2 10 26 https://repository.horizon.ac.id/files/original/df02204d9ead76150dfcb8de3d887b04.pdf 1559f897b172600951f202543785e18c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 23, NO.3 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource XGBoost optimization using hybrid Bayesian optimization and nested cross validation for calorie prediction Subject The topic of the resource Bayesian optimization Calorie prediction Extreme gradient boosting Nested cross validation Wearable devices data Description An account of the resource Accurately predicting calorie expenditure is crucial for wearable device applications, enabling personalized fitness and health recommendations. However, traditional models struggle with high data variability and nonlinear relationships in activity data, leading to suboptimal predictions. This study addresses these challenges by integrating extreme gradient boosting (XGBoost) with Bayesian optimization and nested cross validation to enhance predictive accuracy. Unlike previous approaches, our method systematically tunes hyperparameters using Bayesian optimization while employing nested cross validation to prevent overfitting, ensuring robust model evaluation. We utilize a dataset of daily activity records, including steps, distance, and active minutes, extracted from wearable devices. Our experimental findings indicate a substantial enhancement in prediction performance, achieving a mean squared error (MSE) of 4294.27, an R-squared (R2) score of 0.9917, and a root mean squared error (RMSE) of 65.53. The proposed model outperforms baseline approaches such as random forest and support vector machines in terms of predictive accuracy. These findings underscore the advantage of our approach in predictive modeling. Beyond calorie estimation, the proposed methodology is adaptable to other domains requiring high-precision predictions, such as healthcare analytics and personalized recommendation systems. Creator An entity primarily responsible for making the resource Budiman1, Nur Alamsyah2, Titan Parama Yoga2, R. Yadi Rakhman Alamsyah2, Elia Setiana1 Source A related resource from which the described resource is derived Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Mar 23, 2025 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Bayesian optimization Calorie prediction Extreme gradient boosting Nested cross validation Wearable devices data https://repository.horizon.ac.id/files/original/72e6e4a49cddf55c1cc4cfa62f516930.pdf 8367b10818ad8ebded58cf9ba5a50677 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 23, NO.3 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A multiband sub-6 THz patch antenna with high gain for IoT and 6G communication Subject The topic of the resource 6G communication Industrial and innovation Microstrip patch antenna Multiple-input multiple-output antenna Resistor, inductor, and capacitor Sub-6 Description An account of the resource This comprehensive study introduces a meticulously designed and characterized terahertz (THz) multiple-input multiple-output (MIMO) antenna engineered to operate within the 0.4 THz to 1.6 THz frequency range. The antenna’s construction includes a copper patch and ground plane integrated into a polyimide substrate, ensuring exceptional durability and robust performance. Significantly, the antenna reveals four distinct resonance frequencies at 0.46 THz, 0.9 THz, 1.31 THz, and 1.44 THz each accompanied by bandwidths of 0.005 THz, 0.17 THz, and 0.34 THz, respectively. Moreover, the antenna delivers notable gains of 8.52 dB, 11.54 dB, and 13.25 dB at these frequencies, coupled with substantial efficiencies of 88.32%, 92.02%, and 89.89%, respectively. Additionally, the antenna showcases exceptional isolation of 26 dB, a low envelope correlation coefficient (ECC) of 0.003, and a diversity gain (DG) of 9.98. These remarkable attributes underscore the antenna’s aptness for high-performance THz applications, offering substantial advantages in terms of gain, efficiency, and isolation for next-generation wireless communication systems. Creator An entity primarily responsible for making the resource Redwan Al Mahmud Bin Asad Ananta1, Md. Sharif Ahammed1, Md. Ashraful Haque1, Narinderjit Singh Sawaran Singh2, Kamal Hossain Nahin1, Jamal Hossain Nirob1, Md. Kawsar Ahmed1, Liton Chandra Paul3 Source A related resource from which the described resource is derived Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Mar 11, 2025 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/112c5c5a614ad7bb366a02b80e6e8e2f.pdf 6914e45efb8dce9a691637e1fb5fc20d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 23, NO.3 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A 6G THz MIMO antenna with high gain and wide bandwidth for high-speed wireless communication Subject The topic of the resource 6G communication Graphene High-gain Industrial and innovation Resistor inductor capacitor Terahertz antenna Wide-bandwidth Description An account of the resource This study presents a comprehensive industrial and innovation design and thorough analysis of a terahertz (THz) multiple-input multiple-output (MIMO) antenna, addressing the increasing demand for high-performance multi-antenna systems in THz communication applications. The primary objective of this research is to develop a compact and efficient MIMO antenna that operates over a wide frequency range and provides high isolation, specifically within the 1–10 THz spectrum. The proposed antenna achieves an impressive total bandwidth of approximately 9 THz, featuring seven distinct resonance frequencies at 1.39 THz, 3.26 THz, 4.72 THz, 5.96 THz, 7.07 THz, 8.194 THz, and 9.426 THz. The design employs a polyimide substrate and a graphene patch. Key performance metrics include a maximum gain of 15 dB, efficiency of 99.8%, and isolation values that range from 28 dB to 63 dB. An resistor inductor capacitor (RLC) equivalent circuit using advanced design system (ADS) software. Additionally, the antenna displays remarkable diversity metrics, with an envelope correlation coefficient (ECC) of 0.000778 and a diversity gain of 9.99961 dB. With compact dimensions of (65×180) μm2 and outstanding performance characteristics, this design is confirmed to be suitable for THz applications, fulfilling the research goal of facilitating efficient and reliable communication in sophisticated multi-antenna systems. Creator An entity primarily responsible for making the resource Redwan Al Mahmud Bin Asad Ananta1, Md. Sharif Ahammed1, Md. Ashraful Haque1, Md. Kawsar Ahmed1, Narinderjit Singh Sawaran Singh2, Jamal Hossain Nirob1, Kamal Hossain Nahin1, Liton Chandra Paul3 Source A related resource from which the described resource is derived Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Mar 11, 2025 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT 6G communication Graphene High-gain Industrial and innovation Resistor inductor capacitor Terahertz antenna Wide-bandwidth https://repository.horizon.ac.id/files/original/95d39fc0cd11050906158a8fe9430003.pdf 01d9c391a2c0f4c9de789e6ace1121c5 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 23, NO.3 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Earthquake magnitude prediction based on radon cloud data near Grindulu fault, Indonesia using the statistical method Subject The topic of the resource Earthquake magnitude Mitigation Prediction Radon Statistic Description An account of the resource Earthquake 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. Creator An entity primarily responsible for making the resource Sunarno1, Thomas Oka Pratama1, Faridah1, Nugroho Ananto2, Hermin Kartika Sari3, Rony Wijaya1,4, Memory Motivanisman Waruwu1,4 Source A related resource from which the described resource is derived Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Mar 23, 2025 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Earthquake magnitude Mitigation Prediction Radon Statistic https://repository.horizon.ac.id/files/original/239a8a9656f4b807c88559f520c40a35.pdf 103031664fe50c6f85a691232000b7a1 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 23, NO.3 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Adulterated beef detection with redundant gas sensor using optimized convolutional neural network Subject The topic of the resource Adulterated beef Convolutional neural network Machine learning Pork adulteration Redundant gas sensor Description An account of the resource Various types of research have been developed to detect beef adulteration, but the accuracy and reliability of these results still require improvement. This study proposes designing a highly precise redundant electronic nose system using an optimized convolutional neural network (CNN) method to detect adulterated beef mixed with pork. As baselines, other classifiers are also utilized, namely the decision tree (DT), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM). Several data preprocessing methods are employed to increase prediction accuracy, namely feature selection, principal component analysis (PCA), and time series smoothing. The weight of each data sample was 100 g with 15 classes of pork and beef mixing ratios of 0%, 0.1%, 0.5%, 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% pork. With the single-layer sensor configuration, the average CNN classification success rates were 97.15%, 96.29%, and 99.64% for layers 1, 2, and 3, respectively. In addition, from the combination of the three layers, a prediction results of 99.72% was obtained. Thus, a redundant gas sensor array configuration can improve the classification results. In addition, the relatively high accuracy of the optimized CNN provides a convincing alternative for identifying possible beef adulteration. Creator An entity primarily responsible for making the resource Ardani Cesario Zuhri1, Agus Widodo1, Mario Ardhany1, Danny Mokhammad Gandana1, Galang Ilman Islami1, Galuh Prihantoro2 Source A related resource from which the described resource is derived Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Mar 11, 2025 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Adulterated beef Convolutional neural network Machine learning Pork adulteration Redundant gas sensor https://repository.horizon.ac.id/files/original/aaa677f68ca298b3a1f183f05c4fc152.pdf 61bb198acec04d535fc17bc244e228ef Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 23, NO.3 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Addressing overfitting in comparative study for deep learning-based classification Subject The topic of the resource Deep learning Feature extraction Few-shot learning InceptionV3 Overfitting Stanford dog Xception Description An account of the resource Despite 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. Creator An entity primarily responsible for making the resource Jing-Yee Ong, Lee-Yeng Ong, Meng-Chew Leow Source A related resource from which the described resource is derived Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Mar 11, 2025 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Deep learning Feature extraction Few-shot learning InceptionV3 Overfitting Stanford dog Xception https://repository.horizon.ac.id/files/original/b42a696850fe5459a5c9ae8279c312a0.pdf b38c309350ee374b57c90224ec449576 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 23, NO.3 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Website-based: smart goat farm monitoring cages Subject The topic of the resource Cloud storage Goat farming Internet of things Livestock monitoring Smart farm system Description An account of the resource Goat farming holds significant profit potential due to high community demand for goat meat and its role in supporting national food security. To optimize its development, proper and efficient farm management is essential. This study aims to design a system for monitoring and improving goat farming by observing key environmental and animal health conditions, such as feed availability, temperature, humidity, and overall livestock health. The proposed system utilizes internet of things (IoT) technology and cloud storage to create a smart farm environment. Various IoT devices, including cameras, thermal sensors, and lighting equipment, are integrated and connected via Wi-Fi. These devices collect real-time data, which is then processed into informative analytics to monitor and support farm development. Through the use of IoT and cloud-based solutions, this system is expected to enable real-time supervision and create ideal, controlled conditions for goat farming, ultimately benefiting farmers. Creator An entity primarily responsible for making the resource Dwinanda Hafid Wicaksana, Wella Source A related resource from which the described resource is derived Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Mar 11, 2025 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Cloud storage Goat farming Internet of things Livestock monitoring Smart farm system https://repository.horizon.ac.id/files/original/da6595a2b6b35c7972800bb0946725cf.pdf e7fe3fed6e98f87676375deb1624964a Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 23, NO.3 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Integrating artificial intelligence into accounting systems: a qualitative study on user experiences and challenges Subject The topic of the resource Algorithm transparency Artificial intelligence Challenges in accounting systems Job role transitions User experiences Description An account of the resource This research explores the integration of artificial intelligence (AI) in accounting systems, focusing on user experiences and challenges faced by accountants and financial professionals. Using qualitative methods, in-depth interviews with diverse accounting professionals reveal key themes: optimism mixed with skepticism about AI’s potential, concerns over algorithm transparency, and trust issues due to the “black box” nature of AI systems. Participants highlight inadequate training programs, which hinder effective AI use and fuel resistance to adoption. The study also discusses the impact of AI on job roles, emphasizing a shift towards strategic thinking and advisory functions while routine tasks are automated. Implementation challenges include system compatibility, data integration issues, and significant resource investments, compounded by organizational resistance and lack of executive support. The findings stress the need for transparent AI algorithms, comprehensive training programs, and managed job role transitions to maximize AI benefits. This research provides insights into real-world user experiences, offering a roadmap for organizations to support effective AI integration in accounting, leading to improved performance, job satisfaction, and acceptance of AI technologies. Creator An entity primarily responsible for making the resource Andhika1, Lawrence Adi Supriyono2 Source A related resource from which the described resource is derived Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Mar 12, 2025 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Algorithm transparency Artificial intelligence Challenges in accounting systems Job role transitions User experiences https://repository.horizon.ac.id/files/original/c94058e7a28c7604c0c1a5e3d2350d7f.pdf 70f6d938a12de69c0ac138d6d6aa0d04 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 23, NO.3 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Design and simulation of rectangular patch antenna arrays with high bandwidth for 2.4 GHz ISM band applications Subject The topic of the resource Ansys high frequency structure simulator Impedance matching Microstrip patch antenna array Quarter-wave transformer Wide bandwidth Description An account of the resource Ongoing advancements in microstrip patch antenna (MPA) development research is driven by its compact size, cost-effectiveness and ease of fabrication. This paper presents a flexible design of patch antenna array (PAA) to address bandwidth (BW) limitations within the 2.4 GHz industrial, scientific, and medical (ISM) band, where narrow BW is a common challenge. To explore the effectiveness of different array configurations, we designed and evaluated 1×2, 2×2, and 2×4 element rectangular PAA, employing a quarter-wave transformer (QWT) method and parallel scheme for connecting patches. By utilizing Ansys high frequency structure simulator (HFSS) as the modeling environment, we conducted extensive simulations to refine the antenna parameters and achieve the most optimal MPA prototype. Our investigation demonstrates sufficiently good results, including BWs of 290 MHz and 210 MHz for 2×2 and 2×4 PAAs respectively, which account for 8.75% and 12% of the total value. The parameter return loss (RL) (S_11) reached -51dB for single-element patch antenna (SPA) and -37.5 dB for 8-element PAA, that shows an ideal impedance matching. In addition, the designed 2×4 PAA exhibited impressive performance metrics, accounting for 9.17 dB in gain, 13 dBi in directivity, and voltage standing wave ratio (VSWR) maintained below 0.5, ensuring excellent signal transmission and reception. Creator An entity primarily responsible for making the resource Kymbat Kopbay, Madiyar Nurgaliyev, Ahmet Saymbetov, Nurzhigit Kuttybay, Askhat Bolatbek, Sayat Orynbassar, Batyrbek Zholamanov Source A related resource from which the described resource is derived Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Mar 11, 2025 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Ansys high frequency structure simulator Impedance matching Microstrip patch antenna array Quarter-wave transformer Wide bandwidth https://repository.horizon.ac.id/files/original/5d1774f447e0bffd5ad6f26ecb4bf7f2.pdf c771ae4857bec45668a2bcbeac3f3dfc Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 23, NO.3 2025 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Reversible data hiding with selective bits difference expansion and modulus function Subject The topic of the resource Cryptography Difference expansion Information security Reversible data hiding Steganography Description An account of the resource The integration of the internet of things (IoT) has significantly enhanced human life but also raises concerns about information security and privacy. Information security can be achieved through cryptography, which encrypts data to make it unreadable, or steganography, which hides data within other media. For sensitive media, such as military, medical, and forensic imaging, specialized techniques like reversible data hiding (RDH) are necessary to ensure the media can be fully restored after data extraction. Many researchers have proposed improvements to the RDH method in recent years. In this study, we propose an improved RDH method utilizing difference expansion and a modulus function. The method embeds data into the 4-bit, 3-bit, and 2-bit least significant bits (LSB) of the difference value of pixels, with a range of -2 and 2. The experimental findings demonstrate that our approach achieves a embedding capacity of 0.2507 bpp with 55.445 dB of peak signal-to-noise ratio (PSNR) for common images and 0.3849 bpp with 54.6810 dB of PSNR for medical images, using 2-bit difference values. The results demonstrate that our approach surpasses previous methods and holds promise for practical applications in IoT systems and the medical field, where secure and reversible data embedding is essential Creator An entity primarily responsible for making the resource Aulia Arham1,2, Hanung Adi Nugroho2, Domi Sepri1 Source A related resource from which the described resource is derived Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Mar 11, 2025 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Cryptography Difference expansion Information security Reversible data hiding Steganography