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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 1: April 2025" : 65 Documents clear
Link adaptation techniques for throughput enhancement in LEO satellites: a survey Idmouida, Habib; Minaoui, Khalid Minaoui
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp262-271

Abstract

In addition to the rapid geometric change of low earth orbit (LEO) satellites, the earth-to-space channel suffers from various attenuations that affect the communication link. To overcome this challenge, the link adaptation technique emerges as a key solution to optimize the transmission performance of LEO satellites, especially the data throughput. The existing contributions in the literature remain scattered across the research board, and a comprehensive survey of this research area still lacks at this stage. The present survey examines various link adaptation methods, mainly variable coding and modulation, adaptive coding and modulation, and hybrid methods using artificial intelligence. In addition, this study explains how this technique leverages a set of recommended standards and cost-effective technologies, such as software-defined radio (SDR) and field programmable gate arrays (FPGA), to fine-tune transmission strategies. Lastly, the paper provides a comparative study of the current research on this field and sheds light on future directions, where the need for higher data throughput makes emerging learning-based techniques and new experimental standards a necessity.
Video mosaic: employing an efficient ORB feature extraction technique with hamming distance matching for enhanced performance H, Shridhar; Harakannanavar, Sunil S.; Kanabur, Vidyashree; H, Jayalaxmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp161-171

Abstract

Video mosaicing is a computer vision and image processing technique used to create a panoramic or wide-angle view from a sequence of video frames. The goal is to seamlessly combine multiple video frames to form a larger and more comprehensive view of a scene. In recent years, the field of image processing has witnessed a growing interest in video mosaic research owing to its application in surveillance and defense applications. This paper introduces an automatic algorithm for video mosaic creation, addressing the alignment and blending of non-overlapping frames within each input video. The proposed algorithm navigates through several key steps to achieve a seamless and continuous mosaic, particularly tackling issues related to camera motion and content variations across frames. The effect of the good number of matches to be chosen while performing frame stitching is evaluated. The proposed algorithm effectively produces a video mosaic with aligned and blended non-overlapping frames, resulting in a visually continuous mosaic. The output video serves as a testament to the algorithm’s prowess in addressing challenges related to video frame alignment and blending.
An ensemble approach for detection of diabetes using SVM and DT Vamsikrishna, Mangalapalli; Gupta, Manu; Bagade, Jayashri; Bhimanpallewar, Ratnmala; Shelke, Priya; Bodapati, Jagadeesh; Komali, Govindu; Mande, Praveen
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp689-698

Abstract

As diabetes affects the health of the entire population, it is a chronic disease that is still an important worldwide health issue. Diabetes increases the possibility of long-term complications, such as kidney failure and heart disease. If this disease is discovered early, people may live longer and in better health. In order to detect and prevent particular diseases, machine learning (ML) has become essential. An ensemble approach for detection of diabetes using support vector machine (SVM) and decision tree (DT) presents in this paper. In this case, to identify diabetes, two ML techniques are DT and SVM have been combined with an ensemble classifier. They obtain the information, they require from the Public Health Institute’s statistics area. There are 270 records, or instances, in the collection. This dataset includes the following attributes: age, a body mass index (BMI) glucose, and insulin. The development of a system that predictions a patient’s risk of diabetes is the goal of this analysis. Several performance metrics, including F1-score, recall, accuracy, and precision, were used to achieve this. From overall results, 96% of precision, 97% of accuracy, 96% of F1-score, and 97% of recall values are the results achieved for the ensemble model (SVM+DT) which is more effective than other individual ML models as DT and SVM.
Modeling non-linear communication systems using neural networks Fateh, Rachid; Ougraz, Hassan; Belaadel, Youness; Pouliquen, Mathieu; Frikel, Miloud; Safi, Said
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp215-226

Abstract

Nonlinear systems present significant modeling challenges due to their complex dynamics and often unpredictable behavior. Traditional mathematical approaches can struggle to represent such systems accurately. In recent years, neural networks have emerged as promising tools to address this challenge. This article explores the use of neural networks to model nonlinear systems, focusing specifically on the application of the Hammerstein system. We examine network architecture and training methodologies suited to the complexity of nonlinear dynamics. Additionally, we explore strategies to improve the interpretability of neural network models in this context, enabling a better understanding of the underlying behavior of the system. Through a case study and empirical evaluations, we demonstrate the effectiveness of neural network-based approaches for estimating the behavior of nonlinear systems. Our work highlights the potential of neural networks as a versatile and powerful tool for modeling complex nonlinear phenomena.
Development of clustering with Bayesian algorithm for optimal route formation in software-defined radio underwater WSN Sreeraj, Anoop; P, Vijayalakshmi; Rajendran, Velayutham
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp254-261

Abstract

Underwater wireless sensor networks (UWSNs) have recently offered chances to investigate oceans and thus enhance the underwater world. WSNs are imperative for discovering the ocean region. Software-defined networking (SDN) improves flexibility and uses the clustering method to improve lifespan. This article introduces the Development of a clustering process with a Bayesian algorithm (CPBA) for optimal route formation in software-defined radio UWSN. The clustering concept improves energy efficiency; however, cluster head (CH) selection is challenging. The present clustering mechanisms could be more successful in suitably assigning the node's energy. This mechanism utilizes a slap swarm optimization algorithm to pick out the optimal CH by node energy and distance among inter-cluster as well as intra-cluster. In addition, the Bayesian algorithm selects the best forwarder from sender to base station. Thus, enhances efficiency. The simulation results demonstrate that the UWSN improves both the 23% packet forward ratio and 0.014 joule energy. Furthermore, it minimizes the 30% network delay.
Virtual exhibition systems using virtual reality technology Winarno, P. M.; Istiono, Wirawan; Abhinaya, Rajendra
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp367-380

Abstract

Exhibitions are an activity that can bring a lot of benefits to a company. By participating in an exhibition, a company can carry out promotions to increase their sales and improve their company image. However, there are several shortcomings that can be found with conventional exhibitions held in a face-to-face manner. These exhibitions cost a lot of money, run for only a relatively short period of time, and are limited by the location of the exhibition. Because of this, the idea came up to create a virtual exhibition system which could be used as an alternative to conventional exhibitions. The development of a virtual exhibition system for this research was carried out using the Unity game engine. At the virtual exhibition, users can choose which exhibition they want to visit and enter the exhibition room view products and find information about them. Evaluation is carried out using a user acceptance test with Likert scale questions. The evaluation results show a user satisfaction level of 92.7% among the 18 users who have tested the application. With this, it can be said that the virtual exhibition system based on virtual reality technology has been successfully built.
Machine learning-based intelligent result compilation RPA bot for higher education institutions Yadav, Neelam; Panda, Supriya P.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp587-594

Abstract

Educators are essential for societal progress, and well-educated students are pivotal for a promising future. Higher education faces challenges such as budget constraints, limited time, and a shortage of trained personnel, leading to faculty stress. Emerging technologies such as artificial intelligence (AI), machine learning (ML), and block chain provide solutions, with robotic process automation (RPA) bots a notable advanced AI subfield-automating repetitive tasks, thereby freeing teachers to focus on more essential responsibilities. RPA bots automate various educational processes, including examinations, admissions, marks updating, student record management, result compilation, human resources, resume screening, and administration. This research examines robotic automation in higher education institutions (HEIs), selecting and prioritizing RPA tasks through a survey involving subject matter experts (SMEs) from different HEIs, including professors and RPA experts. The research aims to develop a “virtual software bot” for automating “result compilation” post-examination. Using tools like XPATH, Whisper, and the web-based automation program Selenium web in Python, the bot automates this process. The ML library “Whisper” addresses the reCAPTCHA problem. The automated bot generates comma separated values (CSV) files in specific formats, completing the task 58 times faster than humans and saving 43 man-hours by compiling results for 653 students in 45 minutes.
Analysis of VFDPC for three-level neutral point clamped AC-DC converters with capacitor balancing solution Razali, Azziddin Mohamad; Mohd Yusoff, Nor Azizah; Ab Shukor, Syahar Azalia; Mohamed Hariri, Muhammad Hafeez; Jidin, Auzani; Sutikno, Tole
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp63-75

Abstract

This paper presents an analysis of the dynamic performance of a three-level neutral point clamped (NPC) AC-DC converter utilizing the advanced control technique of virtual flux direct power control (VFDPC). VFDPC estimates the three-phase grid voltage and instantaneous active and reactive power components, eliminating the need for an AC input voltage sensor used in conventional direct power control (DPC). This reduction in sensors decreases system complexity and cost while mitigating high-frequency noise and interference. Integrating VFDPC into 3L NPC AC-DC converters significantly enhances overall performance, leading to more efficient and robust power conversion systems. However, a significant challenge in the three-level NPC topology is the voltage imbalance in the neutral point of the DC-link capacitor, which can cause excessive voltage stress on switching devices and degrade system performance. To address this, a novel lookup table has been developed, incorporating strategies to balance the capacitor voltage. The results of this study demonstrate that VFDPC generates nearly sinusoidal line currents with reduced current total harmonic distortion (THD). Additionally, VFDPC ensures unity, lagging, and leading power factor operation, while providing flexibility to adjust the DC-link output voltage and accommodate load variations. These capabilities highlight VFDPC effectiveness in managing power quality and system stability, even under varying load conditions.
An intelligent intrusion detection system to prevent URL redirection attack Sadanand, Vijaya Shetty; Naidu, Palamaneni Ramesh; Bolla, Dileep Reddy; Neeli, Jyoti; Prakash, Ramya
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp527-534

Abstract

In today’s digital age, the widespread use of social networking platforms like Facebook, Twitter, and Instagram, alongside messaging services such as Email and WhatsApp, has increased the convenience of communication. However, this accessibility has also provided a fertile ground for cybercriminals and spammers to exploit these platforms through URL redirection attacks, which are often used to steal sensitive user information. Existing solutions, including machine learning (ML), deep learning (DL), and ensemble methods have been employed to combat such threats. Despite their effectiveness, these approaches struggle to detect emerging types of attacks and suffer from limitations when dealing with imbalanced data, leading to reduced detection performance. To address these challenges, this research introduces an improved extreme gradient boosting (IXGB) algorithm that optimizes the weight adjustments in the model, aiming to enhance the detection of malicious URLs. The proposed method focuses on improving classification accuracy, especially for new or unseen types of attacks. Experimental results on a standard dataset demonstrate that IXGB achieves superior accuracy compared to traditional models, making it a promising approach for enhancing cybersecurity on social media and messaging platforms.
Diabetes detection and prediction through a multimodal artificial intelligence framework Kulkarni, Gururaj N.; Kelapati, Kelapati
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp459-468

Abstract

Diabetes detection and prediction are crucial in modern healthcare, requiring advanced methodologies and comprehensive data analysis. This study aims to review the application of multi-parameters and artificial intelligence (AI) techniques in diabetes assessment, identify existing research limitations and gaps, and propose a novel multimodal framework for enhanced detection and prediction. The research objectives include evaluating current AI methodologies, analyzing multi-parameter integration, and addressing challenges in early detection and model evaluation. The study utilizes a systematic review approach, analyzing recent literature on AI-based diabetes detection and prediction, focusing on diverse data sources and machine learning (ML) techniques. Findings reveal a significant lack of integration of diverse data sources, limited focus on early detection strategies, and challenges in model evaluation. The study concludes with a proposed innovative framework for more accurate and personalized diabetes detection, contributing to the advancement of diabetes research and highlighting the potential of AI-driven healthcare interventions. This research underscores the importance of comprehensive data integration and robust evaluation methods in enhancing diabetes detection and prediction.

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