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                  <text>VOL. 23, NO.2 2025</text>
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                <text>A wideband microstrip antenna employing ring and hexadecagonal slots with parasitic elements for W-band applications</text>
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                <text>Hexadecagonal slot&#13;
Millimeter-wave&#13;
Parasitic elements&#13;
Point-to-point communications&#13;
Ring slot&#13;
Slotted patch&#13;
W-band</text>
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                <text>This article presents a monopole patch antenna, designed for operation in the W-band. The antenna is constructed on Rogers/RT 5880 dielectric material with dimensions of 3.4×4×0.16 mm³, a loss tangent of 0.0009, and a relative permittivity of 2.2. The initial design features a simple rectangular patch measuring 1.3542×1.0306 mm², powered by a microstrip line using an inset feed. To enhance the bandwidth and gain, two parasitic rectangular elements were added on both sides of the patch in addition to the incorporation of a circular ring slot with the ground plane. Further improvements in bandwidth and return loss were achieved by etching a hexadecagonal-shaped slot on the patch. Simulation results indicate that the optimized design achieves an impedance bandwidth of 28.54 GHz, ranging from 79.67 GHz to 108.21 GHz, centered at 88 GHz. The antenna also shows a maximum return loss of 59 dB and a voltage standing wave ratio (VSWR) of 1.0022. The radiation pattern is directional, with a peak gain of approximately 8.57 dBi, and a maximum directivity of about 8.6 dB, as predicted by the computer simulation technology (CST) frequency-domain solver. These advantageous characteristics make the proposed antenna a suitable choice for point-to-point transmission applications.</text>
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                <text>AbdulGuddoos S. A. Gaid1, Mohammed M. S. Qaid1, Mohammed A. E. Abdullah1,&#13;
Mohammad Ahmed Alomari2</text>
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                <text>Journal homepage: http://telkomnika.uad.ac.id</text>
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                <text>Three-position gearshifts remote control for agricultural tractors</text>
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                <text>Agricultural tractors&#13;
Direct current linear actuator&#13;
Gearshifts&#13;
Programmable logic controller&#13;
Radio remote control</text>
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                <text>This research presents the development and evaluation of a three-position gearshifts remote control system for agricultural tractors, designed to improve operational efficiency and reduce operator fatigue. The system utilizes a programmable logic controller to remotely control a linear actuator, enabling seamless gear shifting between three predetermined positions. The primary objective is to provide operators with a convenient, ergonomic alternative to traditional manual gear shifting, particularly in challenging or confined working environments. The system was tested under two conditions: first, with a programmable logic controller controlling the linear actuator via a remote transmitter; second, with the system installed on an actual tractor and tested in a road scenario. Results from both tests demonstrate the system’s effectiveness in enhancing ease of operation, reducing physical strain, and maintaining gearshifts precision. The findings suggest that the remote control system offers significant potential for improving tractor operation, particularly for tasks requiring frequent gear changes or when working in difficult terrain. This research contributes to the ongoing development of automation in agricultural machinery, offering insights into remote control applications and the integration of electromechanical systems in agricultural vehicles.</text>
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                <text>Thewin Sakunbunyong1, Tossapol Jangnoi2, Tanawat Chalardsakul1, Viroch Sukontanakarn1</text>
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                <text>Journal homepage: http://telkomnika.uad.ac.id</text>
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        <name>Agricultural tractors Direct current linear actuator Gearshifts Programmable logic controller Radio remote control</name>
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                <text>Advanced crop yield prediction using machine learning and deep learning: a comprehensive review</text>
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                <text>Crop yield prediction&#13;
Deep learning&#13;
Machine learning&#13;
Remote sensing&#13;
Systematic literature review&#13;
Vegetation indices</text>
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                <text>The advancement of machine learning (ML) and deep learning (DL) techniques has significantly improved crop yield prediction, making it more accurate and reliable. In this review, the implementation of ML and DL algorithms for crop yield prediction is thoroughly investigated, focusing on their crucial role in enhancing crop productivity. Along with ML and DL algorithms examine, the review analyses the use of remote sensing technologies, such as satellite and drone data, in providing high-resolution inputs essential for accurate yield predictions. The study identifies the state of art algorithms, most used features, data sources and evaluation metrics, providing a comparison of ML and DL. The findings indicate that DL models are more effective with large datasets, while ML models remain robust for smaller datasets. The future directions are proposed to develop the generalised models for different crops and regions. The review aims to assist researchers by summarising state of art techniques and identifying the present.</text>
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                <text>Ayush Anand, Kavita Jhajharia</text>
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                <text>Journal homepage: http://telkomnika.uad.ac.id</text>
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                <text>Enhancing spam detection using Harris Hawks optimization algorithm</text>
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                <text>Feature selection&#13;
Harris Hawks algorithm&#13;
ISCX-URL2016 dataset&#13;
Machine learning&#13;
Spam</text>
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                <text>This paper employs machine learning (ML) algorithms to identify and classify spam emails. The Harris Hawks optimization (HHO) algorithm can detect the crucial features that distinguish spam from ham emails. The HHO algorithm decreased the number of features in the ISCX-URL2016 spam dataset from 72 to 10. Implementing this will enhance the efficiency and cognitive acquisition of the ML algorithms. The decision tree (DT), Naive Bayes (NB), and AdaBoost algorithms are evaluated and contrasted to identify spam emails. The random search algorithm is used to optimize the significant hyperparameters of each algorithm for the specific task of spam identification. All three ML algorithms showed exceptional accuracy in detecting spam emails during the conducted testing. The DT algorithm attained a remarkable accuracy rate of 99.75%. The AdaBoost algorithm ranks second with an incredible accuracy of 99.67%. Finally, the NB algorithm attained an accuracy of 96.30%. The results demonstrate that the HHO algorithm shows promise in recognizing the crucial features of spam emails.</text>
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                <text>Mosleh M. Abualhaj1, Sumaya Nabil Alkhatib1, Ahmad Adel Abu-Shareha1, Adeeb M. Alsaaidah1, Mohammed Anbar2</text>
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                <text>Journal homepage: http://telkomnika.uad.ac.id</text>
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        <name>Feature selection Harris Hawks algorithm ISCX-URL2016 dataset Machine learning Spam</name>
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                  <text>VOL. 23, NO.2 2025</text>
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                <text>Adversarial-robust steganalysis system leveraging adversarial training and EfficientNet</text>
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                <text>Adversarial attacks&#13;
Deep learning&#13;
EfficientNet&#13;
Steganalysis&#13;
Steganography</text>
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                <text>Steganalysis aims to detect hidden messages within digital media, presenting a significant challenge in the field of information security. This paper introduces an adversarial-robust steganalysis system leveraging adversarial training and the powerful feature extraction capabilities of EfficientNet. We utilize EfficientNet to extract robust features from images, which are subsequently classified by a dense neural network to distinguish between steganographic and non-steganographic content. To enhance the system’s resilience against adversarial attacks, we implement a custom adversarial training loop that generates adversarial examples using the fast gradient sign method (FGSM) and integrates these examples into the training process. Our results demonstrate that the proposed system not only achieves high accuracy in detecting steganographic content but also maintains robustness against adversarial perturbations. This dual approach of leveraging state-of-the-art deep learning architectures and adversarial training provides a significant advancement in the field of steganalysis, ensuring more reliable detection of hidden messages in digital images.</text>
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                <text>In this study, we present the design and investigation of a terahertz (THz) frequency antenna optimized for the 2-10 THz range, featuring both single-element and multiple-input multiple-output (MIMO) configurations, with a focus on industrial and innovative applications to enhance future 6G communication systems. The antenna, constructed on a polyimide substrate with dimensions of 90×30 μm, achieves a bandwidth from 4.0328 to 10 THz. The MIMO configuration, which includes two ports, demonstrates excellent isolation with a value of -27 dB. The proposed antenna system achieves a gain of 12.38 dB and an efficiency of 89%, making it highly appropriate for THz communication applications. Furthermore, the envelope correlation coefficient (ECC) of 0.002 and diversity gain (DG) of 9.99 affirm the antenna’s effectiveness in MIMO systems. A resistance inductance capacitance (RLC) circuit model was employed to accurately represent the S11 curve, ensuring precise characterization of the antenna’s performance. These results underscore the probability of the proposed antenna for high-speed, short-range communication systems.</text>
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                  <text>VOL. 23, NO.2 2025</text>
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                <text>Oversampling vs. undersampling in TF-IDF variations for imbalanced Indonesian short texts classification</text>
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                <text>Bahasa Indonesia&#13;
Imbalanced dataset&#13;
Oversampling method&#13;
Short-text classification&#13;
Term frequency inversed document frequency Undersampling method</text>
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                <text>Even though it is considered a more traditional method compared to more modern algorithms, term frequency inversed document frequency (TF-IDF) nevertheless produces good results in a range of text mining tasks. This study assesses the effectiveness of several TF-IDF modifications for short text classification. Imbalanced datasets are another issue that is addressed in this research. To rectify the imbalanced issue, we integrate standard, log-scaled, and boolean TF-IDF in short text classification with undersampling and oversampling methods. Precision, recall, and f-measure metrics are used to evaluate each experiment. The best result is obtained when applying boolean TF-IDF with the oversampling method. Oversampling methods outperform the undersampling methods in every experiment, although there are some cases where experiments with undersampling methods are considerable. Additionally, our conducted study reveals that employing modified TF-IDF, such as boolean or log-scaled versions, provides greater advantages to classification performance, particularly in handling imbalanced datasets, when compared to solely relying on the standard TF-IDF approach.</text>
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                <text>I Nyoman Prayana Trisna, Ni Wayan Emmy Rosiana Dewi, Muhammad Alam Pasirulloh</text>
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                <text>Journal homepage: http://telkomnika.uad.ac.id</text>
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                <text>Dec 26, 2024</text>
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                <text>PERI IRAWAN</text>
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        <name>Bahasa Indonesia Imbalanced dataset Oversampling method Short-text classification Term frequency inversed document frequency Undersampling method</name>
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