1 10 27 https://repository.horizon.ac.id/files/original/9d5f3276447e82650987cb684656250b.pdf 73869eeb36a096b96d92da0d394ed478 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. 22, NO.5 2024 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 3D word embedding vector feature extraction and hybrid CNN-LSTM for natural disaster reports identification Subject The topic of the resource Convolutional neural network Feature extraction Long short-term memory Natural disaster Word embedding Description An account of the resource Social media contain various information, such as natural disaster reports. Artificial intelligence is used to identify reports from eyewitnesses early for disaster warning systems. The artificial intelligence system includes a text classification model with feature extraction and classification algorithms. Word embedding-based feature extraction is widely used for 1-dimensional (1D) and 2-dimensional (2D) data, suitable for traditional or deep learning algorithms. However, applying feature extraction to 3-dimensional (3D) data for text classification is limited. Previous studies focused on word embedding for 1D, 2D, and 3D outputs with convolutional neural network (CNN). Yet, using 3D data and CNN did not perform well. Despite using CNN and 3D variants, identifying natural disaster reports remains below 80% accuracy. This research aims to improve identifying earthquakes, floods, and forest fires with 3D data and hybrid CNN long short-term memory (LSTM). The study found models with accuracies of 83.38%, 83.72%, and 89.03% for each disaster type. Hybrid CNN LSTM significantly enhanced identification compared to CNN alone, supported by statistical tests with P value less than 0.0001. Creator An entity primarily responsible for making the resource Mohammad Reza Faisal1, Dodon Turianto Nugrahadi1, Irwan Budiman1, Muliadi1, Mera Kartika Delimayanti2, Septyan Eka Prastya3, Imam Tahyudin4 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Jul 12, 2024 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/05c096aff02a1197b3af5b9a07bf84d4.pdf 64e3094642f7b6db325647c9bfb998c5 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. 22, NO.5 2024 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 contrived dataset of substation automation for cybersecurity research in the smart grid networks based on IEC61850 Subject The topic of the resource CyberGrid Cybersecurity research Datasets Deep learning IEC61850 Smart grid Substation automation system Description An account of the resource Relevant datasets that depict and/or emulate the smart grid networks (SGN) are key to developing cybersecurity models that can effectively provide security for mission-critical infrastructure. The difficulty in obtaining relevant SGN real-life datasets presents a considerable challenge for researchers in the field and, the existing datasets lack representation of the IEC61850 protocol for modelling substation automation processes for cybersecurity solutions. This paper presents a dataset simulated from a fully virtual testbed, intended to provide researchers with the necessary datasets for research and experiments that require massive amounts of data close to the real-world scenario. Experimentally, the dataset was used to develop an intrusion detection model based on gated recurrent unit (GRU), deep belief network (DBN), long-short term memory (LSTM), and evaluated using accuracy, precision, recall, F1-score, detection rate, false alarm rate (FAR), missed alarm rate (MAR), mean squared error (MSE), mean absolute error (MAE), and loss. Results show that the standalone deep learning (DL) algorithms outperformed the hybridized ones and are more suitable for developing models for securing substation automation systems that support generic object-oriented substation events (GOOSE), and manufacturing message specification (MMS) and run on IEC61850, distributed network protocol version 3 (DNP3), and Modbus-transmission control protocol (ModbusTCP). Creator An entity primarily responsible for making the resource John Edet Efiong1, Jide Ebenezer Taiwo Akinsola2, Bodunde Odunola Akinyemi3, Emmanuel Ajayi Olajubu4, Ganiyu Adesola Aderounmu1 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource May 26, 2024 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/7d62edd5e974112be779003c4bbcd6a2.pdf a4b9aea63d5581b3153ad75870811e8f 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. 22, NO.5 2024 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 high accuracy of deep learning based CNN architecture: classic, VGGNet, and RestNet50 for Covid-19 image classification Subject The topic of the resource Classic convolutional neural network Convolutional neural network Covid-19 RestNet-50 VGGNet Description An account of the resource This research paper provides a detailed examination of different convolutional neural network (CNN) structures used in Covid-19 image classification tasks. The study thoroughly investigates the performance of classic CNN, visual geometry group (VGG), and ResNet-50 architectures across a variety of datasets. The analysis focuses on evaluating the efficacy of each architecture by considering metrics such as accuracy, precision, recall, and F1-Score. The experimental results reveal that the ResNet-50 architecture achieves the highest performance with an accuracy rate of 96.63%, outperforming both VGG and classic CNN models. This finding emphasizes the importance of architectural choices and hyperparameter selection in achieving optimal performance in image classification tasks. The combination of the ResNet-50 architecture with the Adam optimizer demonstrates its effectiveness in improving classification accuracy. These findings contribute to the field of deep learning by providing valuable insights into the performance analysis of CNN architectures and highlighting the significance of selecting appropriate hyperparameters for optimal model performance. The selection of VGG and ResNet-50 architectures was based on their strong feature extraction capabilities, proven state-of-the-art performance, and their suitability for transfer learning. VGG and ResNet-50 also have widely available pre-trained models, facilitating their usage and experimentation. Creator An entity primarily responsible for making the resource Ibnu Utomo Wahyu Mulyono1, Eko Hari Rachmawanto1, Christy Atika Sari1, Md Kamruzzaman Sarker2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Jul 12, 2024 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/68d1db48a28d2a9d616883ac89272b78.pdf 5b7aaa29dc6b33e48103b6c2a3cd87d4 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. 22, NO.5 2024 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 highly effective and simplified DPWM algorithm for NPC inverters for high power factor load applications Subject The topic of the resource High power factor Neutral point clamped Pulse width modulation Three phase neutral point clamped Zero-sequence function Description An account of the resource In recent years, high-power three-level neutral point clamped (NPC) inverters have significantly increased, particularly in the renewable energy sector and industries. These inverters are crucial in applications such as offshore wind farms and high-power motor drives, where reducing switching losses and improving the overall longevity, efficiency, and reliability of inverter systems are key. While conventional space vector pulse width modulation (SVPWM) has been used to reduce switching losses, it also increases the computational burden in the αβ coordinates. Our study introduces an effective and highly efficient carrier-based discontinuous pulse width modulation (DPWM) method specifically designed for high power factor loads to address this challenge. This approach significantly reduces computational execution time by adding the zero-sequence function into the fundamental signals. The efficiency of our new method is evident in the results: the proposed DPWM has successfully reduced switching losses, leading to a substantial enhancement of system efficiency. Our space vector analysis and simulation results, achieved using MATLAB and PLECS software, further validate the efficiency of our approach. Additionally, experimental results with a small-scale three-phase NPC inverter were conducted to validate the effectiveness and efficiency of the proposed DPWM. Creator An entity primarily responsible for making the resource Duc-Hung Nguyen1,2, Minh-Duc Pham1,2, Nho Van Nguyen1,2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Jul 12, 2024 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 High power factor Neutral point clamped Pulse width modulation Three phase neutral point clamped Zero-sequence function https://repository.horizon.ac.id/files/original/651f49c79c34a4e252b6f8bdb1c5e287.pdf 7d52c21b65906dc0f4a3db646db738ae 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. 22, NO.5 2024 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 Analysis comparison, calibration, and application of low-cost soil moisture in smart agriculture based on internet of things Subject The topic of the resource Calibration Capacitive sensor Internet of things Resistive sensor Soil moisture Description An account of the resource The gravimetric method is one of the most accurate for determining soil water content (SWC). Several low-cost sensors have been developed to simplify measuring water content in soil by measuring soil moisture. However, the sensor must be calibrated to determine soil moisture parameters accurately. In this research, comparative analysis, and calibration of resistive and capacitive low-cost sensors were carried out. The calibration method for each sensor uses the gravimetric water content (GWC) and volumetric water content (VWC) methods. Measuring changes in SWC using sensors is performed in real time based on internet of things (IoT). Based on the measurements of the capacitive, resistive type 1, and resistive type 2 sensors with three repetitions, the linear regression R2 values were obtained at 0.980, 0.827, and 0.942, respectively. Furthermore, a stability test is carried out to see how stable the sensor is when making measurements over a long period. The result is that the capacitive, resistive type 1, and resistive type 2 sensors have errors 1.971×10-4, 7.001×10-4, and 6.270×10-4. Based on the results obtained, capacitive sensors have the highest level of accuracy and stability. Furthermore, capacitive sensors are applied to IoT-based agriculture with long range (LoRa) as communication data. Creator An entity primarily responsible for making the resource Beny Agustirandi1, Inayatul Inayah1,2, Nina Siti Aminah1, Maman Budiman1 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource May 26, 2024 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 Calibration Capacitive sensor Internet of things Resistive sensor Soil moisture https://repository.horizon.ac.id/files/original/ffe2b285872f9c5f7294b6ebb63bd697.pdf 55819128c864dd8421373b0d1e643e1e 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. 22, NO.5 2024 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 ANN-based design of miniaturized circular dual-band 4×4 MIMO antenna for 28/38 GHz 5G mmWave applications Subject The topic of the resource 5G technologies Antenna Artificial neural network Millimeter wave Multiple input multiple output Description An account of the resource This paper introduces an innovative approach to design an extremely compact circular dual-band antenna suitable for 28/38 GHz 5G mmWave communications. Leveraging artificial neural network (ANN) and specially, multilayer perceptron (MLP) architecture, the suggested antenna’s dimensions, which allow it to resonate across both frequencies, are predicted. The proposed circular patch antenna, featuring strategically placed rectangular and circular slots in the patch and the ground plane, attains a remarkable frequency range of 3 and 2 GHz for the initial resonant frequency of 28 GHz and the subsequent resonant frequency of 38 GHz bands, respectively. With maximal gains of 4.5 and 7 dB at the corresponding resonance frequency, respectively, the antenna also exhibits high efficiency. Remarkably, the dimensions of the individual antenna element are compact, measuring 4×6×0.8 mm3, showcasing a notable decrease in physical footprint. Furthermore, the single antenna seamlessly transforms into a 4×4 multiple input multiple output (MIMO) antenna occupying a total volume of 16×16×0.8 mm3, showcasing superior isolation and good diversity performance. This research not only contributes significantly to advancing miniaturized dual-band antennas tailored for 5G mmWave applications but also underscores the effectiveness of ANN, particularly MLP architecture, in optimizing antenna designs. The proposed antenna, with its small form factor, stands out as a promising solution for new generation 5G communication systems. Creator An entity primarily responsible for making the resource Lahcen Sellak1, Asma Khabba2, Samira Chabaa1,2, Saida Ibnyaich2, Atmane Baddou1, Abdelouhab Zeroual2, Tole Sutikno3 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 Jul 12, 2024 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 5G technologies Antenna Artificial neural network Millimeter wave Multiple input multiple output https://repository.horizon.ac.id/files/original/b55be8f9f40a1204ee647340310b83a1.pdf 2b19baa5bed9e998aecc32a43ed26655 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. 22, NO.5 2024 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 Augmented reality in customer experience: systematic review Subject The topic of the resource Attitude Augmented reality Consumer experience Customer experience Customer satisfaction Interactivity Systematic review Description An account of the resource Augmented reality (AR) is an emerging technology that offers the opportunity to explore a new way of shopping in the customer experience, showcasing its benefits, such as the superimposition of virtual elements in a physical environment or the high degree of interactivity provided by this technology. Despite its great potential to satisfy customer needs, the evaluation of the customer experience has not been fully studied. The main of this study is to identify the constructs that influence customer experience using the systematic review technique. A total of 88 studies published between 2016 and 2021, which relate to customer experience, were identified. Relevant information, such as the definitions of AR and customer experience, and the constructs that various authors use to assess customer experience, was extracted. The results of the review indicate that five fundamental constructs–attitude, interactivity, customer satisfaction, purchase intention, and hedonic value–are used to assess customer experience. These results contribute to a better understanding of the customer experience with AR. Creator An entity primarily responsible for making the resource Christian Cervantes1, Sussy Bayona-Oré2, Nicolás Pintado Torre1 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource May 26, 2024 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 Attitude Augmented reality Consumer experience Customer experience Customer satisfaction Interactivity Systematic review https://repository.horizon.ac.id/files/original/fcac93a3c41141d9a0e6fb344678c031.pdf 5c582d0a68652a872627b9131c9c8185 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. 22, NO.5 2024 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 Class-G series audio power amplifier for subwoofer Subject The topic of the resource Audio power amplifier Class-AB Class-G Power efficiency Subwoofer Description An account of the resource An audio power amplifier is an electronic device used to amplify a small signal source power at the input into a large signal power at the output that is speaker. In general, audio power amplifiers use class-AB amplifiers. In the process of amplifying the signal power, there will be power losses in the amplifiers which results in relatively lower amplifier power efficiency because there is a difference between the supply and output voltage levels. In this paper a class-G series audio power amplifier for subwoofers is designed to minimize these power losses and increase the power efficiency of the amplifier. The designed amplifier voltage supply is 40 V with a maximum output power of 80 W at an 8 Ω load. The amplifier has frequency response from 20–200 Hz and gain twice. Realization of the power amplifier and measurements were carried out using the circuit maker simulator, and the measurement results of the class-G power amplifier were compared with the class-AB power amplifier. The measurement results show that both power amplifiers meet the design specifications. The proposed class-G amplifier has 0.14% larger total harmonic distortion (THD) but has 10.4% greater power efficiency advantages over the class-AB amplifier. Creator An entity primarily responsible for making the resource Class-G series audio power amplifier for subwoofer 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 May 26, 2024 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 Audio power amplifier Class-AB Class-G Power efficiency Subwoofer https://repository.horizon.ac.id/files/original/eb0a10ee3f316080a94712256f9b7591.pdf f6d20c1004766cab2dbdd50f63154767 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. 22, NO.5 2024 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 Effective capacity analysis of full-duplex-cooperative non-orthogonal multiple access systems Subject The topic of the resource Effective capacity Full-duplex Multi-reconfigurable intelligent surfaces Non-orthogonal multiple access Outage probability Description An account of the resource This study explores a dual-user full-duplex cooperative non-orthogonal multiple access (FD-CNOMA) network, presenting closed-form expressions for the outage probability (OP) and effective capacity (EC) of both users. The analytical results indicate that an increased signal-to-noise ratio (SNR) corresponds to a reduced OP and heightened EC, signifying enhanced communication quality. We analyze the OP and EC under two cases: R1=2R2 and R1=R2. Our analytical expressions reveal that both θ and ρ significantly impact the effective capacities. To validate these analytical findings, Monte Carlo simulations are performed, demonstrating alignment between theoretical insights and practical outcomes. The results underscore the critical role of SNR in influencing network performance, providing valuable insights into optimizing communication quality in FD-CNOMA systems. Creator An entity primarily responsible for making the resource Huu Q. Tran1, Samarendra Nath Sur2 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 May 26, 2024 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 Effective capacity Full-duplex Multi-reconfigurable intelligent surfaces Non-orthogonal multiple access Outage probability https://repository.horizon.ac.id/files/original/403207a23fdec4b85542edaa7735a9dc.pdf dfeb990d25a828905f34a3913e68e789 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. 22, NO.5 2024 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 Enhanced matrix-based error correction coding techniques for embedded memories Subject The topic of the resource Correction masking technique Error correction code H-matrix Memories single bit upsets Multiple bit upsets Description An account of the resource Memories play a very important role in computing systems due to the continuous advancements in technology. They are used to store data that is used for proper system operations. Memory architectures that are more intricately designed are more prone to radiation-induced errors such as single bit upsets (SBU) and multiple bit upsets (MBU). Error correction codes (ECC) are used to recover the corrupted data that are stored in memories. H-matrix-based ECC is the commonly used ECC for memories. On the other side, the correction masking (CM) technique was added to ECC to mask the correctable error patterns. CM technique protects the error-free bits while correcting erroneous bits. In this paper, optimized H-matrices are presented. These matrices are used to design an ECC with the CM technique to correct 2-bit to 7-bit adjacent errors. The result shows there is a reduction in the power of 2, 3, and 4 adjacent bit errors by 19.009%, 4.615%, and 27.934% respectively. Creator An entity primarily responsible for making the resource Jammula Shivani, Mandadi Shankar Teja, Manickaraj Vinodhini 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 May 26, 2024 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