1 10 27 https://repository.horizon.ac.id/files/original/5fce893e773019ad72806eb90e2397aa.pdf 11148365a9d1756d49137658bb5afdb7 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 Optimizing multi-tenant database architecture for efficient software as a service delivery Subject The topic of the resource Cloud Memory efficient Multi-tenant database design Query performance Software as a service Description An account of the resource A multi-tenant database (MTDB) is the backbone for any cloud app that employs a software as a service (SaaS) delivery paradigm. Every cloud-based SaaS delivery strategy relies heavily on the architecture of multitenant databases. The hardware and performance costs for quicker query execution and space savings provided by the architecture of MTDBs are implementation costs. All tenants’ data may be kept in a single table with a common schema and database format, making it the most cost-effective MTDB configuration. The arrangement becomes congested if tenants have varying storage needs. In this research, we present a space-saving architecture that improves transactional query execution while avoiding the waste of space due to different attribute needs. Extensible markup language (XML) and JavaScript object notation (JSON) compare the proposed system against the state of the art. The suggested multitenant database architecture reduces unnecessary space and improves query performance. The experimental findings show that the suggested system outperforms the state-ofthe- art extension table method. Creator An entity primarily responsible for making the resource Sanjeev Kumar Pippal1, Sumit Kumar2, Ruchi Rani3Journal homepage: 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 Aug 5, 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 Cloud Memory efficient Multi-tenant database design Query performance Software as a service 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/c9e55f70617852ad383a56b81903173c.pdf 276beddbeaf2b38ca8cd3b45e9f89af0 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 Enhancing melanoma skin cancer classification through data augmentation Subject The topic of the resource Convolutional neural networks Data augmentation Melanoma MobileNetV2 Skin disease Transfer learning Visual geometry group-19 Description An account of the resource Skin cancer is a dangerous and prevalent cancer illness. It is the abnormal growth of cells in the outermost of the skin. Currently, it has received tremendous attention, highlighting an urgent need to address this worldwide public health crisis. The purpose of this study is to propose a convolutional neural network (CNN) to help dermatology physicians in the inspection, identification, and diagnosis of skin cancer. More precisely, we offer an automated method that leverages deep learning techniques to categorize binary categories of skin lesions. Our technique enlarges skin cancer by utilizing data pre-processing and augmentation to address the imbalanced class problem. Subsequently, fine-tuning is conducted on the pre-trained models visual geometry group (VGG-19) and MobileNetV2 to extract and classify the image features using transfer learning. The model is tested on the society for imaging informatics in medicine international skin imaging collaboration (SIIM-ISIC) 2020 dataset and achieved an accuracy of 95.16%, sensitivity of 90.83%, specificity of 99.2%, area under curve (AUC) of 97.57%, and precision of 99.06%. The proposed model based on MobileNetV2 outperforms the other techniques. Creator An entity primarily responsible for making the resource Mohammed M’hamedi1, Mohammed Merzoug1, Mourad Hadjila2, Amina Bekkouche1 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 Convolutional neural networks Data augmentation Melanoma MobileNetV2 Skin disease Transfer learning Visual geometry group-19 https://repository.horizon.ac.id/files/original/00a92274c1188849d543934463470f13.pdf 0e5842a97173d70c12683c29e9f24a36 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 Leveraging of recurrent neural networks architectures and SMOTE for dyslexia prediction optimization in children Subject The topic of the resource Dyslexia Gate recurrent unit Long short term-memory Recurrent neural networks Synthetic minority oversampling technique Description An account of the resource Dyslexia in children are serious problems that need to be addressed early. Many previous studies have focused on the detection/prediction of dyslexia. However, in the prediction process, there is often an imbalance in the dataset used (between patients with dyslexia and non-dyslexia). Therefore, we are trying to build a system using recurrent neural networks architectures that can quickly and accurately predict the possibility of a child having dyslexia. To overcome the data imbalance between dyslexics and non-dyslexics, we also apply the synthetic minority oversampling technique (SMOTE) method to the dataset. SMOTE will synthesize dyslexic data to balance the numbers with non-dyslexic data. This study used a dataset of 3640 participants (392 dyslexic and 3248 non-dyslexics). For the process of predicting dyslexia, several algorithms such as simple recurrent neural networks (RNN), long short term-memory (LSTM), and gate recurrent units (GRU) are used. As a result, there is an increase in prediction accuracy when SMOTE is applied (compared to without SMOTE) in the dyslexia forecasting process using RNN (92.68% for training and 91.16% for testing), LSTM (94.81% for training and 93.16% for testing), and GRU (96.43% for training and 92.24% for testing). Using SMOTE+RNN architecture in this research increased the accuracy of dyslexia prediction by up to 5% compared to without SMOTE. Creator An entity primarily responsible for making the resource Yuri Pamungkas1, Muhammad Rifqi Nur Ramadani2 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/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/448676fa5ba2c0343052ab3c6318dedb.pdf 3a93fb22d44caa02014055712e312efc 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 The causal loop diagram model of traceability system rental equipment in oil and gas supporting companies Subject The topic of the resource Causal loop diagram Oil and gas support company Rental equipment System thinking Traceability Description An account of the resource Traceability in equipment rental systems enhances security, reliability, and operational transparency by providing the ability to accurately track leased equipment. Challenges in implementing traceability include difficulties in collecting accurate data, the absence of standardized recording practices, and the complexities of integrating technology to ensure complete tracking. This research aims to identify variables affecting the traceability system thinking to improve its efficiency in ongoing business processes. A qualitative descriptive approach is used to offer comprehensive insights into implementing traceability in equipment rental systems, focusing on oil and gas support companies. The study employs the causal loop diagram (CLD) method to dynamically map and identify traceability process variables. Findings show that traceability enables more precise tracking of equipment movement and usage, enhancing inventory management and streamlining maintenance. The CLD method reveals the dynamic relationships between system variables such as equipment availability, maintenance needs, and customer satisfaction, which guide continuous improvement. These results provide stakeholders with valuable insights for optimizing efficiency and service quality in equipment rental operations, particularly in oil and gas support companies. Enhanced traceability can significantly boost operational effectiveness and customer satisfaction. Creator An entity primarily responsible for making the resource Asep Endih Nurhidayat1,2, Rina Fitriana2,3,4, Didien Suhardini2,3,4, Asri Nugrahanti5 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 Causal loop diagram Oil and gas support company Rental equipment System thinking Traceability https://repository.horizon.ac.id/files/original/133f67a4378d182bca5213100424dd96.pdf 801735d316ff897391a26bbbb7e595eb 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 Graphene based terahertz patch antenna for breast tumor detection Subject The topic of the resource 2D materials Breast tumor Graphene Return loss Terahertz Description An account of the resource Breast cancer is a potentially life-threatening disease is one of the most common cancers specially for women. For fast and effective treatment early detection of the tumor is very important. But the frequent checkup is not possible for many people with the traditional way of detection due to their cost and availability. To detect the biomedical imaging terahertz (THz) frequency is a suitable range and using patch antenna in biomedical detection is cost effective compared to the traditional methods. So, to detect the early stage of breast tumor, this article has proposed a graphene based THz antenna. The primary goal of this antenna is to detect the breast tumor with the improvement of the performance and the secondary goal is to make the antenna very light weighted. The design of the antenna was done using computer simulation technology (CST) studio and the dimension of the antenna is kept 18×23 μm2 and a breast phantom model has been designed and the performance analysis of the antenna has been done for both free space and breast tissue. The return loss of the antenna in free space is -52.4422 dB at 6.96 THz. The details of the detection of tumor through the proposed patch antenna has been shown in this study. Creator An entity primarily responsible for making the resource Lia Moni, Md. Saniat Rahman Zishan, Sumit Hassan Eshan, Raja Rashidul Hasan 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 TEXT Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/358c10a17b44373263f7f6981075add1.pdf 2d21791398de92fbba794366282d022b 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 ISSN: 1693-6930, DOI: 10.12928/TELKOMNIKA.v22i5.26064 ❒ 1053 Design of high gain and wideband circular patch antenna based on DGS for 28 GHz 5G applications Subject The topic of the resource Circular patch antenna Defected ground structures Enhanced bandwidth Enhanced gain Improved reflection coefficient Description An account of the resource In this study, a single band 28 GHz antenna with defected ground structure (DGS) has been proposed. The integration of a DGS is explored to exploit ground plane defects for achieving wideband operation. Through systematic design and optimization, our approach achieves remarkable bandwidth enhancement, expanding from 0.75 GHz to 5.78 GHz, covering frequencies from 26.43 GHz to 32.21 GHz, resulting in an impressive impedance bandwidth of 20.5%. Notably, the proposed methodology significantly improves the reflection coefficient, reducing it from -16 dB to -57 dB. Furthermore, the antenna demonstrates a gain of 5.123 dBi and an enhanced voltage standing wave ratio (VSWR) of 1.0056348. Comparative analysis against existing works underscores the superior performance of our antenna design, affirming its potential for various applications. This work presents a novel DGS featuring a circular microstrip patch antenna (MPA) with dimensions of 8×8×0.5 m³, utilizing Rogers RT5880LZ substrate (E=2) with a thickness of 0.5 mm. Creator An entity primarily responsible for making the resource Md. Sohel Rana1,2, Md. Nahid Hasan1, Mohammad Faisal2, Md. Emran Khan1 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 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/cc583db31b7c5479e4d5171293df90f3.pdf ee3e783fc5eb5fc4109b52d518dc3fe1 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 MDI and PI XGBoost regression-based methods: regional best pricing prediction for logistics services Subject The topic of the resource Explainable artificial intelligence Extreme gradient boosting Mean decrease in impurity Permutation importance Retail price prediction Description An account of the resource The logistics industry in Indonesia, with PT Pos Indonesia as the dominant player, is confronted with intense price competition. The challenge lies in establishing the most favorable price for regional logistics services in every region, with the aim of gaining a competitive edge and augmenting revenue. This intricate task encompasses local market conditions, competition, customer preferences, operational costs, and economic factors. To address this complexity, this study proposes the utilization of machine learning for price prediction. The price prediction model devised incorporates the extreme gradient boosting regression (XGBR), support vector machine (SVM), random forest, and logistics regression algorithms. This research contributes to the field by employing mean decrease in impurity (MDI) and permutation importance (PI) to elucidate how machine learning models facilitate optimal price predictions. The findings of this study can assist company management in enhancing their comprehension of how to make informed pricing decisions. The test results demonstrate values of 0.001, 0.005, 0.458, 0.009, and 0.9998. By employing machine learning techniques and explanatory models, PT Pos Indonesia can more accurately determine optimal prices in each region, bolster profits, and effectively compete in the expanding regional market. Creator An entity primarily responsible for making the resource Agus Purnomo1, Aji Gautama Putrada2, Roni Habibi3, Syafrianita4 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 Explainable artificial intelligence Extreme gradient boosting Mean decrease in impurity Permutation importance Retail price prediction 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