2 10 26 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/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 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/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/5f5a726b67a5a44c9f795e350d4b5484.pdf e913e867d2fdca45024d8f95909ee848 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 Rainfall prediction using support vector regression in Udupi region Karnataka, India Subject The topic of the resource Central ground water board Hyperparameters Prediction accuracy Root mean squared error Support vector regression Description An account of the resource The hydromatereological processes are examined through analysis of temporal rainfall variability. India is an agricultural land and its economy is mainly dependent on timely rains to produce good harvest. The amount of rainfall varies with regional and temporal variation in distribution. The present research has been conducted to predict the temporal variations in rainfall in Udupi district, Karnataka, India using support vector regression (SVR) model and to validate the findings using actual rainfall records. The data has been collected from the statistical department, Udupi district, Government of Karnataka, India. The prediction accuracy of SVR based rainfall prediction model depends on tuning of algorithmic-based parameters. The parameter optimization is performed using grid search to select the optimal values of hyperparameters. The analysis was performed for the year 2018 based on the training dataset from 2000-2017. It is observed that there is a decreasing trend in total annual rainfall in 2018 and it is concluded that the average yearly rainfall has declined during the years 2018 and 2019. The rainfall predicted results were validated with actual records. The SVR based rainfall prediction model will predicts the rainfall accurately for application in agricultural sector. Creator An entity primarily responsible for making the resource Krishnamurthy Nayak1, Sumukha K Nayak2, Supreetha Balavalikar Shivarama1 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 Central ground water board Hyperparameters Prediction accuracy Root mean squared error Support vector regression https://repository.horizon.ac.id/files/original/b8c8f55af849f47e54a3ebeec0ff1a29.pdf 7c5313c8e5bbf64f97930b261e57e99a 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 PV solar anomaly detection using low-cost data logger and ANN algorithm Subject The topic of the resource Artificial intelligence Edge device Energy management system Energy monitoring Predictive maintenance Renewable energy Solar energy Description An account of the resource This paper presents an innovative edge device architecture that significantly enhances solar energy management systems. By integrating advanced functionalities such as generation prediction, maintenance alerts, and solar anomaly detection, this architecture transforms solar energy management. Through edge computing, it enables real-time analysis and decision-making at the network edge. Leveraging machine learning algorithms and accurate predictive models, these edge devices provide precise energy generation forecasts, facilitating optimal energy utilization and strategic planning for stakeholders. Additionally, the architecture incorporates anomaly detection techniques to proactively identify deviations from normal operation, minimizing downtime, and enabling timely maintenance. This approach ensures uninterrupted energy generation, enhancing the reliability and efficiency of the entire monitoring system. The integration of these features within edge devices improves the performance and reliability of energy monitoring systems. Implementing this cutting-edge architecture empowers stakeholders to achieve superior energy management, substantial cost reductions, and unparalleled system reliability. Creator An entity primarily responsible for making the resource Younes Ledmaoui1, Adila El Maghraoui2, Mohamed El Aroussi1, Rachid Saadane1, Abdellah Chehri3, Ahmed Chebak2 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 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 Artificial intelligence Edge device Energy management system Energy monitoring Predictive maintenance Renewable energy Solar energy 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/9b8185484aae247db2346a73e34551b0.pdf 0d6a8728ed2bc36f1c6344e94a5ccc0b 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 The bending effects on the performance of a flexible circular microstrip antenna on rubber-carbon substrates at 2.45 GHz Subject The topic of the resource Bending effects Flexible microstrip antenna Industrial, scientific and medical band Rubber-carbon substrate Wearable antenna Description An account of the resource In this paper, we studied the effects of bending on the performance of a flexible circular microstrip antenna on rubber-carbon substrates at the 2.45 GHz industrial, scientific, and medical (ISM) band. Several rubber compositions are analyzed which include natural rubber (no carbon filler), rubber with 20% carbon filler, rubber with 25% carbon filler, and rubber with 50% carbon filler. Four types of bending directions are applied in this work i.e., side-inward, side-outward, top-inward, and top-outward with bending radius ranging from 100-500 mm. It is observed that even though the resonant frequency of the antenna shifted a bit when bending is applied, the S11 at the intended frequency remains below -10 dB. The bandwidth and gain also maintain an acceptable performance despite all the directions and radius of the bending, due to the wide bandwidth characteristics of the antenna. With these results, the proposed antenna is shown to be usable for wearable applications at 2.45 GHz, in both flat and bent conditions. We also show that it is essential to design a flexible antenna with a wide bandwidth to guarantee that the antenna maintains optimal performance even under curved conditions. Creator An entity primarily responsible for making the resource Ahmad Alhadi Ruslan1,2, Sarah Yasmin Mohamad1,2, Norun Farihah Abdul Malek1,2, Siti Hajar Yusoff1, Siti Noorjannah Ibrahim1 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 Bending effects Flexible microstrip antenna Industrial, scientific and medical band Rubber-carbon substrate Wearable antenna https://repository.horizon.ac.id/files/original/1bb6d2d6a37d70c6e87de0755d18d499.pdf b2e4f8ebe5c680b07772c614a710628d 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 Electroencephalography-based wheelchair navigation control using convolutional neural network method Subject The topic of the resource Classification Convolutional neural network Deep learning Electroencephalograph Wheelchair Description An account of the resource Artificial intelligence refers to a computer-based system capable of learning human activities. For instance, in medical technology, AI can be used for a thought-controlled wheelchair. This study discusses the use of deep learning, specifically convolutional neural network (CNN), in predictiong of the user intention to navigate a wheelchair. The training data was collected from an EEG sensor and included the wheelchair’s movements - turning right, turning left, moving forward, moving backward, and idle. The signals were then sampled and feature-extracted using root mean square (RMS). In CNN classification, both raw and RMS data were used. This study compared two different CNN architectures. The first architecture has three convolutional layers and three pooling layers, while the second has two of each. The research compares the accuracy and loss values of CNN predictions using architecture 1 and 2 on both raw and RMS data. The experimental results indicate that when using raw data, the first CNN architecture achieved an accuracy of 85.12%, and the second model achieved 91.04%. However, when using RMS data, the first architecture achieved an accuracy of 76.47%, and the second achieved 73.74%. The study concludes that the movement of the wheelchair is better in real-time when using raw data compared to using RMS data. Creator An entity primarily responsible for making the resource Khairul Anam1,2, Satrio Marta Wicaksono1,2, Muchamad Arif Hana Sasono1,2, Bima Wahyu Maulana1,2, Fatkhul Mubarok1,2, Ananta Pinsentius Rahmat Pamungkas1,2, Moch. Rijal Fatoni1,2 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 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 Classification Convolutional neural network Deep learning Electroencephalograph Wheelchair