3 10 26 https://repository.horizon.ac.id/files/original/344a7bfd943557759640109c03b27d5e.pdf 3fe737eee4dd334c1c50c5fa70d24cf3 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.6 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 Automatic diagnosis of rice plant diseases using VGG-16 and computer vision Subject The topic of the resource Automatic diagnosis Computer vision Optimization algorithm Rice plant disease Visual geometry group-16 Description An account of the resource Pathogens are organisms that cause disease in plants. In the case of rice, these pathogens can include fungi, bacteria, nematodes, protozoa, and viruses. This study aims to investigate rice plant diseases using a hybrid system that employs the visual geometry group-16 (VGG-16) architecture and computer vision techniques, alongside various optimization algorithms and hyperparameters. We utilize the convolutional neural network (CNN) architecture of VGG-16 for feature extraction, implementing a process known as transfer learning. Additionally, this research compares different optimization algorithms with the VGG-16 model to identify the most effective optimization for the CNN architecture applied to the tested dataset. The main contribution of this study is the development of a model for identifying rice plant diseases based on data collected using VGG-16 for feature extraction and neural networks for classification with specific parameters. Our findings indicate that the best optimization algorithm is stochastic gradient descent (SGD) with momentum, achieving training and validation loss results of 0.173 and 0.168, respectively. Furthermore, the training and validation accuracies were 0.95 and 0.957. The model’s performance metrics include an accuracy of 95.75, precision of 95.75, recall of 95.75, and an F1-score of 95.73. Creator An entity primarily responsible for making the resource Al-Bahra1, Henderi2, Nur Azizah2, Muhammad Hudzaifah Nasrullah3, Didik Setiyadi4 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 Oct 19, 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 Automatic diagnosis Computer vision Optimization algorithm Rice plant disease Visual geometry group-16 https://repository.horizon.ac.id/files/original/9d35d14a1596112cd6bcea87ea123d5f.pdf 76f02aabae9a62161ddc1b110ec95238 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.6 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 Optimizing vehicle inspection efficiency and integrity in Tanzania through blockchain technology Subject The topic of the resource Blockchain Data integrity Hyperledger fabric InterPlanetary file system Road safety Vehicle inspection Description An account of the resource This study proposes a blockchain-based solution to improve the efficiency and integrity of vehicle inspections in Tanzania, with a focus on the National Institute of Transport. The system combines Hyperledger fabric, a permissioned blockchain that provides identity management and fine-grained access control, with the InterPlanetary file system (IPFS), a decentralized content-addressed store for large artifacts such as inspection images and portable document format (PDF) forms. Smart contracts encode inspection rules and approvals, which yield tamper-evident records, faster retrieval of histories, and uniform enforcement across centers. A mathematical model based on the M/M/1 queueing system, combined with a cost-benefit analysis, supports empirical findings: the total inspection cycle time decreases by approximately 30 percent, the average waiting time declines by about 20 to 30 percent, and annual operational savings reach approximately USD 800,000. These gains enhance auditability and transparency, which contribute to road safety outcomes by reducing opportunities for tampering and error. The design includes offline capture with later synchronization, which suits centers with intermittent connectivity. The approach is transferable to adjacent public services, for example, licensing, fine collection, and selected registries. Creator An entity primarily responsible for making the resource Cleverence Kombe1, Robert Sikumbili2, Leticia Mihayo2, Angela-Aida Runyoro2 1Department of 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 Oct 19, 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 Blockchain Data integrity Hyperledger fabric InterPlanetary file system Road safety Vehicle inspection https://repository.horizon.ac.id/files/original/7cc16c6bec82882d2314c47ad9e085cc.pdf f6e99774705e1294a700f4ff3142ca6c 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.6 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 Analyzing temporal properties of speech trajectory using graph structures towards speech recognition Subject The topic of the resource Graph eigenvalues Graph signal processing Speech analysis Structural processing Speech trajectory Description An account of the resource Speech signal analysis aims to identify patterns within data to develop effective recognition algorithms. This process primarily utilizes feature extraction techniques such as linear predictive coding (LPC), linear predictive cepstral coefficients (LPCCs), and Mel-frequency cepstral coefficients (MFCCs). These features are crucial for constructing recognition algorithms that leverage both statistical and deep learning methods. While deep learning models require extensive datasets, they often prove unsuitable for low-resource languages. The Hidden Markov model (HMM) is the most widely adopted statistical framework in speech processing. However, HMMs are characterized by state-dependent models, where each state interacts only with its neighboring states. This limitation restricts HMMs from capturing long-term signal properties, highlighting the need for addressing these constraints at the feature extraction stage. Most feature extraction methods rely on short-term signal processing, which further limits the comprehension of speech utterances. To overcome these limitations, alternative methods are necessary to capture more comprehensive patterns. This paper presents a graph-based approach for analyzing speech trajectories and their temporal properties, which are subsequently validated using HMMs in speech recognition tasks. Graph-based representations on a low-resource Telugu dataset improve recognition accuracy by 13% while reducing processing time compared to traditional LPC. Creator An entity primarily responsible for making the resource Parabattina Bhagath1, Malempati Shanmukha2, Gnana Nagasri Puthi2 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 Sep 10, 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 Graph eigenvalues Graph signal processing Speech analysis Structural processing Speech trajectory https://repository.horizon.ac.id/files/original/41fd04f20c14428eeea7110f015ba3c0.pdf 3579df79eb21e5796e7e7c669f5bd6a4 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.6 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 Performance enhancement of PV generator using a sensor based dual axis solar tracking system in Algeria Subject The topic of the resource Electric actuator Light dependent resistor sensor Photovoltaic system Renewable energy Solar tracking system Description An account of the resource This article presents the implementation of a two-axis solar tracking system and its impacts to increase the performance of the photovoltaic system in northern Algeria. The system enhances the efficiency of solar systems by optimizing their exposure to sunlight making the sunbeam perpendicular to solar panel. The main objective of the study is to develop a technically proficient and economically viable solution to increase solar energy production. The design relies on integrating light sensors and motors controlled by an Arduino board, enabling automatic adjustment of solar panel positions. This approach offers dynamic and precise orientation, based on light dependent resistor (LDR) sensor design and threshold value, resulting in a significant increase in energy output. The results show that the dual-axis solar tracking system can capture 60.64% more solar energy, taking into account the power consumption of the two electric actuators. The findings of this study will positively influence the promotion of clean and sustainable energy sources while providing a practical solution for more efficient utilization of solar energy in Algeria. Creator An entity primarily responsible for making the resource Sakina Atoui1, Harb Hadjer2, Belaïd Abdelghani2 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 Oct 19, 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 Electric actuator Light dependent resistor sensor Photovoltaic system Renewable energy Solar tracking system https://repository.horizon.ac.id/files/original/6b9c8ffaaa11cf2c68a24da8cdd0e173.pdf d4c2faed33fa2c565a60cdff9763a30e 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.6 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 Advanced signal transformation techniques to improve spectral efficiency in visible light communication systems Subject The topic of the resource Complex signal mapping Hermitian symmetry Light emitting diodes Quad-light emitting diode complex modulation Visible light communication Description An account of the resource Visible light communication (VLC) offers high-speed wireless communication using the visible light spectrum. Achieving high spectral efficiency while maintaining a low bit error rate (BER) remains a challenge. This paper explores the use of quadrature amplitude modulation (QAM) combined with orthogonal frequency division multiplexing (OFDM) to address these challenges. Matrix laboratory (MATLAB) simulations show that QAM-OFDM achieves a BER of 0.001 at comparable signal-to-noise ratios (SNR), outperforming traditional hermitian symmetry (HS), complex signal mapping (CSM), and quad-light emitting diode (LED) complex modulation (QCM) techniques. Unlike CSM, and QCM, which increase complexity, and BER, QAM-OFDM efficiently utilizes available bandwidth, reducing errors, and enhancing spectral efficiency. The study concludes, that QAM-OFDM happens to be the optimal solution for the future VLC systems, offering better performance within both efficiency, and reliability. Creator An entity primarily responsible for making the resource Shaher Fleyeh Nawaf1,2, Ammar Bouallegue1, Sameh Najeh3 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 Oct 19, 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 Complex signal mapping Hermitian symmetry Light emitting diodes Quad-light emitting diode complex modulation Visible light communication https://repository.horizon.ac.id/files/original/43f5c370ca269a2f044924b65b2c9f77.pdf 06f6548a2af5d9db946546c3f32688f3 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.6 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 Mixed attention mechanism on ResNet-DeepLabV3+ for paddy field segmentation Subject The topic of the resource Attention mechanism DeepLabV3+ Remote sensing Residual network Semantic segmentation Description An account of the resource Rice cultivation monitoring is crucial for Indonesia, where paddy field areas declined by 2.45% according to the Central Bureau of Statistics due to land function changes and shifting crop preferences. Regular monitoring of paddy field distribution is essential for understanding agricultural land utilization by farmers and landowners. Satellite imagery has become increasingly common for agricultural land observation, but traditional neural networks alone provide insufficient segmentation accuracy. This study proposes an enhanced deep learning architecture combining residual network (ResNet)-DeepLabV3+ with coordinate attention (CA) and spatial group-wise enhancement (SGE) modules. The attention mechanisms establish direct connections between context vectors and inputs, enabling the model to prioritize relevant spatial and spectral features for precise paddy field identification. The CA module enhances spectral feature discrimination, whereas the SGE improves spatial characteristic representation. The experimental results demonstrate superior performance over the baseline methods, achieving intersection over union (IoU) of 0.85, dice coefficient of 0.89, and accuracy of 0.95. The proposed mixed attention mechanism significantly improves the accuracy and efficiency of automatic crop area identification from satellite imagery. Creator An entity primarily responsible for making the resource Alya Khairunnisa Rizkita1, Masagus Muhammad Luthfi Ramadhan1, Yohanes Fridolin Hestrio1,2, Muhammad Hannan Hunafa1, Danang Surya Candra2, Wisnu Jatmiko1 Source A related resource from which the described resource is derived Journal homepage: https://telkomnika.uad.ac.id/index.php/TELKOMNIKA Date A point or period of time associated with an event in the lifecycle of the resource Sep 10, 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 Attention mechanism DeepLabV3+ Remote sensing Residual network Semantic segmentation