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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 65 Documents
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Search results for , issue "Vol 38, No 3: June 2025" : 65 Documents clear
The impact of COVID-19 on e-commerce: a cross-national analysis of policy implications Cheong, Jia Qi; Tsen, Wong Hock; Abdul Karim, Samsul Ariffin; Cheah, Jeffrey S. S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1946-1956

Abstract

The field of e-commerce research has evolved over recent decades, but the coronavirus disease 2019 (COVID-19) pandemic significantly accelerated its prominence, as evidenced by extensive literature. The pandemic underscored the pivotal role of e-commerce in driving the digital transformation of the global economy. However, there remains a lack of comprehensive reviews in this area, particularly comparative analyses of how different countries leveraged e-commerce to navigate the pandemic’s challenges. This paper addresses this gap by examining the literature on e-commerce adoption and its implications during COVID-19, focusing on select countries, including China, Malaysia, and several European nations. The case of China, as a major economic power in Asia, offers particularly valuable insights.
Adaptive mathematical modeling for predicting and analyzing malware Beketova, Gulzhanat; Manapova, Ainur
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1698-1707

Abstract

In this paper, we propose and investigate an improved mathematical model of malware propagation in network structures based on a modification of the well-known raw-immune-response susceptible-infected-recovered (SIR) model. For detailed numerical analysis, our study introduces the fourth-order Runge-Kutta method, which provides higher accuracy in determining fundamental parameters such as infection, recovery and immunity loss coefficients of network nodes. The obtained simulation results demonstrate that the peak of the epidemic occurs when 34.7% of all nodes are infected, with a peak after 32.5-time units. The main contribution of this work is the in-depth understanding and quantification of cyber threats, which emphasizes the importance of prompt response, regular system software updates, and continuous monitoring of network activity. This research makes a significant contribution to cybersecurity applications by providing quantitative tools and strategies to help strengthen network defenses against malicious attacks. The identified patterns and their numerical interpretation can be integrated into processes for optimizing measures to prevent the widespread spread of malware, thereby enhancing the overall security and stability of networked systems.
For S-band WLAN applications, a patch antenna design, simulation, and optimization Ahmed, Md. Eftiar; Pranto, Biprojitt Saha; Rana, Md. Sohel; Faruq Shakil, Md. Omar; Ala Walid, Md. Abul; Arin, Ifat; Mondal, Saikat; Chooyan, Samanta Mostafa
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1613-1623

Abstract

A rectangular microstrip patch antenna for 2.45 GHz is designed, tested, and analyzed in this study. It uses two substrate materials (design I and II) with different permittivity levels. RT5880 (design-I) and FR-4 (design-II) substrates have a thickness of 1.57 mm and 1.6 mm, respectively. Design-I and design-II substrates have relative permittivity of 2.2 and 4.3, respectively. Performance and efficiency are considered due to the substrate material's relative permittivity and thickness; return loss (S11), voltage standing wave ratio (VSWR), gain, directivity, surface current, and efficiency. Design II and design I have 3.25 dBi and 8.089 dBi gains, respectively, and 5.92 dBi and 8.64 dBi directivity, respectively. Design I had the best antenna efficiency, 93.64%, compared to design II, 54.96%. In contrast to the design I and design II, which had return losses (S11) of -53.29 dB and -51.38 dB, each of the suggested antennas had a return loss (S11) of more than -50 dB. The VSWR for design I is 1.0043, while the Design II material is 1.0054. This study aims to reduce return loss (S11) and close the VSWR to 1. This proposed design improves antenna gain, directivity, and efficiency for future wireless applications on wireless local area networks (WLANs).
Enhanced deep auto encoder technique for brain tumor classification and detection Badashah, Syed Jahangir; Moholkar, Kavita; Bangare, Sunil L.; Gupta, Gaurav; T., Devi; Francis, Sammy; Hariram, Venkatesan; Omarov, Batyrkhan; Rane, Kantilal Pitambar; Raghuvanshi, Abhishek
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp2031-2040

Abstract

A brain tumor can develop due to uncontrolled proliferation of aberrant cells in brain tissue. Malignant tumor can influence the nearby brain tissues, potentially resulting in the person's death. Early diagnosis of a brain tumor is crucial for ensuring the survival of patients. This article introduces an improved method using a deep auto encoder for the classification and detection of brain tumor. Magnetic resonance imaging (MRI) images are obtained from the BraTS data sets. The images undergo preprocessing using an adaptive Wiener filter. Image preprocessing is essential for eliminating noise from the input MRI pictures, hence enhancing the accuracy of MRI image classification. The fuzzy C-means technique is used to accomplish image segmentation. The classification model comprises deep auto encoder, convolution neural network (CNN), and K-nearest neighbor techniques. The classification model is developed and evaluated using MRI image slices from the BraTS dataset. Accuracy of deep auto encoder is 98.81%. Accuracy of CNN is 95.50 and accuracy of K-nearest neighbor (KNN) technique is 91.30%.
Enhancing solar radiation forecasting using machine learning algorithms K. M., Mahesh Kumar; Soundharya, Uppuluri Lakshmi; Hemalatha, R.; C., Anjanappa; M. J., Suganya
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1463-1470

Abstract

With the increasing amount of photovoltaic (PV) generation, accurate solar radiation forecasting is essential to the safe operation of power systems. This work examines many machines learning (ML) techniques that use both exogenous and endogenous inputs to forecast sun radiation. In order to find pertinent input parameters and their values based on previous observations, the forecasting models’ performance is assessed using metrics like mean absolute error (MAE), mean squared error (MSE), R-squared (R2), and root mean squared error (RMSE). Accurate power output forecasting is becoming more and more necessary as the need to switch to renewable energy sources (RES) like solar and wind power grows. There is a clear demand for more reliable solutions because current models frequently struggle with temporal complexity and noise. A revolutionary deep learning-based technique designed especially for green energy power forecasting was developed in response. The study uses time series smoothing and the autoregressive integrated moving average (ARIMA) model for casing in order to create a solid basis for analysis and modeling that is free of noise and outliers. The proposed method aims to address the limitations of existing forecasting methods and promote the creation of more accurate and reliable forecasts in the field of renewable energy.
Quick response code generation for e-invoicing in Saudi Arabia Sayed, Abdelrazek Wahba; Rabea, Zeinab
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1980-1989

Abstract

In the digital era, the emergence of quick response (QR) code technology has become a vital tool for enhancing the efficiency of electronic invoice management and promoting security and transparency in financial transactions, while reducing costs and ensuring compliance with regulations. This study focuses on QR code technology and electronic invoice requirements in the Kingdom of Saudi Arabia, by exploring the generation of QR codes for electronic invoices. The study begins by analyzing QR code technology and its role in encoding and decoding information. Subsequently, the electronic invoice requirements in Saudi Arabia are reviewed, with a focus on the applicable systems and regulations. The research also includes details on generating QR codes for electronic invoices, considering factors such as data encoding, security protocols, and compatibility standards using the Python programming language. Various steps of this process are explained. The study aims to provide a comprehensive understanding of the technology and requirements related to electronic invoices in Saudi Arabia and to develop a program for creating QR codes for electronic invoices to improve and develop the financial and technological infrastructure in the Kingdom of Saudi Arabia, thereby contributing to supporting the digital economy and promoting sustainable development.
IoT based intrusion detection data analysis using deep learning models Baich, Marwa; Sael, Nawal; Hamim, Touria
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1804-1818

Abstract

In both the academic and industrial domains, integration of the internet of things (IoT) is now universally accepted as a significant technical achievement. IoT offers a multitude of security issues despite its many advantages, such as protecting networks and devices, handling resourceconstrained network scenarios, and controlling threats to IoT networks. This article gives a state-of-the-art analysis on the application of multiple deep learning (DL) algorithms in IoT intrusion detection systems (IDS), covering the years 2020 to 2024. Moreover, two popular network datasets, NSL-KDD and UNSW-NB15, are used for an experimental evaluation. The study thoroughly examines and assesses the advantages of well-known deep learning algorithms, including DNN, CNN, RNN, LSTM, and FFDNN. The study demonstrates the exceptional performance of the DNN approach on both datasets, with 99.14% accuracy in multiclass classification in NSLKDD and 99.36% accuracy in binary classification. Furthermore, on UNSWNB15, 82.26% of multiclass classifications and 93.96% of binary classifications with a 42-second minimum running time were achieved, along with an excellent performance in reducing false alarms at a rate of 2.19%.
Plagiarism detection using text-representing centroids techniques Nualnim, Sureeporn; Maliyaem, Maleerat; Unger, Herwig
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1722-1734

Abstract

This study addresses the limitations of traditional plagiarism detection methods by introducing the text-representing centroid (TRC) technique. TRC is designed to improve the accuracy of detecting semantic similarities and sophisticated forms of plagiarism. It utilizes a co-occurrence graph to identify centroid terms that represent the core meaning of text documents, effectively capturing the contextual associations between terms. Extensive experiments were conducted on a dataset of academic papers to assess TRC’s performance against traditional techniques across various categories of plagiarism, including near-copy, modified-copy, and paraphrasing. The results demonstrate the effectiveness of the TRC technique, achieving an average precision of 0.96 and a recall of 0.71. This performance surpasses methods such as Jaccard and Cosine similarity in accurately detecting more, complex forms of plagiarism. These findings highlight TRC’s potential as a robust tool for both academic and industry applications, helping to ensure integrity in textual content through precise and comprehensive plagiarism detection.
Brain tumor classification for optimizing performance using hybrid RNN classifier Gari Kalavathi, Boya Nethappa; Ramamoorthy, Umadevi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1905-1913

Abstract

Tumor is the uncontrolled growth of cancer cells in any part of the human body. Brain tumoris the leading cause of cancer deaths worldwide among adults and childrens. Early detection of brain cancers is essential. To prevent more issues, early defect detection is essential. Healthcare physicians may discover and categorize brain tumors with the use of computational intelligence-focused tools. An essential task for diagnosing tumors and choosing the right type of therapy is classifying brain tumors. Brain tumor identification and segmentation using magnetic resonance imaging (MRI) scans is now recognized as one of the most significant and difficult research areas in the world of medical image processing. The field of medical imaging has gained greatly from the use of artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL). DL has shown significant presentation, especially in the areas of brain tumor classification and segmentation. In this work, brain tumor classification for optimizing performance using hybrid recurrent neural network (RNN) classifier is presented. Different types of brain tumors are classified using a mix of RNN and inception residual neural network (ResNet). This strategy will produce improved F1-score, precision, accuracy, and recall scores.
Machine learning based strategies for managing employee retention: determining factors in hospitality industry Kaja Mytheen, Basari Kodi; Jeyakumar, Murugachandravel; Ramasamy, Kannan; Mani, Geetha; Jayamurugan, Prabhu; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1652-1660

Abstract

In order to boost performance and remain competitive, the Indian hospitality industry must recruit and retain employees if it wants to succeed in the long run. In order to do this, it will need to use a number of staff retention initiatives. It is suggested that effective employee retention tactics be analyzed using machine learning (ML) approaches for prediction. The results show that the hotel industry uses tactics to keep its employees, such as competitive compensation and benefits, opportunities for growth and recognition, safe and healthy workplaces, adaptable schedules, employment stability, and ongoing education and development. There is a noticeable disparity between the hotel industry’s demographics and retention tactics. In the hotel industry, there is a modestly negative correlation between employee desire to depart and employee retention methods. Pay and benefits, recognition and gratitude, a safe and healthy workplace, opportunities for professional growth, and development all play a role in how satisfied hospitality workers are with their jobs. The hotel sector has to implement strong welfare initiatives if it wants its workers to have a healthy work-life balance. The hotel business should promote the development of professional connections among its employees.

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