TELKOMNIKA (Telecommunication Computing Electronics and Control)
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Enhancing terahertz patch antenna performance with metamaterials for biomedical applications
Full text linkThis paper presents the performance enhancement of a terahertz (THz) patch antenna using the metamaterials (MTM). The antenna design features a rectangular patch with a modified ground structure, implemented on an FR4 substrate with dielectric properties of 4.4, a tan (δ) of 0.02, and a thickness of 1.6 µm. Operating at 4.92 THz, the antenna exhibits a -10 dB bandwidth of 0.25 THz (250 GHz), catering to diverse biomedical applications. To investigate the impact of incorporating MTM, the proposed MTM is positioned beneath the antenna at a separation of 12.8 µm. A comparative analysis of the antenna’s performance with and without MTM reveals a significant influence of MTM insertion. However, the results confirm the big influence of the addition of the MTM. As results, the return loss was improved from -23.16 dB to -44.73 dB. The gain was additionally elevated from 1.46 dB to 5.06 dB. The design and simulation of the antenna were carried out through high frequency structure simulator (HFSS) software
K-Means clustering interpretation using recency, frequency, and monetary factor for retail customers segmentation
Full text linkEfforts to retain customers represent a crucial customer relationship management (CRM) strategy in every business, offering the potential to enhance profits, particularly for small and medium enterprises (SMEs). In the context of this study, which focuses on the transaction dataset of retailers in a developing market, Indonesia, the emphasis has predominantly been on customer attraction rather than the implementation of customer retention strategies. The primary objective of this research was to scrutinize customer transaction data within the dataset. The K-Means clustering (KMC) method, integrated with recency, frequency, and monetary (RFM) attributes, was employed to classify customers and formulate effective strategies for customer retention. Conducted through a descriptive research method with a quantitative approach, the study involved sequential stages of data preprocessing and RFM analysis for comprehensive data analysis. The outcomes revealed the identification of 5 distinct clusters with associated strategies based on the RFM scores obtained. These strategies, tailored to each cluster, serve as valuable insights in industrial and innovation for marketing and business strategic teams, offering practical approaches to customer retention that can lead to increased benefits for SMEs
Addressing overfitting in comparative study for deep learning-based classification
Full text linkDespite significant advancements in deep learning methodologies for animal species classification, there remains a notable research gap in effectively addressing biases inherent in training datasets, combating overfitting during model training, and enhancing overall performance to ensure reliable and accurate classification results in real-world applications. Therefore, this study explores the complex challenges of dog species classification, with a specific focus on addressing biases, combatting overfitting, and enhancing overall performance using deep learning methodologies. Initially, the Stanford Dog dataset serves as the foundation for training, complemented by additional data from annotated datasets. The primary aim is to mitigate biases and reduce overfitting, which is essential for improving the performance of deep learning-based classification in terms of dataset size and computational time. Feature extraction and few-shot learning techniques are compared to assess and improve the model performance. The experimentation involves the utilization of optimal classifiers, specifically InceptionV3 and Xception. In order to tackle overfitting, a range of strategies are deployed, including data augmentation, early stopping, and the integration of dropout and freezing layers which particularly achieved a better performance with Xception on the augmented dataset
Deep learning approaches for accurate wood species recognition
Full text linkWood species identification is a crucial task in various industries, including forestry, woodworking, and conservation. Traditional methods rely on manual expertise, which can be time-consuming and error prone. Hence, an automatic wood species recognition system is developed in this study using deep learning (DL) models. In this study, three deep convolutional neural network (CNN) architectures, SqueezeNet, GoogLeNet, and ResNet-50 was tailored for wood species classification. The accuracy of the DL models was evaluated in recognizing fifty different wood species. Additionally, the wood species images were altered using JPEG Compression, Gaussian Blur, Salt and Pepper, and Speckle noises to assess the models' performance in identifying the wood species from the distorted images. Results show that the ResNET-50 based wood recognition system is the most accurate model to recognise the wood species. The implications of this research extend to forestry management, quality control in woodworking industries, and the preservation of endangered wood species in conservation efforts
Earthquake magnitude prediction based on radon cloud data near Grindulu fault, Indonesia using the statistical method
Full text linkEarthquake prediction is one of the most challenging and vital tasks that demands new methodologies for improving the accuracy of predictions. The research aims to present how radon gas concentration fluctuations are associated with the prediction of earthquakes in the Eurasian-Indo-Australian Plates. The paper discusses a statistical method of forecasting earthquake magnitudes greater than M4.5 from real-time radon gas monitoring close to the Grindulu Fault, Pacitan, East Java, Indonesia. This developed model has had the least errors in the form of mean absolute error (MAE), 0.30; mean absolute percentage error (MAPE), 0.06; root mean square error (RMSE), 0.55; mean squared error (MSE), 0.30; symmetric mean absolute percentage error (SMAPE), 0.06; complex normalized mean absolute percentage error (cnMAPE), 0.97; error absolute average (EAA), 0.30; and error relative average (ERA), -0.11, showing great accuracy and uniformity in prediction. These observations support the model’s efficiency that may be adopted in earthquake early warning systems for better disaster preparedness. Predictive errors are reduced, and there is support for improved disaster management strategy, public safety education, and effective emergency response personnel training. This study can be used as a foothold for further advances in earthquake prediction methodologies and refinement of early warning systems
An insight on using deep learning algorithm in diagnosing gastritis
Full text linkChronic autoimmune gastritis (CAG) is a condition in which the stomach membrane is significantly impacted by inflammation. Despite the availability of numerous modern medical techniques, the detection of this condition continues to be a difficult challenge. White light endoscopy (WLE) has been employed to diagnose gastritis, but it has been subject to certain constraints. This technique is most effective when executed by an endoscopist who possesses a high level of expertise. In the present day, WLE is frequently accompanied by artificial intelligence (AI) due to its superior ability to detect defects that lead to damage. Recently, there has been a substantial increase in the efficacy of AI in conjunction with the expertise of endoscopists in the detection of CAG. The 25,216 intriguing case studies were examined in the eight selected studies. The collection comprised 84,678 frames and 10,937 images. The AI was 94% sensitive (95% CI: 0.88-0.97, I2 = 96.2%) and 96% specific (95% CI: 0.88-0.98, I2 = 98.04%). The receiver operating characteristic curve had an area of 0.98 (95% confidence interval: 0.96–0.99). A camera is highly effective when combined with AI to assist in the identification of CAG and is advantageous for clinical review
Mixed attention mechanism on ResNet-DeepLabV3+ for paddy field segmentation
Full text linkRice cultivation monitoring is crucial for Indonesia, where paddy field areas de clined by 2.45% according to the Central Bureau of Statistics due to land func tion changes and shifting crop preferences. Regular monitoring of paddy field distribution is essential for understanding agricultural land utilization by farmers and landowners. Satellite imagery has become increasingly common for agricul tural land observation, but traditional neural networks alone provide insufficient segmentation accuracy. This study proposes an enhanced deep learning architec ture combining residual network (ResNet)-DeepLabV3+ with coordinate atten tion (CA) and spatial group-wise enhancement (SGE) modules. The attention mechanisms establish direct connections between context vectors and inputs, enabling the model to prioritize relevant spatial and spectral features for precise paddy field identification. The CA module enhances spectral feature discrim ination, whereas the SGE improves spatial characteristic representation. The experimental results demonstrate superior performance over the baseline meth ods, achieving intersection over union (IoU) of 0.85, dice coefficient of 0.89, and accuracy of 0.95. The proposed mixed attention mechanism significantly improves the accuracy and efficiency of automatic crop area identification from satellite imagery
Application of artificial intelligence in emission prediction for hybrid electric vehicles: integrating ANN and GPR
Full text linkIn recent years, hybrid electric vehicles (HEVs) have emerged as a promising solution to mitigate vehicular emissions and improve fuel efficiency. This study focuses on the Toyota Prius HEV, employing advanced artificial neural networks (ANN) and Gaussian process regression (GPR) to develop a predictive model for vehicle emissions. The model considers multiple pollutants, including carbon monoxide (CO), carbon dioxide (CO₂), hydrocarbons (HC), and nitrogen oxides (NOx), measured under diverse driving conditions. The ANN model predicts emission trends, while GPR estimates prediction uncertainty, enhancing the model’s robustness. The GPR models achieved uncertainty levels of ±0.829 ppm for CO, ±9.978 ppm for HC, ±0.144 ppm for NOx, and ±411.256 ppm for CO₂, respectively, underscoring the robustness of the integrated approach for emission prediction. This research aims to support the development of more sustainable vehicle technologies and inform policy making for environmental sustainability (e.g., Euro 6/Euro 7 standards). Overall, the study addresses how artificial intelligence (AI) can be utilized to achieve accurate multi-pollutant emission predictions in HEVs. The findings reveal that an integrated ANN-GPR approach yields superior predictive performance (R² values approaching 1.0) with quantifiable uncertainty, outperforming a stand-alone ANN model and providing a robust solution to the emission prediction challenge
A design and reconfigurable phase shift inductor inductor capacitor converter for switch failures
Full text linkThe reliability of a converter operation strongly affects overall system performance and is vital for uninterrupted power-electronic operation. Harsh operating conditions and environmental stresses degrade device performance and reduce reliability. In particular, a switching device failure may prevent an inductor inductor capacitor (LLC) resonant converter from operating near its resonant frequency while still maintaining stable output voltage, potentially causing loss of operation as well as significant drops in both efficiency and power delivery. To address this challenge, this paper proposes a fault-tolerant topology and control strategy for the LLC converter under open circuit switch (OCF) faults. The proposed method integrates a bypass arm with a secondary-side series configuration; when a primary-side open-circuit fault occurs, the auxiliary switch is activated to bypass the faulty leg, reconfiguring the secondary side into a voltage doubler rectifier (VDR). This reconfiguration enables continuous operation with an output voltage doubled relative to the normal condition, while minimizing performance degradation. Simulation results confirm that, even under a single-switch OCF, the proposed approach maintains an efficiency of 98% with output voltage fluctuation limited to less than 1%. Compared to conventional methods, the proposed strategy greatly enhances reliability and fault tolerance, making it well-suited for high-efficiency power conversion applications
Lightning studies on effects on distribution lines: a bibliometric analysis
Full text linkThe study of lightning effects on distribution lines is of vital importance for the reliability and safety of electrical systems, as lightning is one of the main causes of failures. The purpose of this study is to perform a bibliometric analysis to evaluate academic productivity trends and research trajectories in this field. The methodology was based on a comprehensive search of the Scopus database, from which a total of 545 articles published between 1932 and 2024 were analyzed. For the analysis, the VOSviewer tool and the Bibliometrix library in R were used. The results reveal a constant increase in productivity since the 1970s, with Japan and China emerging as the most prolific countries. The research has evolved from early theoretical and experimental studies toward the use of advanced computational models and, more recently, the application of machine learning techniques for fault detection. In conclusion, the findings of this study provide a consolidated view of the field, which is fundamental for engineers to be able to design more robust protection systems and to guide future research toward model validation and the integration of renewable energy technologies