, they miss out to present the actual gap existing between commercial available products and research-based models. Hence, this paper contributes towards discussing the core taxonomy of evolving security methods using ML along with their research trend to offer better insight to existing state of effectiveness. The study further contributes towards highlighting the potential trade-off between the real-world solution and on-going ML based approaches.
Load frequency control for multi-area power system with two-source using sliding mode control Thai Phan, Quoc; Lam-The Tran, Thinh; Tuan Le, Phat; Bao Ho, Dinh; Van Huynh, Van
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1449-1458

Abstract

A consistent electrical supply relies on the stability of power systems. In changing load conditions, control methods like load frequency control (LFC) are essential for safeguarding its stability. Conventional methods of LFC frequently encounter uncertainties in the system, external disruptions, and nonlinearities. This article introduces a more sophisticated method for managing load frequency and improving LFC in power systems through the utilization of sliding mode control (SMC). SMC provides strong stability and resilience against nonlinearities and disturbances, making it a promising method to overcome the drawbacks of traditional control techniques. We offer an in-depth examination of the second-order-integral SMC (SOISMC) method specifically designed for LFC, covering the creation and execution of the control algorithm. The method being suggested utilizes a sliding/gliding surface to maintain the system trajectories as continuous on the surface even with changes in parameters and external disturbances. Simulation results show big enhancements in frequency stability and system performance when compared to conventional proportional-integral-derivative (PID) controllers. The article also features a comparison between SOISMC and other contemporary control methods, emphasizing its strength in terms of resilience and flexibility.

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