3 10 26 https://repository.horizon.ac.id/files/original/cc793fdc68f4af50d95cc3675a2d13ba.pdf 18335e610a1f654da04183c4ca302ce7 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 Implementation of ICMP flood detection and mitigation system based on software-defined network and sFlow-RT Subject The topic of the resource Internet control message protocol flood Network security OpenFlow Software-defined networks sFlow-RT Description An account of the resource This study evaluates internet control message protocol (ICMP) flood detection and mitigation in software-defined networks (SDN) using an SDN architecture with sFlow-RT for real-time traffic monitoring. OpenFlow switches and sFlow agents detect malicious patterns, following the prepare, plan, design, implement, operate, optimize (PPDIOO) methodology. Unlike prior approaches, this system leverages SDN programmability and sFlow-RT’s real-time analytics to reduce ICMP packets from 311,130.2 to 99 and latency by 80%, outperforming traditional methods in speed and responsiveness. It ensures network availability, with practical benefits for large-scale networks like internet service providers (ISPs). However, sFlow sampling rates may affect accuracy in high-speed networks, and a single OpenDaylight (ODL) controller limits generalizability. Future work should test alternative controllers and extend to other DDoS types like user datagram protocol (UDP) floods in diverse topologies. Creator An entity primarily responsible for making the resource Rikie Kartadie1, Adi Kusjani2, Rangga Warsito3, Yudhi Kusnanto3, Lucia Nugraheni Harnaningrum4 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 10, 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/5b2069c11e29bf001c7e426cbc3b42ec.pdf 2060d49081e50eb1866f4a4e7315f4e0 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 Leveraging technology to improve tuberculosis patient adherence: a comprehensive review Subject The topic of the resource Advanced technologies Internet of things Medication Smart medicine bottle Tuberculosis Description An account of the resource Tuberculosis (TB) is a chronic disease that requires long-term treatment, generally for at least 6 to 9 months. Patients should follow the recommended treatment scheme regularly and completely. Poor adherence to treatment can cause patients to remain a source of infection for others. Patients with TB need additional support during treatment, in terms of information, motivation, and emotional support. Compliance monitoring helps ensure that patients take drugs according to a predetermined schedule. Comprehensive approach review method, careful selection of relevant data from various sources. This aims to provide overview of modern technology used to optimize the success of TB treatment. This paper aims to provide various methods that have existed in conventional and technology-enhanced approaches to monitoring and evaluating the treatment of TB patients. The existing studies only focus on making tools as a reminder to take medication but do not evaluate whether the drug is consumed. In addition, this paper describes prospective ideas involving advanced technology by using the internet of things (IoT)-based smart medicine bottle to accommodate the problem and become an effective communication solution in TB medication. Creator An entity primarily responsible for making the resource Almas Fahrana1, Sri Hernawati1,5, Saiful Bukhori1,4, Al Munawir1,3, Bayu Taruna Widjaja Putra1,2 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 Advanced technologies Internet of things Medication Smart medicine bottle Tuberculosis https://repository.horizon.ac.id/files/original/1e26547143b1517caad16e616a77bcf1.pdf c37f5b3b80f5d0978ed8d122ef2965df 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 compact triband patch antenna design at terahertz frequencies Subject The topic of the resource Ansys high frequency structure simulator Microstrip antenna Multiband antenna Slot Terahertz antenna Description An account of the resource The rapid evolution of terahertz (THz) technology has fueled an increasing demand for efficient, compact, and adaptable antennas that can function for a number of frequency bands in the THz spectral regime. This research outlines the analyses and design process of a multiband antenna for THz applications. Initially, an antenna with a single frequency band is created without any slot, with its lower resonant mode functions at a singular frequency of 171 GHz. To achieve multiband functionality, various rectangular slots can be added into the microstrip antenna’s radiating element. The suggested structure is constructed on a polyimide substrate, while its radiating elements are crafted from copper, with a compact size of 1.4×1.1×0.14 mm. It can achieve a reflection coefficient of −30.38 dB, −33.37 dB, and −19.33 dB at 123 GHz, 168 GHz, and 182 GHz, respectively. Furthermore, the antenna yields favorable gains at the respective frequencies, measuring 3.97 dB, 4.34 dB, and 5.66 dB for 0.123, 0.168, and 0.182 THz respectively. Additionally, the antenna demonstrates high efficiencies of 81.5%, 85%, and 91.2%, respectively. Hence, the suggested THz antenna will be useful for surveillance radar (123 GHz), medical imaging (168 GHz), and radio astronomy (182 GHz) applications. Creator An entity primarily responsible for making the resource Youssef Amraoui1, Imane Halkhams2, Rachid El Alami1, Mohammed Ouazzani Jamil2, Hassan Qjidaa2 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 Microstrip antenna Multiband antenna Slot Terahertz antenna https://repository.horizon.ac.id/files/original/236d6241ab7f6f1ad2d33b147c5cbb68.pdf a16b0d669268f9e2a06515145a93deba 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 Convolutional neural network-based real-time drowsy driver detection for accident prevention Subject The topic of the resource Convolutional neural network Deep learning Driver Drowsiness Lightweight convolutional neural network Description An account of the resource Drowsy driving significantly threatens road safety, contributing to many accidents globally. This paper presents a convolutional neural network (CNN)-based real-time drowsy driver detection system aimed at preventing such accidents, particularly for deployment in Android applications. We propose a lightweight CNN architecture that effectively identifies drowsiness and microsleep episodes by categorizing driver facial expressions into four distinct categories: close-eye expressions, open-eye expressions, yawns, and no yawns. Our model, which employs facial landmark detection and various pre-processing techniques to enhance accuracy, achieves an impressive 96.6% accuracy. This performance surpasses several popular CNN architectures, including VGG16, VGG19, MobileNetV2, ResNet50, and DenseNet121. Notably, our proposed model is highly efficient, with only 0.4 million parameters and a memory requirement of 1.51 MB, making it ideal for real-time applications. The comparative analysis highlights the superior balance between accuracy and resource efficiency of our model, demonstrating its potential for practical deployment in reducing accidents caused by driver fatigue. Creator An entity primarily responsible for making the resource Nippon Datta1, Tanjim Mahmud2, Manoara Begum3, Mohammad Tarek Aziz1, Dilshad Islam4, Md. Faisal Bin Abdul Aziz5, Khudaybergen Kochkarov6, Temur Eshchanov7, Valisher Sapayev Odilbek Uglu8, Sobir Parmanov9, Mohammad Shahadat Hossain10,11, Karl Andersson11 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 Convolutional neural network Deep learning Driver Drowsiness Lightweight convolutional neural network https://repository.horizon.ac.id/files/original/c0368798fd549273789cd573bb9c2606.pdf 016a3bbc857e1f7a0e7032138c8cf41a 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 Jacobian approximation of the Sum-Alpha stopping criterion Subject The topic of the resource Error correction Iterative decoding Log-MAP algorithm Stopping rule Turbo code Description An account of the resource This article will report the development of new application of the Sum-Alpha stopping criterion to the case of log – maximum a posterioru Log-MAP turbo decoding. It shows how to adapt Sum-Alphas quantities when using the Log-MAP algorithm and how to deduce a good decision threshold. We apply a logarithm to the quantity Sum-Alpha which is evaluated by the same Jacobian logarithm of the Log-MAP algorithm. We call this new adaptation Jacobian Approximation of Sum-Alpha (JASA) criterion. The simulation results demonstrate that the JASA criterion achieves comparable performance (in terms of bit error rate (BER) and frame error rate (FER)) to the Sum-Alpha and cross-entropy (CE) criteria, with the same average number of iterations. Creator An entity primarily responsible for making the resource Aissa Ouardi 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 Error correction Iterative decoding Log-MAP algorithm Stopping rule Turbo code https://repository.horizon.ac.id/files/original/567b272c9ba61500071b5129793cfaa0.pdf 360a74bd7a901172c62c00aedb1c3d37 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 Indonesian continuous speech recognition optimization with convolution bidirectional long short-term memory architecture Subject The topic of the resource Bidirectional long short-term memory Continuous speech Convolution bidirectional long short-term memory Indonesian speech recognition Speech recognition Description An account of the resource Speech recognition can be defined as converting voice signals into text or lines of words by using algorithms implemented in computer programs. There are several types of speech recognition, including recognition for isolated word speech, continuous speech, spontaneous speech, and conversational speech. Research on continuous speech recognition, especially in Indonesian, has been developed using both stochastic methods such as Hidden Markov model (HMM) and deep learning methods. Currently, deep learning approaches are more widely used in speech recognition applications. This research optimizes Indonesian speech recognition by adding convolution layers to the bidirectional long short-term memory (Bi-LSTM) architecture. The goal of this research is to find the best architecture so that better Indonesian continuous speech recognition results can be obtained. The dataset used in this research was created by the intelligent systems research group in the Department of Informatics at Universitas Diponegoro. All speakers who participated in this dataset came from five ethnic groups in Indonesia, representing the dialects of their respective ethnic groups. The research results show that by adding a convolution layer to the Bi-LSTM architecture, speech recognition performance increases significantly with an average word error rate (WER) reduction of 15.56% compared to using only the Bi-LSTM architecture. Creator An entity primarily responsible for making the resource Sukmawati Nur Endah, Rismiyati, Priyo Sidik Sasongko, Anwar Petrus F. Naiborhu 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 Bidirectional long short-term memory Continuous speech Convolution bidirectional long short-term memory Indonesian speech recognition Speech recognition