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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 36, No 3: December 2024" : 65 Documents clear
Influence of the use of ground enhancement materials on the reduction of electrical resistivity in grounding systems: a review Hugo, Martínez Ángeles; José Gabriel, Ríos Moreno; Rodrigo Rafael, Velázquez Castillo; Mario, Trejo Perea
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1365-1378

Abstract

A grounding system (GS) is an indispensable component in an electrical system network, as it is responsible for conducting electrical discharges to the ground due to faults caused by lightning strikes or transient system failures. Globally, it is estimated that 40 lightning strikes occur per second on the planet, amounting to around 1.2 billion per year, resulting in daily losses of various electrical equipment and human fatalities ranging from 6,000 to 24,000. Additionally, soil resistivity, which impedes the flow of electricity from electrical discharges into the ground, leads to inadequate mitigation of electrical overload effects, resulting in poor GS performance. Consequently, the implementation of ground enhancement materials (GEMs) to reduce impedance to optimal levels becomes necessary. The objective of this review is to broadly examine the current status of GEMs reported in the literature for use in GS, focusing on their composition and their effectiveness in improving soil conductivity and dissipating electrical currents as well as to identify emerging trends and current challenges in the development and application of these materials, in order to provide information to guide future research in the design and implementation of efficient and safe GS.
Probing the depths: assessing the efficacy of the two-tier deception-driven security model Gamilla, Anazel P.; Palaoag, Thelma D.; Naagas, Marlon A.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1631-1639

Abstract

In the age characterized by relentless cyber threats, the need for innovative and proactive security measures has never been more important. Deception is defined as the deliberate structure of tricks, traps, and false information to mislead and discourage threats, while providing timely warning signals and useful information to defenders. The two-tier deception-driven security model's implementation focuses on applying deception security techniques to deceive potential attackers and protect network resources, with an emphasis on a proactive defense approach. The study emphasized the deployment and deep testing of the model, which aims to assess its efficacy and feasibility in real-time practice. The study shows that the two-layered approach effectively defends the network within the multiple layers using a combination of decoys, honeypots, and deceptive network segments. The deception security model effectively prevents and confuses potential threats, improving the network's overall resilience and threat defense capabilities. The findings suggest that integrating deception techniques into cybersecurity frameworks can provide a robust layer of protection against evolving cyber threats. Furthermore, this research contributes to the ongoing discourse on proactive cybersecurity strategies and offers practical insights for improving network defense mechanisms.
Stacking-based ensemble learning for identifying artist signatures on paintings Hidayati, Shintami Chusnul; Irawan Rahardja, Agustinus Aldi; Suciati, Nanik
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1683-1693

Abstract

Identifying artist signatures on paintings is essential for authenticating artworks and advancing digital humanities. An artist’s signature is a consistent element included in each painting that the artist creates, providing a unique identifier for their work. Traditional methods that rely on expert analysis and manual comparison are time-consuming and are prone to human error. Although convolutional neural networks (CNNs) have shown promise in automating this process, existing single-model approaches struggle with the diversity and complexity of artistic styles, leading to limitations in their performance and generalizability. Therefore, this study proposes an ensemble learning approach that integrates the predictive power of multiple CNN-based models. The proposed framework leverages the strengths of three state-of-the-art CNNs: EfficientNetB4, ResNet-50, and Xception. These models were independently trained, and the predictions were combined using a meta-learning strategy. To address class imbalance, data augmentation techniques and weighted loss functions were employed. The experimental results obtained on a dataset of more than 8,000 paintings from 50 artists demonstrate significant improvements over individual CNN architectures and other ensemble methods, thereby effectively capturing complex features and improving generalizability.
Android malware detection using the random forest algorithm El Attaoui, Anas; El Hami, Norelislam; Koulou, Younes
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1876-1883

Abstract

The rapid growth in Android device usage has resulted in a significant increase in malware targeting this platform, posing serious threats to user security and privacy. This research tackles the challenge of Android malware detection by leveraging advanced machine learning techniques, with a particular emphasis on the random forest (RF) algorithm. Our primary objective is to accurately identify and classify malicious applications to enhance the security of Android devices. In this study, we employed the RF algorithm to analyze a comprehensive dataset of Android applications, where the classification of each application as either malware or benign is known. The method was rigorously tested, yielding impressive results: an average accuracy of 98.47%, a sensitivity of 98.60%, and an F-score of 98.60%. These metrics underscore the effectiveness of our approach. Moreover, we conducted a comparative analysis of the RF algorithm against other malware detection methods. The results demonstrate that the RF algorithm outperforms these alternative methods, offering superior detection capabilities and contributing to more robust Android security measures.
Mitigating blackhole attacks in wireless body area network Abdessamad, Goumidi Mohammed; Ehlem, Zigh; Adda Belkacem, Ali-Pacha
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1555-1563

Abstract

In this paper, we aimed to develop a trusted secured routing Ad-hoc on-demand distance vector (AODV) protocol to fight against blackhole attacks within the wireless body area network (WBAN). The trusted secure routing protocol incorporates a routing strategy based on trust value to detect malicious nodes based on their trust value, a routing technique based on node residual energy to select the node with the highest residual energy during the communication process, and a hybrid cryptography algorithm that merges the Affine cipher with the modified RSA cipher algorithm to secure communication against malevolent biomedical sensor attacks. Simulation outcomes demonstrate that the suggested protocol outperforms the traditional AODV routing protocol in all evaluation metrics, including data rate, energy consumption, and packet delivery ratio. Its main strength is that it considers several factors, like illegitimate medical sensor detection, efficient network energy use, and secure data transmission, unlike similar secured routing protocols. Furthermore, the hybrid cipher algorithm improves the effectiveness and increases the security level of sensitive data compared to traditional cipher algorithms such as the Affine cipher and the RSA cipher.
Recognizing Indonesian sign language (Bisindo) gesture in complex backgrounds Saputra, Muhammad Alfhi; Rakun, Erdefi
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1583-1593

Abstract

Sign language, particularly Indonesian sign language (Bisindo), is vital for deaf individuals, but learning it is challenging. This study aims to develop an automated Bisindo recognition system suitable for diverse backgrounds. Previous research focused on greenscreen backgrounds and struggled with natural or complex backgrounds. To address this problem, the study proposes using Faster region-based convolutional neural networks (RCNN) and YOLOv5 for hand and face detection, MobileNetV2 for feature extraction, and long short-term memory (LSTM) for classification. The system is also designed to focus on computational efficiency. YOLOv5 model achieves the best result with a sentence accuracy (SAcc) of 49.29% and a word error rate (WER) of 16.42%, with a computational time of 0.0188 seconds, surpassing the baseline model. Additionally, the system achieved a SacreBLEU score of 67.77%, demonstrating its effectiveness in Bisindo recognition across various backgrounds. This research improves accessibility for deaf individuals by advancing automated sign language recognition technology.
Pneumonia stage analyzes through image processing Chowdhury, Nishu; Choudhury, Pranto Protim; Moon, Shatabdi Roy
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1778-1786

Abstract

A physical examination and diagnostic imaging techniques including lung biopsies, ultrasounds, and chest X-rays are typically used to make the diagnosis of pneumonia infection, an infectious disease that has the potential to be life-threatening. The objective of this research is to categorize the stages of pneumonia through image processing methods. Before that, an ensemble model for diagnosing pneumonia infections is created utilizing the transfer learning algorithms ResNet50V2 and DenseNet201. The 5,857 images were taken from the PAUL MOONEY dataset for this research. The proposed ensemble averaging model recognizes lung infection appropriately and accurately. By applying a contour detection approach, the left and right chests are separated and the affected pixels from there to analyze the stage of pneumonia. It is very crucial to identify the stage for treatment purposes.
Identification and characterisation of earthquake clusters from seismic historical data Markhaba, Karmenova; Aizhan, Tlebaldinova; Karlygash, Alibekkyzy; Zheniskul, Zhantassova; Indira, Karymsakova
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1594-1604

Abstract

New approaches and methods based on machine learning technologies make it possible to identify not only the spread of earthquakes, but also to establish hidden patterns that allow further assessment of any risks associated with their occurrence. In this article, the clustering algorithms of K-means and K-medoids are applied for the analysis of seismic data recorded on the territory of the Republic of Kazakhstan. Using the Elbow and Silhouette methods, the optimal value of K clusters was determined, which was later used in classifying a data set using cluster analysis methods. The results of seismic data classification by clustering algorithms are in line with expectations. However, when measuring the quality of clustering, the accuracy of the model by the K-means method exceeded the accuracy of the K-medoids model, and the scoring value by the K-means method is ahead of the value by the K-medoids method. In addition, the presented results of descriptive statistics allowed to carry out a more in-depth analysis of the characteristics of each cluster.
Colour sorting ROS-based robot evaluation under different lights and camera angles Saaid, Mohammad Farid; Thamrin, Norashikin M.; Misnan, Mohamad Farid; Mohamad, Roslina; Romli, Nurul A’qilah
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1807-1815

Abstract

Automated colour sorting, aided by mobile robots, is widely prevalent in the current manufacturing industry. Obstacles, such as fluctuating light conditions and camera angles, frequently hinder this procedure. Creating a colour sorting robot is a complex and time-consuming task, especially due to the vulnerability of the RGB colour space to detection errors in extreme brightness or darkness. In response to these concerns, we introduce a mobile robot that operates on the robot operating system (ROS) platform and incorporates OpenCV. This robot employs the hue, saturation, and value (HSV) colour space model for its image processing capabilities in recognising the colours and Welzl’s algorithm for the ball’s diameter estimation. The robot’s performance was assessed across various luminous fluxes and camera tilt angles. It demonstrated exceptional performance at 64 lm and a tilt angle of 40 degrees, achieving an average accuracy of 87.5% for detecting the colour of the ball, and 81.25% for determining its location based on colour. For the ball’s diameter estimation, it was found that the best estimation was received at 64 lm and 30 degrees, with both 96.32%.
Integrating computing techniques in preserving Makassar’s Pakarena dance heritage Syahrir, Nurlina; Alimuddin, Alimuddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1674-1682

Abstract

This interdisciplinary study bridges the cultural heritage of the Pakarena dance, a key element of Makassar ethnicity, with contemporary computing and informatics to examine its social implications, particularly in terms of identity formation, community transmission, and narrative preservation. Emphasizing the dance’s crucial role within the Makassar community, the research employs digital technologies for comprehensive data collection, analysis, and dissemination. Utilizing a qualitative framework supplemented by digital ethnography, the methodology includes in-depth interviews and participant observation, enriched with advanced data analysis and virtual reality (VR) presentations. This innovative approach facilitates the digital capture of the narratives and experiences of dance practitioners, cultural experts, and community members, ensuring the preservation of the dance’s cultural narrative and expression. The study reveals that the Pakarena dance is not only a bearer of the Makassar community’s history and traditions but also a platform for individual creativity and cultural identity, adapting while preserving its core in the face of societal shifts. The findings highlight the potential of computing and informatics in cultural preservation, suggesting new methods for documenting, analyzing, and promoting intangible cultural heritage. The study advocates for the use of technology to enhance and perpetuate cultural heritage, especially for younger generations, in our increasingly digital era.

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