1 10 27 https://repository.horizon.ac.id/files/original/7aa34a1a62ec2e55fc3d7ad4cbffee03.pdf e8481a4a384740b2f5a43ca5348522ef 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 Multi objective hyperparameter tuning via random search on deep learning models Subject The topic of the resource Convolutional neural network Deep learning models Hyperparameter tuning Multiobjective optimization Random search Description An account of the resource This research examines the efficacy of random search (RS) in hyperparameter tuning, comparing its performance to baseline methods namely manual search and grid search. Our analysis spans various deep learning (DL) architectures-multilayer perceptron (MLP), convolutional neural network (CNN), and AlexNet implemented on prominent benchmark datasets of Modified National Institute of Standards and Technology (MNIST) and Canadian Institute for Advanced Research-10 (CIFAR-10). In the context of this study, the evaluation will be adopting a multi-objective framework, navigating the delicate trade-offs between conflicting performance metrics, including accuracy, F1-score, and model parameter size. The primary objective of employing a multi-objective evaluation framework is to enhance the understanding regarding the interactions of these performance metrics interact and influence each other. In real-world scenarios, DL models often need to strike a balance between these conflicting objectives. This research adds to the increasing wealth of knowledge in hyperparameter tuning for DL models and serves as a reference point for practitioners seeking to optimize their DL architectures. The results of our analysis are positioned to provide invaluable insights into the intricate balancing act required during the process of hyperparameter fine-tuning. These insights will contribute to the ongoing advancement of best practices in optimizing DL models and facilitating the ongoing optimization of the DL models. Creator An entity primarily responsible for making the resource Abdul Rahman Mohamad Rom, Nursuriati Jamil, Shafaf Ibrahim 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/6b6d5a0b5f98d94e4f258a77f7c326fb.pdf 18cb222170a7d166da8f02746fd60858 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 impact of software metrics in NASA metric data program dataset modules for software defect prediction Subject The topic of the resource K-nearest neighbor NASA metric data program Software defect Software defect prediction Software metrics Description An account of the resource This paper discusses software metrics and their impact on software defect prediction values in the NASA metric data program (MDP) dataset. The NASA MDP dataset consists of four categories of software metrics: halstead, McCabe, LoC, and misc. However, there is no study showing which metrics participate in increasing the area under the curve (AUC) value of the NASA MDP dataset. This study utilizes 12 modules from the NASA MDP dataset, where these 12 modules are being tested into 14 relationships of software metrics derived from the four existing metric categories. Subsequently, classification is performed using the k-nearest neighbor (kNN) method. The research concludes that software metrics have a significant impact on the AUC value, with the LoC+McCabe+misc metrics relationship influencing the improvement of the AUC value. However, the metrics relationship that has the most impact on achieving less optimal AUC values is McCabe. Halstead metric also plays a role in decreasing the performance of other metrics. Creator An entity primarily responsible for making the resource Adinda Ayu Puspita Ramadhani, Radityo Adi Nugroho, Mohammad Reza Faisal, Friska Abadi, Rudy Herteno 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 2, 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 K-nearest neighbor NASA metric data program Software defect Software defect prediction Software metrics https://repository.horizon.ac.id/files/original/d06d873c6c8cc277917905904e4b1d4c.pdf ad72306f6306630750e1ca22a5c84a22 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 Pansharpening with multi-CAE: impact of patch size and overlapping pixels on spectral and spatial distortion Subject The topic of the resource Adaptive intensity-hue-saturation Convolutional-autoencoder Multispectral Pansharpening Remote sensing Description An account of the resource A novel technique utilizing a convolutional autoencoder (CAE) is introduced with the aim of enhancing the spatial resolution of multispectral (MS) images while concurrently mitigating spectral distortion. First, an original panchromatic (PAN) image is constructed from its spatially degraded version. Then, the relationship between the original PAN image and its degraded version is utilized to reconstruct the high-resolution MS image; in addition, an intensity component of MS image, which is obtained using an adaptive intensity-hue-saturation (AIHS), is reconstructed by utilizing the aforementioned relationship. Two types of remote sensing datasets are adopted, and the effect of the patch size with the overlapping pixel on spectral and spatial distortion is considered. After training CAE, the low-resolution MS image and its intensity component are given to the trained network as input to obtain the MS image and intensity component with better details. Eventually, the fused image is obtained by using a component substitution (CS) framework. Experimental findings corroborate that the proposed method yields superior outcomes compared with several existing approaches, demonstrating advantages in both objective metrics and visual fidelity. Creator An entity primarily responsible for making the resource Ahmad Al Smadi1, Ahed Abugabah2, Mutasem Khlail Alsmadi3, Ala Alsanabani4, Atif Mehmood5, Ahmad Mohammad Al-Smadi6 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 2, 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 Adaptive intensity-hue-saturation Convolutional-autoencoder Multispectral Pansharpening Remote sensing 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/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/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/e086474e87c5ba9349859e432926d31c.pdf 57435db0eacd15263e79ca4e547fef33 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 Migration aspects from monolith to distributed systems using software code build and deployment time and latency perspective Subject The topic of the resource Containerization Continuous-integration Continuous-delivery Fault tolerance Microservices Scalability Description An account of the resource The transition from an error-prone, slower, and extremely high-volume legacy system like monolithic system to a faster, lighter, and error-free microservices based system is not always so simple. Microservices are independently deployable and allow for a better team autonomy. In this work, several migration efforts to migrate from a legacy based monolithic system to a pure distributed microservices based system has been tested and deployed in keeping two DevOps principles, the software code build and deployment time and latency in monolithic and microservices. Some real-time projects are considered to measure the performance and the time taken to execute the experiments. To measure the total build and deployment time and latency, Jenkins, Prometheus, and JMeter are installed which are industry-recommended softwares. It is observed that there is a total of 7 seconds taken to build and deploy at containers for 10 microservices whereas 10 monolith applications took almost 260 seconds to be built and deployed to the application server. While increasing more requests per second it is observed that upto 3000 requests per second, it impacted the response time of monolith applications but microservices stays the same. The main conclusion is that microservices are rarely impacted in response time with respect to requests per second. Creator An entity primarily responsible for making the resource Alok Aggarwal, Vinay Singh 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 10, 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 Containerization Continuous-integration Continuous-delivery Fault tolerance Microservices Scalability 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/1923fb321087a8fbd598ee90306e2cd3.pdf 3c0bae9e088250acfe9d81450757d2e1 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 Optimized decision tree classification method for diabetes prediction Subject The topic of the resource Decision tree Diabetes prediction Highest accuracy Particle swarm optimization Parameter optimization Description An account of the resource Diabetes is one of the most deadly chronic diseases because most sufferers do not realize they have it. A more accurate prediction of diabetes disease must be made to reduce the risk of bad things happening to sufferers. This research will optimize the decision tree (DT) classification method for diabetes prediction. Optimization is done by splitting criteria, splitting data, particle swarm optimization (PSO), and parameter optimization to find the highest and most accurate forecast of diabetes. Splitting criteria is done by comparing the results of three criteria, namely gain ratio (GR), information gain (IG), and gini index (GI). Splitting data is done by dividing training data and testing data into three comparison groups, namely 70:30, 80:20, and 90:10. The application of PSO and parameter optimization is carried out to increase the accuracy value. The processed data is taken from the UCI machine learning repository with 520 records and 17 attributes (1 class/label attribute). From the experiments, the GI criterion with splitting data 90:10 obtained the greatest accuracy of 98.08%, and the combination with PSO resulted in an accuracy of 97.66%. Meanwhile, parameter optimization with splitting data 90:10 combined with GR criteria resulted in the highest accuracy of 97.90%. Creator An entity primarily responsible for making the resource Elly Muningsih1, Fabriyan Fandi Dwi Imaniawan1, Aprih Widayanto2, Eva Argarini Pratama1, Sutrisno2, Sri Kiswati1 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 Decision tree Diabetes prediction Highest accuracy Particle swarm optimization Parameter optimization https://repository.horizon.ac.id/files/original/4b439b64ed1f3ac507b33c34fddd7639.pdf ba05720930c8e88911f6ef07b463cd9e 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 Effectiveness of Bluetooth communication of digital stethoscope using quality of service method Subject The topic of the resource HC-05 Heart sound Quality of service Stethoscope Wireless Description An account of the resource This study aims to design and evaluate a wireless electronic stethoscope that can transmit heart sound using Bluetooth HC-05 and Bluetooth 5.0 transmitter. This novel design contributes to the remote diagnosis and monitoring of heart conditions, especially for patients with infectious diseases. The heart sound signals are captured using a mic condenser mic, amplified, filtered, and converted to digital data by a microcontroller. The data are then transmitted by Bluetooth HC-05 to a module and by Bluetooth 5.0 transmitter to a headset. The quality-of-service parameters such as throughput, delay, and packet loss ratio (PLR) of the data transmission at different distances are measured. The results show that the wireless electronic stethoscope can transmit heart sound data with a small PLR of 0.10% and a throughput of 1002.5 bps. The study concludes that the wireless electronic stethoscope is an effective and useful device for examining heart conditions remotely, without compromising the functionality of the device. Creator An entity primarily responsible for making the resource Endang Dian Setyoningsih, Sumber, Triwiyanto, Farid Amrinsani 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 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 HC-05 Heart sound Quality of service Stethoscope Wireless