1 10 28 https://repository.horizon.ac.id/files/original/d01e8115f68c8eea347fb739aae1b279.pdf 3f9c2ecbc4169d49a972ab15b83cb505 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.6 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 IR4.0 and internet of things: future directions towards enhanced connectivity, automation, and sustainable innovation Subject The topic of the resource Artificial intelligence Fourth industrial revolution Industry four Industry revolution Internet of things Smart city Description An account of the resource This study provides a systematic review of the literature on the fourth industrial revolution (IR4.0) and the internet of things (IoT) in genetics, informatics, and biotechnology, as well as their many rapidly evolving applications, which often overlap with various aspects of life. The study reviews the latest research, books, scientific theses, and official websites in this field, placing them in a coherent context for researchers. It covers innovations that have driven the continuous development of IR4.0 and its challenges. The study highlights numerous fields and modern technologies expected to bring about tangible and radical changes in health, agriculture, and industry. It addresses key aspects of IR4.0 and the significant advancements brought by smart cities, and most importantly, artificial intelligence (AI), including robots, autonomous cars, 3D printing, big data, IoT, nanotechnology, biotechnology, energy storage, and quantum computing. By applying cutting-edge technology across various disciplines to boost productivity and foster development, technology plays a crucial role in linking the physical, digital, and biological realms. This revolution is built on multiple axes, including the convergence of biotechnology, digital media, and physical systems. Creator An entity primarily responsible for making the resource Yousef A. Baker El-Ebiary1, Salameh A. Mjlae2, Hadeel Ahmad3, Suzie Y. Rababa’h4, Mahmoud A. Rababah5, Omar G. Arabiat6 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 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 Artificial intelligence Fourth industrial revolution Industry four Industry revolution Internet of things Smart city https://repository.horizon.ac.id/files/original/ec13d45cffb06e52bd9b21349c635cac.pdf 0c0cf1efd4235b1c0602ca096d0bf38e 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.6 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 Tomato leaf disease recognition system using Faster R-CNN Subject The topic of the resource Detectron2 Faster region-based convolutional neural network Machine learning Object detection Tomato leaf disease Description An account of the resource The objective of this paper is to detect tomato leaf disease using Faster region-based convolutional neural network (R-CNN). The tomato leaf disease recognition system utilizes a dataset consisting of healthy tomato leaves and eight leaf diseases, including early blight, late blight, leaf mold, mosaic virus, septoria, spider mites, yellow leaf curl virus, and leaf miner. The dataset is obtained from various sources, such as Kaggle, Google Images, Bing Images, and Roboflow Universe. Pre-processing techniques, including collage, tile, static crop, and resize, are applied to prepare the dataset for training. Data augmentation methods, such as flipping, 90° rotation, exposure adjustment, and hue modification, are applied to enhance the model’s robustness and generalize its performance. Specifically, we implemented Faster R-CNN as part of Detectron2 using its base models and configurations. The results demonstrate that the X101-FPN base model for Faster R-CNN with the default configurations of Detectron2 is efficient and general enough to be applied to defect detection. This approach results in an average precision (AP) detection score of 87.01% for validation results. Creator An entity primarily responsible for making the resource Karel Octavianus Bachri, Bryan Santoso, Duma Kristina Yanti Hutapea, Catherine Olivia Sereati, Lanny W. Pandjaitan 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 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 Detectron2 Faster region-based convolutional neural network Machine learning Object detection Tomato leaf disease https://repository.horizon.ac.id/files/original/8062b56d190daca89fc018868254d31e.pdf aa465e31eef2bfb72aac97744b27483d 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.6 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 Gilbert cell down-conversion mixer for THz wireless communication with passive baluns Subject The topic of the resource 6G Active down-converter Balun Gilbert mixer PH15NHF Description An account of the resource This article presents the design of an active down-conversion mixer for the superheterodyne receiver system for 6G wireless communications. Creator An entity primarily responsible for making the resource Abdeladim El Krouk1, Abdelhafid Es-Saqy2, Mohammed Fattah1, Said Mazer2, Mahmoud Mehdi3, Moulhime El Bekkali2, Catherine Algani4 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 6, 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/c19df6e4efb88904403a6e10d2a3adf0.pdf d80c5325c4719de6c9b05e4292ceba87 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.6 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 Light weight dual polarized horn antenna for polarimetry C-band synthetic aperture radar sensor onboard UAV Subject The topic of the resource Circular polarized Dual polarized Horn antenna Synthetic aperture radar Unmanned aerial vehicle Description An account of the resource Unmanned aerial vehicles (UAVs) can collect earth surface information with synthetic aperture radar (SAR) sensors during night, day, and bad weather. However, UAVs have only limited volume and weight for the SAR payload. A very light dual circularly polarized pyramid horn antenna was developed in order to improve sense capability in UAV dual polarimetry SAR images. The pyramid horn was designed and simulated in computer simulation technology (CST) 2022. The horn was capable of producing two circular polarizations right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP) alternately using a three stepped septum polarizer and coaxial probe feed on two sides of the waveguide. The results show that the horn antenna was capable of having a wide S11 and S22 bandwidth from 4 GHz to 6.7 GHz. The horn was capable of producing two circular polarizations with an axial ratio below 3 dB from 4.4 GHz to 6.4 GHz. This gain horn antenna was relatively stable in the range of 15 dBiC with a reasonably narrow 3 dB beamwidth of 28° both RHCP and LHCP polarisation in E and H fields respectively. Creator An entity primarily responsible for making the resource Agus Hendra Wahyudi1, Farohaji Kurniawan1, Bambang Setiadi2, Agus Wiyono1, Satria Arief Aditya1, Wahyu Widada3, Efendi Dodi Arisandi4 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/995bb2e781c7e85b568361d959b992f3.pdf 26aac4964a04e125ba36df2f56d40b4d 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.6 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 Reducing feature dimensionality for cloud image classification using local binary patterns descriptor Subject The topic of the resource Cloud images Feature ranking Feature selection Local binary patterns Support vector machine Description An account of the resource Clouds play a crucial role in precipitation and weather prediction. Identifying and differentiating clouds accurately poses a significant challenge. In this paper, we present a novel approach that utilizes the local binary patterns (LBP) feature descriptor to extract color cloud images. We employ feature fusion to combine LBP features from the independent channels of the RGB color space. Furthermore, we apply five well-known feature selection methods, namely ReliefF, Ilfs, correlation-based feature selection (CFS), Fisher, and Lasso, to select relevant and useful features. These selected features are then fed into a support vector machine (SVM) classifier. Experimental results demonstrate that our proposed approach achieves superior performance by significantly reducing the number of features while maintaining prediction accuracy. Creator An entity primarily responsible for making the resource Thongchai Surinwarangkoon1, Vinh Truong Hoang2, Kittikhun Meethongjan3 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 6, 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 images Feature ranking Feature selection Local binary patterns Support vector machine https://repository.horizon.ac.id/files/original/3b71cf0ad07f0ae7e786efc4e9491804.pdf d79d76b488a5b1ca3e44944b0f6d1ff7 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.6 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 Development of an IoT based smart potato leaf diseases monitoring and controlling system with image processing Subject The topic of the resource Image processing Internet of things Leaf diseases Real-time monitoring ResNet-50 User interface Description An account of the resource Potato (Solanum tuberosum L.) is a key crop and a major source of livelihood for a vast population of the world after wheat and rice. However, diseases have diverse effects on potato fields, leading to damage to crops and reducing crop production. The two major diseases that afflict potato plants are referred to as early blight and late blight. Protection of this crop yield from blight diseases is one of the foremost challenges. Therefore, detection of these leaf diseases at appropriate time is very essential to prevent the damage. This study intends an internet of things (IoT)-based smart potato leaf diseases monitoring and control system that combines IoT technology with eco-sensing and image processing to identify and categorize these diseases. Studies have found that blight diseases are directly related to temperature and humidity of the planted area. This study measures environmental information using a sensor network installed in the planted area. After sensing and measuring environmental information, acquired values are displayed and farmers get these acquired values via message notification. The system obtains a 97% accuracy rate in recognizing these diseases by using our fine-tuned model of ResNet-50 for image processing. Creator An entity primarily responsible for making the resource Munira Akter Lata1, Tania Khatun2, Marjia Sultana3, Iffat Ara Badhan4, Tasniya Ahmed5, Md. Rakib Hasan6 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 Image processing Internet of things Leaf diseases Real-time monitoring ResNet-50 User interface https://repository.horizon.ac.id/files/original/cde4551abd003dce5a80392d50860325.pdf e2bbda297757f8152c973408d2751fd6 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.6 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 Sentiment analysis of public response to measurable fishing capture policy using LDA and LSTM methods Subject The topic of the resource Blue economy Illegal, unreported, and unregulated fishing Latent Dirichlet allocation Long short-term memory Measured fishing Description An account of the resource Illegal, unreported, and unregulated (IUU) fishing poses a significant threat by depleting fish stocks, damaging marine ecosystems, jeopardizing economic livelihoods, and undermining long-term environmental sustainability. To address this, the government has implemented a public policy of measured fishing within the blue economy framework. Given the involvement of numerous stakeholders, it is crucial for the government to gauge public sentiment through tweets on social media platforms to evaluate and refine the policy’s implementation for greater effectiveness. While the long short-term memory (LSTM) method for sentiment analysis is adept at handling text sequences and context, it struggles with capturing contextual semantic correlations. Conversely, the latent Dirichlet allocation (LDA) method excels in identifying these correlations and uncovering dominant topics. This study shows that integrating LDA for topic modeling with LSTM for sentiment analysis enhances overall performance, providing more accurate and comprehensive insights into public responses and identifying key topics discussed in social media tweets. Creator An entity primarily responsible for making the resource Januar Agung Wicaksono1, Retno Kusumaningrum2, Eko Sediyono3 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 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 Blue economy Illegal, unreported, and unregulated fishing Latent Dirichlet allocation Long short-term memory Measured fishing https://repository.horizon.ac.id/files/original/1a8f1dcee96dc046730da7b02062f38d.pdf 0e14b837ccd92aa78e1c449557fb0674 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.6 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 Research on intelligent river water quality management system using blockchain-internet of things Subject The topic of the resource Blockchain-internet of things Data transparency Decentralized data-management Real-time monitoring Water Water quality Description An account of the resource Each and every living thing needs fresh water to survive. Fresh water is becoming a precious resource as a result of the combined risks of rapid urbanization, pollution, and climatic changes. The quality of the water we drink every day has an impact on our lives, either directly or indirectly. Maintaining the sustainability and health of the environment requires constant attention to water quality. Modern technology makes it possible to gather and analyze data from water distribution networks in order to maximize resources and enhance decision-making for all parties. Despite the enormous global growth of the internet of things (IoT) and blockchain in recent years, their integration is still in its early stages. In this research contains a technological framework that combines blockchain and IoT called B-IoT and aims to reward and incentivize more sustainable water quality management with real-time monitoring and security. IoT is used to monitor water quality in water resources and find any violations. By using blockchain, it is possible to retain the accuracy, reliability, and transparency of the records of breaches. This system will be able to gauge the water’s quality in real-time and allow for the quick identification of any infractions necessary to commit the crime. Creator An entity primarily responsible for making the resource Anitha Roy1,2, Jubilant J Kizhakkethottam3 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 Aug 30, 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 Blockchain-internet of things Data transparency Decentralized data-management Real-time monitoring Water Water quality https://repository.horizon.ac.id/files/original/6247da992f9832b9bc928f0ae6962927.pdf 5039b604175cb09d58b1d59a9d4c2156 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.6 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 Compact miniaturized antenna design and development for a leadless cardiac pacemaker Subject The topic of the resource Biotelemetry Miniaturized antenna Pacemaker Specific absorption rate Ultra-wideband Description An account of the resource Biomedical antennas play a crucial role in implantable medical telemetry, improving patient monitoring and overall well-being. This study presents a compact antenna designed for a lead-free pacemaker, with dimensions of 3.3×4×0.5 mm³. Miniaturization required open cuts in the ground plane and radiating patch, as well as a localized resistance element. The antenna features an exceptional bandwidth of 1646.8 MHz (1.0602-2.7070) GHz, guaranteeing constant performance in the human body. It covers the industrial, scientific and medical bands (ISM, 2.4-2.48 GHz) and ultra-high frequencies (UHF, 0.3-13 GHz). Simulation in a phantom produced a gain of -19.78 dBi at 2.45 GHz and -34.44 dBi at 1.2 GHz. Safety was confirmed by a specific absorption rate (SAR) study using a cardiac model. The antenna’s low SAR (10 g-Avg) enabled maximum input powers to be established: 20.2 mW at 1.2 GHz and 21.23 mW at 2.45 GHz. Comparative simulations using high-frequency structure simulator (HFSS) and computer simulation technology (CST) highlighted the antenna’s superiority over recent systems, demonstrating its effectiveness in the human heart. This antenna represents an advanced solution for improving patient care. Consequently, this antenna emerges as an advanced solution for addressing infectious diseases and cardiovascular conditions in the realm of medical advancements. Creator An entity primarily responsible for making the resource Abdelmouttalib Bousrout1, Asma Khabba2, Saida Ibnyaich2, Tomader Mazri1, Mohamed Habibi3, Tole Sutikno4 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 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 Biotelemetry Miniaturized antenna Pacemaker Specific absorption rate Ultra-wideband https://repository.horizon.ac.id/files/original/86a53e70c8fa16d83a2bdf717fa2b4f0.pdf ef69977159adf5f1d328807ee21ff4de 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.6 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-step constant current-constant voltage charging method to improve CC-CV method on lead acid batteries Subject The topic of the resource Charging strategy Constant current-constant voltage Lead acid battery Multi-step constant current-constant voltage Overcharge protection Description An account of the resource Constant current-constant voltage (CC-CV) is one of the battery charging methods that is commonly used. However, this method has several drawbacks, including the charging current in constant current (CC) mode, which can only be set to a maximum of 0.3 C on lead acid batteries, resulting in a relatively long charging duration. Therefore, in this research, the multi-step constant current-constant voltage (MCC-CV) method of battery charging system is developed where this method can use a greater charging current, resulting in a significant reduction in charging duration by using multiple current setpoints in MCC mode, with the initial setpoint current can be set beyond 0.3 C, which is 0.34 C in this system. This system uses a DC-DC single-ended primary inductance converter (SEPIC) converter as a battery charging control system, equipped with a power cut-off relay when the charging current reaches 0.05 C in constant voltage (CV) mode. From the test results obtained, the MCC-CV method can charge the battery to its full capacity faster than the CC-CV method with a difference of 15.34 minutes and the relay on the system can work properly. Creator An entity primarily responsible for making the resource Mochamad Ari Bagus Nugroho, Ahmad Dzihan Alifi, Suhariningsih, Epyk Sunarno, Eka Prasetyono, Dimas Okky Anggriawan 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 Aug 30, 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 Charging strategy Constant current-constant voltage Lead acid battery Multi-step constant current-constant voltage Overcharge protection