1 10 27 https://repository.horizon.ac.id/files/original/984576bb54d3b55f18273b46af1f1b21.pdf 12c7a0347b3dad2cb3c3aff22c30536e 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.4 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 Privacy and safety of narrowband internet of things devices Subject The topic of the resource Internet of things Long-term evolution Narrowband internet of things Privacy Telecommunication Description An account of the resource Technology’s increasing role in everyday life has pushed the evolution of the internet of things (IoT), which now permeates industries like information technology, agribusiness, and transportation. Critical concerns in IoT security include platform diversity and issues with authentication and authorization. Critical vulnerabilities identified by researchers contain unencrypted communications, compromised interfaces, and compromised access control processes. A new solution, narrowband IoT (NB-IoT), has responded. Based on the cellular network, this technology is designed for improved security and efficiency, operating within the fourth-generation mobile networks and leveraging essential network components. The current study focuses on NB-IoT vulnerabilities, particularly in the radio segment, which is notably vulnerable. The research utilized the open-source tool OpenLTE and hardware like software-defined radio (SDR) in a setting with active NB-IoT sensors on an LTE network. This included deploying a test listening tool and a laboratory-based IMSI catcher to intercept active device communications in a testbed. The results highlight significant vulnerabilities: sensors were deactivated following simulated network attacks with rogue eNodeB and traffic area update (TAU) messages, revealing the technology’s susceptibility to connection failure. Creator An entity primarily responsible for making the resource Ali Abdollahi1, Shohreh Behnam Arzandeh2, Mohsen Sheibani3 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 Mar 29, 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 Internet of things Long-term evolution Narrowband internet of things Privacy Telecommunication https://repository.horizon.ac.id/files/original/855749a31852bfa1d9cea0a4092e79e8.pdf 6e22570cf4ee995f00e1de1f164ccf9d 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.4 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 Unlocking insights from Ministry of Marine Affairs and Fisheries annual reports using LDA: a deep dive into SDG 14 Subject The topic of the resource Annual reports Fisheries Latent Dirichlet allocation Marine resources Ministry of Marine Affairs and Fisheries Strategic planning Topic modeling Description An account of the resource Annual reports serve as vital instruments for government ministries and agencies, enabling transparency and accountability in managing state budgets (APBN) and activities, thereby fulfilling a crucial role in public accountability, particularly in the context of sustainable development goal (SDG) 14. However, due to their extensive nature, it becomes imperative to conduct topic modeling analysis to discern trends and topics within these reports. In this study, latent Dirichlet allocation (LDA), a prominent topic modeling technique, is employed to analyze the annual reports of the Ministry of Marine Affairs and Fisheries (KKP) Indonesia from 2015 to 2022. Utilizing the coherence score as an evaluation metric, we assess the quality of topic models across each report year. Our findings underscore the consistent emphasis on fisheries and marine-related initiatives, emphasizing their relevance to SDG 14 and Indonesia’s maritime landscape. Ultimately, this study offers valuable insights to inform strategic planning and decision-making processes within the KKP, contributing to the advancement of SDG 14 and promoting sustainable development in Indonesia’s fisheries and marine sectors. Creator An entity primarily responsible for making the resource Ahmad Marzuqi1, Rezzy Eko Caraka1,2,3,4,6, Prana Ugiana Gio5, Rung Ching Chen6, Maengseok Noh7, Bens Pardamean8,9 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 Mar 29, 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 Annual reports Fisheries Latent Dirichlet allocation Marine resources Ministry of Marine Affairs and Fisheries Strategic planning Topic modeling https://repository.horizon.ac.id/files/original/3aba67938c758a5a05337a7068621023.pdf 337e13f602e8802a85aa3f19848a79a1 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.4 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 performance estimation of a slotted inverted F-shaped tri-band antenna for satellite/mm-wave 5G application Subject The topic of the resource 5G Antenna Artificial neural network Industrial and innovation Satellite Tri-band Description An account of the resource In this research, we explain comprehensive industrial and innovation results on using an artificial neural network (ANN) method to improve the performance of microstrip patch antennas for 5G, indoor-outdoor, and Ku band uses. To determine if an antenna is appropriate, this article discusses multiple methods, one of which is to do a simulation using validating software like high frequency structure simulator (HFSS) and Altair Feko. Based on the Rogers RT 5880 substrate, the antenna is constructed. There is a loss tangent of 0.0009 and its dimensions are 17.1053 mm in length and 16 mm in width. Its dielectric constant is 2.2. Despite its small size, it boasts an impressive maximum efficiency of almost 90% and a gain of approximately 8 dB. As an indicator of ANN model performance, we may look at the R-squared value (99%), the mean square error (MSE), which is approximately 0.0015, and the confidence interval (99%). The ANN models are the most accurate and have the lowest error rate when it comes to predicting efficiency and gain. The suggested antenna is a promising contender for the targeted Ku band, indoor/outdoor, and 5G uses, as verified by the clustering of computer simulation technology (CST), HFSS, and Altair Feko simulated results with the measured and predicted outcomes of ANN approach. Creator An entity primarily responsible for making the resource Md. Kawsar Ahmed1, Kamal Hossain Nahin1, Md. Sharif Ahammed1, Md. Ashraful Haque1, Narinderjit Singh Sawaran Singh2, Redwan Al Mahmud Asad Ananta1, Jamal Hossain Nirob1, Mirajul Islam3, Liton Chandra Paul4 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 Mar 29, 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 Antenna Artificial neural network Industrial and innovation Satellite Tri-band https://repository.horizon.ac.id/files/original/cc269b98bf6759cbc9113bdea1ae3ac5.pdf 54a738c6e3ec77beafebe3d1b016ebec 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.4 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 Electro-optic MMI coupler as wavelength demultiplexer for advanced SDM wireless-WDM optical signal converter Subject The topic of the resource Electro-optic modulator LiNbO3 Multimode interference coupler SDM-WDM converter Wavelength demultiplexer Description An account of the resource We report the design, fabrication, and experimental results of an optical wavelength demultiplexer for a new wireless-optical signal converter for Beyond-5G/6G mobile communication system. This optical wavelength demultiplexer is based on a lithium niobate (LiNbO3) multimode interference (MMI) coupler and is intended to be applied in an advanced electro-optics modulator (EOM) with ability in converting the space division multiplexing (SDM) wireless-wavelength division multiplexing (WDM) optical signals. The designed MMI coupler displays a high splitting ratio over -13 dB in both O and L bands. The results from the experiments align well with the simulation. The utilization of the MMI coupler in EOM enables the direct conversion of SDM wireless signals to WDM optical signals, without any additional power supply. The modification of the characteristic of a constructed MMI coupler can be achieved by controlling of the applied voltage of the device. Creator An entity primarily responsible for making the resource Mefina Yulias Rofianingrum1,2, Yui Otagaki1, Hiroshi Murata1 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 Mar 29, 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 Electro-optic modulator LiNbO3 Multimode interference coupler SDM-WDM converter Wavelength demultiplexer https://repository.horizon.ac.id/files/original/798a3e1f49c4b5a1853496b3b7bca7f8.pdf 6354801dcff04dbd8d0b2bd40b18ff91 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.4 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 Imputation missing value to overcome sparsity problems Subject The topic of the resource Collaborative filtering Cosine similariy Imputation missing value Recommendation system Sparsity Description An account of the resource Collaborative filtering (CF) is a method to be used in recommendation systems. CF works by analyzing rating data patterns from previous users to produce recommendations according to their interests. However, it faces a crucial problem, sparsity, a condition where a lot of data is empty, which will affect the quality of the recommendations produced. To state this problem, the purpose of this study is to input methods including mean, min, max, and k-nearest neighbor imputation (KNNI). The steps taken include imputation of empty data, followed by similarity calculations using the cosin similarity method, and evaluation using root mean square error (RMSE). The experimental result shows that the mean method is excellent with an average similarity value of 0.99 and an RMSE value of 0.98. Creator An entity primarily responsible for making the resource RZ Abdul Aziz1, Sri Lestari1, Fitria1, Febri Arianto2 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 Mar 20, 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 Collaborative filtering Cosine similariy Imputation missing value Recommendation system Sparsity https://repository.horizon.ac.id/files/original/ac75f52529f5a856dd58e58333e263e5.pdf d837ef81010ed6b6b6ebfa1fedb78e17 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.4 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 Overview of the progression of state-of-the-art language models Subject The topic of the resource Artificial intelligence BERT model Generative pre-trained transformer Machine learning Question-answering Description An account of the resource This review provides a concise overview of key transformer-based language models, including bidirectional encoder representations from transformers (BERT), generative pre-trained transformer 3 (GPT-3), robustly optimized BERT pretraining approach (RoBERTa), a lite BERT (ALBERT), text-to-text transfer transformer (T5), generative pre-trained transformer 4 (GPT-4), and extra large neural network (XLNet). These models have significantly advanced natural language processing (NLP) capabilities, each bringing unique contributions to the field. We delve into BERT’s bidirectional context understanding, GPT-3’s versatility with 175 billion parameters, and RoBERTa’s optimization of BERT. ALBERT emphasizes model efficiency, T5 introduces a text-to-text framework, and GPT-4, with 170 trillion parameters, excels in multimodal tasks. Safety considerations are highlighted, especially in GPT-4. Additionally, XLNet’s permutation-based training achieves bidirectional context understanding. The motivations, advancements, and challenges of these models are explored, offering insights into the evolving landscape of large-scale language models. Creator An entity primarily responsible for making the resource Asmae Briouya, Hasnae Briouya, Ali Choukri 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 Jan 15, 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 Artificial intelligence BERT model Generative pre-trained transformer Machine learning Question-answering https://repository.horizon.ac.id/files/original/65e99c166e8fac99fcdb29978fb446da.pdf ae17f973ad3dfc5486e060da8c838be6 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.4 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 Rumor detection based on deep learning techniques: a systematic review Subject The topic of the resource Deep learning Feature selection Rumor detection Social media Systematic review Description An account of the resource The rise of social media platforms has led to an increase in the flow and dissemination of information, but it has also made generating and spreading rumors easier. Rumor detection requires understanding the context and semantics of text, dealing with the evolving nature of rumors, and processing vast amounts of data in real-time. Deep learning (DL)-based techniques exhibit a higher accuracy in detecting rumors on social media compared to many traditional machine learning approaches. This study presents a systematic review of DL approaches in rumor detection, analyzing datasets, pre-processing methods, feature taxonomy, and frequently used DL methods. In the context of feature selection, we categorize features into three areas: text-based, user-based, and propagation-based. Besides, we surveyed the trends in DL models for rumor detection and classified them into convolutional neural networks (CNN), recurrent neural networks (RNN), graph neural networks (GNN), and other methods based on the model structure. It offers insights into effective algorithms and strategies, aiming to guide researchers, developers, social media users, and governments in detecting and preventing the spread of false information. The study contributes to enhancing research in this field and identifies potential areas for future exploration. Creator An entity primarily responsible for making the resource Lifan Zhang1,2, Shafaf Ibrahim1, Ahmad Firdaus Ahmad Fadzil1 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 Feb 15, 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 Deep learning Feature selection Rumor detection Social media Systematic review https://repository.horizon.ac.id/files/original/22d1498b83bf29253228e7d71cb84b64.pdf e530b12181663b729c04a4a7b0fa4a37 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.4 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 Classifying date fruits using the transfer learning model Subject The topic of the resource Date fruit Educational technology Image classification Malaysia Mobile application Transfer learning Description An account of the resource Date palm trees originate in many tropical regions of the world and produce dates. Each variety can be differentiated through the shape, texture, size, and colour of the fruits. People have difficulties visualising and recognising the types of date fruits because they have many varieties and species. An Android-based mobile application is being proposed to help users quickly identify the dates based on their images and expand their knowledge of dates. The date fruit species classification mobile application categorises nine different varieties of date fruits, namely Ajwa, Medjool, Rutab, Nabtat Ali, Meneifi, Galaxy, Sugaey, Shaishe, and Sokari. The classification, which is based on a transfer learning technique from a pre-trained neural network, achieved a 94.2% accuracy rate. The mobile application features a user-friendly graphical interface that makes it easy to use and understand. Users can learn about different date fruit varieties and improve knowledge retention through a mini game. The application’s usability, usefulness, and interface design were confirmed through the user acceptance survey. Creator An entity primarily responsible for making the resource Alia Nadzirah Mohd Adnan1, Nurul Amelina Nasharuddin1,2 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 Mar 29, 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/847c8a1833aec9e934582eb74329f0d6.pdf 77b435a89ace5b451acbb2162686dccb 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.4 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 The influence of machine learning on the predictive performance of cross-project defect prediction: empirical analysis Subject The topic of the resource Cross-project Defect prediction Machine learning Random forest Software defect Description An account of the resource This empirical investigation delves into the influence of machine learning (ML) algorithms in the realm of cross-project defect prediction, employing the AEEEEM dataset as a foundation. The primary objective is to discern the nuanced influences of various algorithms on predictive performance, with a specific focus on the F1 score metric as evaluation criterion. Four ML algorithms have been carefully assessed in this study: random forest (RF), support vector machines (SVM), k-nearest neighbors (KNN), and logistic regression (LR). The choice of these algorithms reflects their prevalence in software defect prediction literature and their diversity. Through rigorous experimentation and analysis, the investigation unveils compelling evidence affirming the superiority of RF over its counterparts. The F1 score utilized as evaluation metric, capturing the delicate balance between precision and recall, essential in defect prediction scenarios. The nuanced examination of algorithmic efficacy provides practical insights for developers and practitioners navigating the challenges of cross-project defect prediction. By leveraging the rich and diverse AEEEEM dataset, this study ensures a comprehensive exploration of algorithmic influences across varied software projects. The findings not only contribute to the academic discourse on defect prediction but also offer practical guidance for real-world application, emphasizing the pivotal role of RF as a tool in enhancing predictive accuracy and reliability. Creator An entity primarily responsible for making the resource Yahaya Zakariyau Bala1,4, Pathiah Abdul Samat1, Khaironi Yatim Sharif3, Noridayu Manshor2 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 Mar 29, 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/a618d917e1e34723822d59825907bf54.pdf d1a4cc67bd2951b6122ecc6d55e25ee7 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.4 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 Enhancing object detection for humanoid robot soccer: comparative analysis of three models Subject The topic of the resource Humanoid robot soccer Object detection YOLO-NAS YOLOv7 YOLOv8 Description An account of the resource The humanoid robot soccer system encounters a notable challenge in object detection, primarily concentrating on identifying the ball and often neglecting crucial elements like opposing robots and goals, resulting in on-field collisions and imprecise ball shooting. This study comparatively evaluates three you only look once (YOLO) real-time object detection system variants: YOLOv8, YOLOv7, and YOLO-NAS. A dataset of 2104 annotated images, covering classes such as ball, goalpost, and robot, was curated from Roboflow and robot-captured images. The dataset was partitioned into training, validation, and testing sets, and each YOLO model underwent extensive fine-tuning over 100 epochs on this custom dataset, leveraging the pre-trained common objects in context (COCO) model. Evaluation metrics, including mean average precision (mAP) and inference speed, assessed performance. YOLOv8 achieved the highest accuracy with a mAP of 0.92, while YOLOv7 showed the fastest inference speed of 24 ms on the Jetson Nano platform. Balancing accuracy and speed, YOLO-NAS emerged as the optimal choice. Thus, YOLO-NAS is recommended for object detection for humanoid soccer robots, regardless of team affiliation. Future research should focus on enhancing object detection through advanced training techniques, model architectures, and sensor fusion for improved performance in dynamic environments, potentially optimizing through scenario-specific fine-tuning. Creator An entity primarily responsible for making the resource Handaru Jati1, Nur Alif Ilyasa1, Yuniar Indrihapsari1, Ariadhie Chandra2, Dhanapal Durai Dominic3 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 Mar 29, 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