1 10 26 https://repository.horizon.ac.id/files/original/58cb86840ac22aa469cde62993e5727f.pdf d551aebc697d468922a0546d2f1d49b2 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.1 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 1×2 microstrip patch antennas array for mm-waves 5G application Subject The topic of the resource Antenna array Bandwidth Five generation Gain Radar system Description An account of the resource In this paper we present the design of an antenna array for 5G applications. The proposed prototype of the antenna array is design to function at both 24 GHz and 27 GHz frequencies, utilizing Rogers RT5880 with a permittivity equal to 2.2 and a loss tangent of 0.0009. The CST Studio Suite software is employed for simulating the suggested. The primary goals of this research encompass achieving a notable return loss, increased gain, minimized voltage standing wave ratio (VSWR), enhanced directivity, and an overall improvement in operational efficiency. The results of the simulation showcase encouraging performance metrics, including a return loss of -68.70 dB, a bandwidth larger 7.369 GHz (ranging from 22.191 GHz to 29.56 GHz), a gain of 10.52 dB. Furthermore, the microstrip patch antennas (MPA) array system showcases an impressive efficiency rating of 95.63%. Creator An entity primarily responsible for making the resource Karima Benkhadda1, Fatehi ALtalqi1, Abdelhak Bendali1, Abderrahim Haddad2, Samia Zarrik1, Sanae Habibi1, Zahra Sahel1, Mohamed Habibi1, Abdelkader Hadjoudja1 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 Oct 27, 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 Antenna array Bandwidth Five generation Gain Radar system https://repository.horizon.ac.id/files/original/6dd4aa9ba1a46ea0e21faecfa3dea428.pdf e7121f76ed2d06c8069f3a3ffdaa0dba 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.1 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 79 GHz three stacked cylindrical dielectric resonator antenna array for automotive radar systems Subject The topic of the resource 79 GHz Antenna array Automotive radar system Short range radar Stacked cylindrical dielectric resonator antennas Description An account of the resource Very high gain and sharp radiation beam of an original antenna array design, made of 16 linear three stacked cylindrical dielectric resonator antennas (three ScCDRA), is proposed in this work for automotive short-range radar (SRR) applications operating at 79 GHz. Firstly, a single antenna which functions around 79 GHz and reaches a gain value up to 11.8 dB is designed with success by piling three cylindrical DRA having permittivity values, respectively, 17.9, 16.9, and 9. However, relatively near peak values, of the main and the side lobs, makes the preliminary design less efficient for vehicle radar applications. To get a radar design with enhance properties, such as lower return loss, higher gain and especially reduced radiation pattern side lobs, we proceeded with an array design of 16 linear antenna-elements (1×16). As results, the three ScCDRA array structure provides 21.3 dB as gain peak value, a very narrow angular half power beam width (HPBW) of radiation pattern of 0.7 degree at 79 GHz. Feeding network design and positions of the sixteen linear antenna-elements, within this array, have been extensively investigated to carry out an optimal design still resonating around 79 GHz with a lower S11 parameter value up to -40 dB and hilly directional characteristics of radiation diagram. Creator An entity primarily responsible for making the resource Abderrahim Haddad1, Azize Bhaij1, Mousa Hussein2, Mohssin Aoutoul1, Mohammed El Jourmi3, Elhadi Baghaz4, Said Dlimi1, Youssef Errami4, Abdellatif Obbadi4, Fadwa El Moukhtafi1, Redouane Jouali3 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 Oct 18, 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/b60b316c4b4bd484d5f23fd7d52fb581.pdf e0a61b1213dce8fd7b647e1c1ea09e40 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.1 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 comparative analysis of transfer learning models on suicide and non-suicide textual data Subject The topic of the resource Deep learning Media social Sentiment analysis Suicide Transformer model Description An account of the resource The rise of social media has allowed individuals to express themselves freely, increasing the visibility of mental health concerns, including suicidal tendencies. This issue is particularly significant, as suicide is one of the leading causes of death globally. The objective of this study is to develop a model capable of accurately detecting suicide-related textual data using advanced natural language processing techniques. To achieve this, we applied transfer learning models, including bidirectional encoder representations from transformers (BERT), robustly optimized bidirectional encoder representations from transformers (RoBERT), a lite BERT (ALBERT), and decoding-enhanced BERT with disentangled attention (DeBERTa). the dataset used in this research includes 232,074 posts from Reddit, categorized into suicide and non-suicide labels. Preprocessing steps such as removing HTML tags, special characters, and punctuation were applied, followed by stopword removal and lemmatization. The models were trained and evaluated using accuracy, precision, recall, and F1-score metrics. Among the models tested, DeBERTa demonstrated superior performance, achieving an accuracy of 98.70% and an F1-score of 98.70%. These findings suggest that transfer learning models, particularly DeBERTa, are effective in identifying suicidal ideation in textual data. Creator An entity primarily responsible for making the resource Merinda Lestandy1, Abdurrahim2, Amrul Faruq1, Muhammad Irfan1 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 Jan 22, 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 Deep learning Media social Sentiment analysis Suicide Transformer model https://repository.horizon.ac.id/files/original/2ef38393877d4eed03e242c64c1591c0.pdf 020955bb8a62f1c1467ccd6b933f9740 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.1 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 dual-band rectangular shape incorporated into circular patch antenna for 2.4/5 GHz wireless local area network applications Subject The topic of the resource 2.4/5 wireless local area network Circular shape Dual-band antenna Inverted U-slots Omnidirectional antenna Patch antenna Rectangular shape Creator An entity primarily responsible for making the resource Suthasinee Lamultree1, Nattakarn Somsanook1, Wararak Narkkoht1, Chuwong Phongcharoenpanich2 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 Oct 18, 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/3d17c8a043348cd3274ee9a718db9000.pdf ca8c05c5795cec30f8baee67945e8cd0 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.1 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 BER performance in NOMA downlink transmission using AWGN, Rayleigh, and Rician fading channels Subject The topic of the resource Additive white gaussian noise Bit error rate Fifth generation Non-orthogonal multiple access Signal-to-noise ratio Successive interference cancellation Description An account of the resource 5G wireless technology is accessible to a much larger user base compared to previous cellular networks. Users can connect to base stations (BSs) in a variety of methods, including of frequency division multiplexing (FDM), orthogonal frequency division multiplexing (OFDM), orthogonal multiple access (OMA), and non-orthogonal multiple access (NOMA). NOMA is considered to be the best one for 5G. Additionally, NOMA employs radio resource optimization and interference control techniques to enhance spectrum network efficiency and support extremely large connections, such as successive interference cancellation (SIC). Users can be divided into two categories: strong and weak. While weak users may experience a low data rate, strong users need a high one. In this paper, we examine the two users in relation to transmit power, the indicative bit error rate (BER), outage probability (OP), and rate attainable capacity using additive white gaussian noise (AWGN) channel. Moreover, we compare the BER and signal-to-noise ratio (SNR) per bit for two users, showing that the Rician fading channel performs better than the Rayleigh fading channel. The use of NOMA technology is the optimal choice and indicates an improvement in the precision of data transmission within the communication system, particularly in the presence of noise and interference. Creator An entity primarily responsible for making the resource Abdelbari Falloun, Abdessalam Ait Madi 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 Nov 16, 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/0e2c86ceb9c3263ebe2b26d653a0b0fa.pdf 0f6d5849b4b9ea6cd7f44bda163925ad 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.1 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 Comparative analysis of cross-platform development methodologies: a comprehensive study Subject The topic of the resource .NET MAUI Cross-platform development frameworks Flutter GUI React Native Xamarin Description An account of the resource In an era marked by the proliferation of devices and operating systems, delivering native-feeling applications across platforms has become indispensable. This paper scrutinizes native development through the lens of cross-platform frameworks, investigating their merits, major contenders such as React Native, Flutter, Xamarin, and the nascent .NET MAUI, and their practical implementations. By dissecting the distinct strengths and considerations of each framework, we provide developers with insights to make judicious decisions commensurate with their requirements and proficiencies. This inquiry underscores how cross-platform frameworks empower developers to broaden their audience reach while upholding native performance standards, thereby shaping the trajectory of app development through sustained innovation and integration with emergent technologies. Creator An entity primarily responsible for making the resource Raiymbek Jangassiyev1, Zhanat Umarova1, Aisaule Ussenova1, Zlikha Makhanova1, Nurlybek Zhumatayev2, Manat Amirov3, Gulzhan Koishibekova1 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 Nov 26, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/3f12d39186a158006e58448a09a50053.pdf 5fcddab19c9dcb12b9c1e4b1f363500c 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.1 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 Customer segmentation in e-commerce: K-means vs hierarchical clustering Subject The topic of the resource Brand interests Customer insights Customer segmentation E-commerce Hierarchical clustering K-means clustering Description An account of the resource Customer segmentation is important for e-commerce companies to understand and target different customers. The primary focus of this work is the application and comparison of K-means clustering and hierarchical clustering, unsupervised machine learning techniques, in customer segmentation for e-commerce platforms. Clustering leverages customer search behavior, reflecting brand preferences, and identifying distinct customer segments. The proposed work explores the K-means algorithm and hierarchical clustering. It uses them to classify customers in a standard e-commerce customer dataset, mainly focused on frequently searched brands. Both techniques are compared based on silhouette scores and cluster visualizations. K-means clustering yielded well-separated segments compared to hierarchical clustering. Then, using the K-means algorithm, customers are classified into different segments based on brand search patterns. Further, targeted marketing strategies are discussed for each segment. Results show three customer segments: high searchers-low buyers, loyal customers, and moderate engagers. The proposed work provides valuable insights into customers that could be used for developing targeted marketing campaigns, product recommendations, and customer engagement strategies to enhance the conversion rate, customer satisfaction, and, in turn, the growth of an e-commerce platform. Creator An entity primarily responsible for making the resource Sumit Kumar1, Ruchi Rani2, Sanjeev Kumar Pippal3, Riya Agrawal2 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 Sep 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 https://repository.horizon.ac.id/files/original/ee1adcb1ebaecfdc1453619693eaa52f.pdf 8251ba986eb875a34a6f9674a2046d60 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.1 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 Deep learning-based palm tree detection in unmanned aerial vehicle imagery with Mask R-CNN Subject The topic of the resource Deep learning Mask region-based convolutional neural network Palm tree Tree detection Unmanned aerial vehicle Description An account of the resource Oil palm is highly valuable in tropical regions like Southeast Asia, including Indonesia. Therefore, accurate monitoring of oil palm trees is necessary for operational efficiency and reducing its environmental impact. Geospatial data, such as orthomosaic imagery from the unmanned aerial vehicle (UAV), can facilitate this goal. This research aims to integrate UAV data with deep learning algorithms, specifically Mask region-based convolutional neural network (R-CNN), to detect oil palm trees in Indonesia. We utilized Resnet-50 as the backbone and trained the model using data sampled from the template matching tool in eCognition. Considering factors like cloud shadows and other features, such as other plants, buildings, and road segments, we divided the study area into three containing different feature combinations in each. The Mask R-CNN model achieved an accuracy exceeding 80%, which is sufficient and makes it suitable for large-scale oil palm tree detection using high resolution images from UAV. Creator An entity primarily responsible for making the resource Agung Syetiawan1, Danang Budi Susetyo1,4, Yustisi Lumban-Gaol1, Susilo1, Mohammad Ardha1, Yunus Susilo2, Wahono3 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 Nov 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 Deep learning Mask region-based convolutional neural network Palm tree Tree detection Unmanned aerial vehicle https://repository.horizon.ac.id/files/original/63722e684a0dc5d9b8c75503110f2b7b.pdf d55accc6df9a0351c88d22ed9d5999f8 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.1 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 Designing a marketplace system to assist incubators in higher education in fostering technopreneurship Subject The topic of the resource Business incubator Enterprise architecture Higher education Marketplace The open group architecture framework Description An account of the resource Technology-based startups play a vital role in boosting the Indonesian economy. However, many face sustainability challenges due to misalignment with market needs and a lack of comprehensive understanding of business processes. This condition demands the application of academic theories related to increasing productivity and highlights the need for innovative solutions. Universities can significantly support the development of these startups by leveraging business incubators to enhance competitiveness in the digital era. This research aims to propose a marketplace system architecture incorporating a business incubator unit (BIU), integrating scientific knowledge with societal business interests and talents in a university. The system is developed by the enterprise architecture (EA) methodology with the the open group architecture framework (TOGAF). This approach ensures a systematic design process that aligns with best practices. This study results in several strategic artefacts that provide guidance for creating a practical environment for entrepreneurs and translating theoretical insights into actionable business strategies. The artefacts can be rigorously tested and applied in real-world business incubator settings. The proposed system addresses critical challenges startup businesses face, such as enhancing business understanding and providing a fair foundation before entering the competitive market. Creator An entity primarily responsible for making the resource Cepy Slamet1, Ichsan Budiman1, Muhammad Insan Al-Amin2, Muhammad Ali Ramdhani1, Dudang Gojali3 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 Oct 18, 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 Business incubator Enterprise architecture Higher education Marketplace The open group architecture framework https://repository.horizon.ac.id/files/original/0ab469005fde693cfa5c0d1f71fd2ee5.pdf e091b876380f44e80011a668253c22ac 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.1 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 Designing common-source low noise amplifier utilizing GaN HEMT for sub-6 GHz in 5G wireless applications Subject The topic of the resource Advanced design system Fifth generation Gain Gallium nitride High electron mobility transistor Low noise amplifier Noise figure Description An account of the resource In the domain of gallium nitride based high electron mobility transistors (GaN HEMT), this work refines a class A low noise amplifier (LNA) tailored for fifth generation (5G) wireless applications within the sub-6 GHz band. Employing a common-source topology and leveraging GaN HEMT technology, the amplifier seamlessly achieves operation at 3.5 GHz. Simulations were conducted using Advanced Design System (ADS) software. The GaN HEMT transistor manifests noteworthy intrinsic and extrinsic characteristics, with a Vds of 6 V, Vgs of -1.56 V, and Id of 1024 mA. Through meticulous optimization within the [3.3-3.9] GHz frequency band, the GaN HEMT transistor attains an impressive maximum gain of 16.225 dB, coupled with a minimal low noise figure (NF) of 1.232 dB. Additionally, the amplifier showcases noteworthy power added efficiency (PAE) of approximately 60.527%. These exceptional attributes position the amplifier as highly suitable for sub-6 GHz and millimeter-wave applications across the extensive 5G spectrum. The investigation is centered on precisely situating the LNA as a pivotal catalyst for improving 5G network front-end performance. With a dedicated focus on frequencies below 6 GHz, the research not only addresses challenges but also pioneers’ advancements in 5G application LNA design, ultimately elevating the overall system performance. Creator An entity primarily responsible for making the resource Samia Zarrik, Abdelhak Bendali, Fatehi ALtalqi, Karima Benkhadda, Sanae Habibi, Zahra Sahel, Mouad El Kobbi, Abdelkader Hadjoudja, Mohamed Habibi 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 Nov 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