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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 35, No 3: September 2024" : 65 Documents clear
Development of a machine learning algorithm for fake news detection Sia Abdullah, Nur Atiqah; Aniza Rusli, Nur Ida; Yuslee, Nurshaheeda Shazlin
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1732-1743

Abstract

With the extensive technological advancements and expansion, the persistent issues regarding the creation and rapid dissemination of fake news have become a prevalent and recurrent concern. The manipulation of news content has critical repercussions, such as causing public mistrust, fear, harm, and misinformation. Addressing that, this study developed a supervised machine learning algorithm that can accurately classify social media data as fake news. The methodology of the proposed fake news detection model involved five main components: data acquisition from Twitter, data preprocessing, data transformation, model development using Naïve Bayes, decision tree, and support vector machine (SVM) and model evaluation using accuracy, precision, recall and F1-score. The results revealed that decision tree recorded the highest accuracy for both textual data (100%) and metadata (94.54%) and consistently outperformed both Naïve Bayes and SVM in terms of precision, recall, and F1-score metrics, with a score of 100% for the classification of textual data-based datasets. Regarding the metadata-based classification, decision tree also demonstrated excellent performance, with the highest F1-score of 94% for fake news data. Meanwhile, SVM exhibited the highest precision and recall performance for the metadata-based classification. Overall, the application of the decision tree classifier was deemed the most effective in Twitter fake news detection.
Temporal attention network for CNNE model of variable-length ECG signals in early arrhythmia detection Karthikeyan, Poomari Durga; Abirami, M. S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1517-1525

Abstract

Cardiac arrhythmia identification and categorization are crucial for prompt treatment and better patient outcomes. Arrhythmia identification is the main focus of this study's temporal attention network (TAN)-based multiclass categorization of varied-length electrocardiogram (ECG) data. The suggested TAN is designed to handle variable-duration ECG signals, making it ideal for real-time monitoring. The TAN uses a dynamic snippet extraction approach to choose meaningful ECG segments to ensure the model captures essential properties despite the constraints of processing such heterogeneous data. Training and assessment use a large dataset of atrial fibrillation, ventricular, and supraventricular arrhythmias. The TAN outperforms current approaches in multiclass early arrhythmia classification and is very accurate. Concatenating EfficientNet with CNN layer helped overcome different data and variable-length signals. High accuracy: 98% of normal, 97.1% of atrial fibrillation (AF), 98% of other, and 98% of noisy using the proposed CEEC model. Early arrhythmia diagnosis has improved due to the TAN's ability to effectively identify varied-length ECG data and give interpretability. It enables quicker interventions, personalised treatment plans, and improved arrhythmia control, which can greatly benefit patient care.
Detection of cyberattacks using bidirectional generative adversarial network Vallabhaneni, Rohith; Vaddadi, Srinivas A.; Somanathan Pillai, Sanjaikanth E Vadakkethil; Addula, Santosh Reddy; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1653-1660

Abstract

Due to the progress of communication technologies, diverse information is transmitted in distributed systems via a network model. Concurrently, with the evolution of communication technologies, the attacks have broadened, raising concerns about the security of networks. For dealing with different attacks, the analysis of intrusion detection system (IDS) has been carried out. Conventional IDS rely on signatures and are time-consuming for updation, often lacking coverage for all kinds of attacks. Deep learning (DL), specifically generative methods demonstrate potential in detecting intrusions through network data analysis. This work presents a bidirectional generative adversarial network (BiGAN) for the detection of cyberattacks using the IoT23 database. This BiGAN model efficiently detected different attacks and the accuracy and F-score values achieved were 98.8% and 98.2% respectively.
Enhancing IoT network defense: advanced intrusion detection via ensemble learning techniques El Hajla, Salah; Ennaji, El Mahfoud; Maleh, Yassine; Mounir, Soufyane
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp2010-2020

Abstract

The Internet of Things (IoT) has evolved significantly, automating daily activities by connecting numerous devices. However, this growth has increased cybersecurity threats, compromising data integrity. To address this, intrusion detection systems (IDSs) have been developed, mainly using predefined attack patterns. With rising cyber-attacks, improving IDS effectiveness is crucial, and machine learning is a key solution. This research enhances IDS capabilities by introducing binary attack identification and multiclass attack categorization for IoT traffic, aiming to improve IDS performance. Our framework uses the ‘BoT-IoT’ and ‘TON-IoT’ datasets, which include various IoT network traffic and cyber-attack scenarios, such as DDoS and data infiltration, to train machine learning and ensemble models. Specifically, it combines three machine learning models-decision tree, resilient backpropagation (RProp) multilayer perceptron (MLP), and logistic regression-into ensemble methods like voting and stacking to improve prediction accuracy and reduce detection errors. These ensemble classifiers outperform individual models, demonstrating the benefit of diverse learning techniques. Our framework achieves high accuracy, with 99.99% for binary classification on the BoT-IoT dataset and 97.31% on the ToN-IoT dataset. For multiclass classification, it achieves 99.99% on BoT-IoT and 96.32% on ToN-IoT, significantly enhancing IDS effectiveness against IoT cybersecurity threats.
Urban traffic congestion and its association with gas station density: insights from Google Maps data Hasabi, Rafif; Kurniawan, Robert; Sugiarto, Sugiarto; Tri Wahyuni, Ribut Nurul; Nurmawati, Erna
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1618-1626

Abstract

Analyzing air pollution caused by traffic conditions requires appropriate indicators. Currently, air pollution indicators are approximated by the number of vehicles and gas station density. However, this approach cannot provide information at a smaller level. This study aims to identify traffic congestion distribution from Google Maps data as an alternative air pollution indicator at smaller level using map digitization method. In addition, this study examines its relationship with the existing indicator called gas station density. The results show that the digitization method can map the traffic congestion distribution where most areas in West, North, and Central Jakarta are classified as high traffic. In addition, this study found that there is a strong and significant relationship of 0.58277 between traffic congestion distribution and gas station density. Thus, traffic congestion distribution and gas station density data from Google Maps can be used as an indicator of traffic-related air pollution, especially land transportation. Furthermore, this research is expected to serve as a basis for the government in determining mitigation strategies related to traffic congestion and the resulting emissions.
Network load balancing and data categorization in cloud computing Komathi, Arunachalam; Kishore, Somala Rama; Velmurugan, Athiyoor Kannan; Pavithra, Maddipetlolu Rajendran; Selvaraj, Yoganand; Begum, Akbar Sumaiya; Muthukumaran, Dhakshnamoorthy
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1942-1951

Abstract

Cloud computing (CC) is rising quickly as a successful model presenting an on-demand structure. In the CC, the present investigation shows that load-balancing methods established on meta-heuristics offer better solutions for appropriate scheduling and allotment of resources. Conversely, several traditional approaches believe in only some quality of service (QoS) metrics and reject several significant components. Network load balancing and data categorization (NBDC) is proposed. This approach aims to enhance load balancing in the cloud field. This approach consists of two phases: the support vector machine (SVM) algorithm-based data categorization and the ant colony optimization (ACO) algorithm for distributing the network load on the virtual machine (VM). The SVM algorithm performs several data formats, such as text, image, audio, and video, resultant data class that offers high categorization accuracy in the cloud. The ACO algorithm reaches an efficient load balancing based on the time of execution (TE), time of throughput (TT), time of overhead (TO), time of optimization, and migration count (MC). Simulation results related to the baseline approach demonstrate an enhanced system function in terms of service level agreement violation, throughput, execution time, energy utilization, and execution time.
Improvement of horizontal streak on disparity map thru parameter optimization for stereo vision algorithm Yeou Wei, Melvin Gan; Hamzah, Rostam Affendi; Nik Anwar, Nik Syahrim; Herman, Adi Irwan; Jamil Alsayaydeh, Jamil Abedalrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1886-1894

Abstract

In this paper, an improved local based stereo vision disparity map (SVDM) algorithm is proposed. The proposed local based SVDM algorithm include four stages and they are matching cost computation, cost aggregation disparity optimization and disparity refinement. The matching cost computation started by combining pixel to pixel matching techniques, which are absolute difference (AD) and gradient matching (GM) in producing the initial disparity map. Next, the cost aggregation uses minimum spanning tree (MST) segmentation, which equipped with edge preserving properties and noise filtering. Then, disparity optimization uses local approach with winner-take-all (WTA) technique. At the final stage, disparity refinement uses bilateral filter (BF) with weighted median (WM), which can improve the disparity map through noise removing and edges preserving. Then, the research continues to optimize the proposed local based SVDM algorithm through parameters optimization in obtaining the final disparity map. Here, multiple parameters from the proposed SVDM algorithm are manipulated and they are constant values for GM and several constant parameters in BF. By selecting the optimum parameter values, the performance of the proposed SVDM algorithm increased, especially robustness towards the horizontal streaks.
Optimizing blockchain for healthcare IoT: a practical guide to navigating scalability, privacy, and efficiency trade-offs Alhija, Mwaffaq Abu; Al-Baik, Osama; Hussein, Abdelrahman; Abdeljaber, Hikmat
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1773-1785

Abstract

The adoption of blockchain technology provides significant disruptive benefits to internet-of-things (IoT) applications in healthcare in vital aspects like security, integrity, transparency, and efficiency. Nevertheless, in order to fully realize the potential of blockchain-driven solutions, healthcare organizations have to address intricate compromises between essential factors including scalability, privacy and resource utilization considering that the data sensitivity alongside strict regulatory compliance requirements characterize this sector. This research discusses the fundamental aspects of these trade-offs, including the range of consensus protocols (e.g. proof-of-work, proof-of-stake) and cryptographic techniques (e.g. zero-knowledge proofs, homomorphic encryption). A systematic choice matrix is created, which relates specific use cases of the healthcare IoT to the optimal tailored blockchain structures on such critical metrics as transaction volume, frequency, privacy level and resource restrictions. The suggested framework provides solid, actionable recommendations to healthcare organizations in order to help them benefit from the enormous promise of the blockchain for connected IoT healthcare by finding a balance between decentralization advantages and performance, security and compliance requirements.
Deep transfer learning classification of apple fruit diseases Loutfy, Shaimaa Kamal; Rahouma, Kamel Hussein
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1556-1564

Abstract

This paper applies deep convolution neural networks (DCNN) to apple fruit disease classification. Twelve DCNN methods (SqueezeNet, GoogleNet, InceptionV3, DenseNet201, ReaNet50, ResNet101, Xception, InceptionResnetV2, EfficientnetB0, AlexNet, VGG16, and VGG19) have been used. These methods have been trained to classify apples into four categories: normal, blotch, rot, and scab. A dataset of 5179 images, including 3472 for normal, 171 for blotch, 1166 for rot, and 370 for scab, has been used. A practical test on 120 images (30 for each category) has been applied. Seven of these DCNNs—InceptionV3, DenseNet201, ResNet101, ResNet50, GoogleNet, AlexNet, and VGG16—have the best accuracy. InceptionV3 is the highest. It has achieved an accuracy of 100% for all categories. The used dataset is unbalanced and small. So, it's necessary to use data augmentation to overcome any overfitting that may cause. After applying data augmentation, the dataset is balanced and contains 13888 images (3472 for each category). The seven DCNNs are retrained by the balanced dataset and retested by the same 120 images. All DCNN's accuracy has enhanced except InceptionV3, which has decreased. On the other hand, RasNet101 has achieved an accuracy of 100% for all categories. Therefore, ResNet101 has been recommended for apple fruit disease classification.
A particle swarm optimization inspired global and local stability driven predictive load balancing strategy Dey, Niladri Sekhar; Raju Sangaraju, Hrushi Kesava
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1688-1701

Abstract

In distributed systems and parallel computing, optimal load balancing is difficult. These abstract addresses load balancing in distributed situations, highlighting current solutions' flaws and emphasizing the need for new ones. Load balancing research includes centralized and distributed algorithms, heuristics, and predictive models. Despite various successful methods, workload adaptability, overhead reduction, and scaling to large systems remain unresolved. This study proposes a particle swarm optimization (PSO) load balancing method that considers global and local stability considerations. The proposed method uses PSO principles to balance exploration and exploitation and allocate resources among distributed nodes. Predictive components improve preventative load management by predicting workload changes. Global and local load balancing stability criteria distinguish this study. The recommended method considers global system-wide performance indicators, local node-level characteristics, and micro-level stability to maximize system efficiency. A dual-focus technique distinguishes the proposed load balancing strategy from others, solving dynamic distributed system challenges. The study examines load balancing system advances and suggests improvements and further research. More accurate prediction modeling, stability measures, and application-specific enhancements may be studied in the future. Experimental validation and real-world implementation of the recommended approach are necessary to determine its practicality and ability to handle modern distributed computing systems.

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