1 10 26 https://repository.horizon.ac.id/files/original/2f6713f692c13f87c3e5392f995f81d7.pdf b97576354b49ba673b06b81ce69d67d6 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 A hybrid ARIMA and DNN approach with residual learning for electric vehicle charging demand forecasting Subject The topic of the resource Autoregressive integrated moving average Charging demand forecasting Deep neural network Electric vehicle Hybrid model Residual learning Description An account of the resource The rapid growth of electric vehicle (EV) adoption has created significant challenges for power grid management and charging infrastructure planning. Accurate forecasting of EV charging demand is therefore essential to ensure reliable electricity supply and effective station deployment. This study proposes a novel hybrid forecasting framework that combines autoregressive integrated moving average (ARIMA) with deep neural networks (DNN) through a residual learning strategy. In this approach, ARIMA models the linear temporal patterns, while DNN captures the nonlinear residuals, resulting in improved efficiency and predictive accuracy. The proposed hybrid model is one of the first applications of the residual learning approach for EV demand forecasting in Indonesia. Experimental evaluation using real-world daily consumption data shows that the hybrid method achieved the highest prediction accuracy of 98.22%, consistently outperforming single-model baselines. Beyond technical performance, the model can support stakeholders in planning charging infrastructure and help maintain grid stability in rapidly growing EV ecosystems. Creator An entity primarily responsible for making the resource Wahyu Cesar, Dwidharma Priyasta, Prasetyo Aji, Melyana, Agus Suprianto, Osen Fili Nami, Riza 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 Autoregressive integrated moving average Charging demand forecasting Deep neural network Electric vehicle Hybrid model Residual learning https://repository.horizon.ac.id/files/original/3acb868c0a0ae4c0995a098d5e539e7e.pdf eadeea477fa4ad757981234736c0f33a 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 Adaptive DICOM images encryption using quadtree and lightweight ITUBee algorithm Subject The topic of the resource DICOM image Henon map ITUBee Lightweight encryption Medical image Quadtree Description An account of the resource The encryption of medical images protects the privacy of patient information transmitted over networks and communications. In this paper, a lightweight encryption method for medical images is proposed, combining a quadtree-based segmentation and a modified ITUBee algorithm for encryption. A digital imaging and communications in medicine (DICOM) image is divided into variable-size blocks using the quadtree technique, and the key is generated through a two-dimensional Henon map; the first dimension is used in the confusion process (bit permutation) of the pixel values, and the second sequence is used to generate the key schedule through the application round function. Different numbers of rounds are applied to the ITUBee method based on the size of the segments in the quadtree, making the algorithm adaptive by increasing the round number when the block size is reduced. The method is used as a lightweight encryption method for encrypting all blocks, utilizing different round numbers for each block size to balance the degree of complexity with the total time consumption of the DICOM image. The result reinforces the proposed method, which produced a high mean squared error (MSE) between the DICOM image and the Encrypted One, and a lower peak signal-to-noise ratio (PSNR). The proposed generated numbers were also tested using national institute of standards and technology (NIST) to evaluate the randomness. Creator An entity primarily responsible for making the resource Muntaha Abdulzahra Hatem1, Balsam Abdulkadhim Hameedi2, Jamal Nasir Hasoon3, Fahad Ghalib Abdulkadhum4 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 DICOM image Henon map ITUBee Lightweight encryption Medical image Quadtree 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/5cb01a20c034fe765ca575f91b71d6f9.pdf fa69d74c19df8f8146cee959fecb2e06 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 An insight on using deep learning algorithm in diagnosing gastritis Subject The topic of the resource Deep learning Diagnosis system Gastritis detection GoogleNet ResNet TResNet VGGNet Description An account of the resource Chronic autoimmune gastritis (CAG) is a condition in which the stomach membrane is significantly impacted by inflammation. Despite the availability of numerous modern medical techniques, the detection of this condition continues to be a difficult challenge. White light endoscopy (WLE) has been employed to diagnose gastritis, but it has been subject to certain constraints. This technique is most effective when executed by an endoscopist who possesses a high level of expertise. In the present day, WLE is frequently accompanied by artificial intelligence (AI) due to its superior ability to detect defects that lead to damage. Recently, there has been a substantial increase in the efficacy of AI in conjunction with the expertise of endoscopists in the detection of CAG. The 25,216 intriguing case studies were examined in the eight selected studies. The collection comprised 84,678 frames and 10,937 images. The AI was 94% sensitive (95% CI: 0.88-0.97, I2 = 96.2%) and 96% specific (95% CI: 0.88-0.98, I2 = 98.04%). The receiver operating characteristic curve had an area of 0.98 (95% confidence interval: 0.96–0.99). A camera is highly effective when combined with AI to assist in the identification of CAG and is advantageous for clinical review. Creator An entity primarily responsible for making the resource Ragu P. J.1, Ashok Vajravelu1, Muhammad Mahadi bin Abdul Jamil1, Syed Riyaz Ahammed2 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 Deep learning Diagnosis system Gastritis detection GoogleNet ResNet TResNet VGGNet 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/622ce51f897d53147c45c66687b31b16.pdf bfb39f5950f5369b36a1d30debaf00ea 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 Application of artificial intelligence in emission prediction for hybrid electric vehicles: integrating ANN and GPR Subject The topic of the resource Artificial neural networks Emission prediction Gaussian process regression Hybrid electric vehicles Prediction uncertainty Vehicle emissions Description An account of the resource In recent years, hybrid electric vehicles (HEVs) have emerged as a promising solution to mitigate vehicular emissions and improve fuel efficiency. This study focuses on the Toyota Prius HEV, employing advanced artificial neural networks (ANN) and Gaussian process regression (GPR) to develop a predictive model for vehicle emissions. The model considers multiple pollutants, including carbon monoxide (CO), carbon dioxide (CO₂), hydrocarbons (HC), and nitrogen oxides (NOx), measured under diverse driving conditions. The ANN model predicts emission trends, while GPR estimates prediction uncertainty, enhancing the model’s robustness. The GPR models achieved uncertainty levels of ±0.829 ppm for CO, ±9.978 ppm for HC, ±0.144 ppm for NOx, and ±411.256 ppm for CO₂, respectively, underscoring the robustness of the integrated approach for emission prediction. This research aims to support the development of more sustainable vehicle technologies and inform policy making for environmental sustainability (e.g., Euro 6/Euro 7 standards). Overall, the study addresses how artificial intelligence (AI) can be utilized to achieve accurate multi-pollutant emission predictions in HEVs. The findings reveal that an integrated ANN-GPR approach yields superior predictive performance (R² values approaching 1.0) with quantifiable uncertainty, outperforming a stand-alone ANN model and providing a robust solution to the emission prediction challenge. Creator An entity primarily responsible for making the resource Heru Priyanto1, Rizqon Fajar1, Yaaro Telaumbanua1, Ariyanto1, Mohammad Mukhlas Af1, Sigit Tri Atmaja1, Muhammad Samsul Maarif1,2, Kurnia Fajar Adhi Sukra1,2, Fauzi Dwi Setiawan1 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 PERI IRAWAN Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Artificial neural networks Emission prediction Gaussian process regression Hybrid electric vehicles Prediction uncertainty Vehicle emissions 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/4bc334676184a81d32c1d45c3daf6e91.pdf 8c26d53b27a24b8564f62c879c2ec6d8 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 Business intelligence through data visualization: a case study using marketing campaign dataset Subject The topic of the resource Business intelligence Data science Data visualization Machine learning Marketing analytics Description An account of the resource In today’s competitive business environment, data-driven marketing strategies are essential for successful campaign outcomes. This study presents a comprehensive analysis of marketing campaign data, emphasizing its role in enhancing customer engagement, improving decision-making, and increasing conversion rates. It explores the complexity of campaign dynamics and consumer behavior, demonstrating how business intelligence and data visualization techniques support informed marketing decisions and actionable insights. Advanced data science methods such as data cleaning, feature engineering, and cross-validation enhance predictive accuracy and campaign optimization. Visualization plays a central role in transforming raw data into interpretable insights, enabling businesses to identify trends in customer preferences and purchasing behavior. Key findings reveal that customers aged 51–70, particularly those with higher education and income levels, show the greatest purchasing power, especially for wine and meat products. These insights help align marketing strategies with data-driven understanding to design personalized campaigns that resonate with target audiences. By combining analytical methods with effective visualization, businesses can develop impactful campaigns that drive engagement, boost conversions, and foster revenue growth. The study concludes with directions for future research, including real-time data processing and automated decision-making systems to ensure continuous improvement in digital marketing strategies. Creator An entity primarily responsible for making the resource Aditi Bansal, Ankit Gupta 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 Business intelligence Data science Data visualization Machine learning Marketing analytics https://repository.horizon.ac.id/files/original/dfe66c6650b3d39439fa254268ea0d8c.pdf 2764ab4bfa5acd8c97644a2bbbed3f81 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 Comparative performance analysis of convolutional neural network-architectures on coffee-bean roast classification Subject The topic of the resource Agtron level Classification Coffee-bean roast Convolutional neural networks Performance analysis Description An account of the resource The classification of coffee bean roast levels using Agtron standards has evolved from traditional subjective methods to technology-driven approaches employing advanced artificial intelligence. Recent advancements in computer vision have demonstrated the capability of convolutional neural networks (CNNs) in providing objective and consistent roast level classification compared to human visual assessment, which is prone to variability and subjectivity. This research presents a performance analysis of five CNN architectures (AlexNet, ResNet, MobileNet, VGGNet, and DenseNet) for classifying coffee beans into eight distinct Agtron roast levels. The comprehensive methodology encompasses four phases: i) data acquisition, ii) image preprocessing, iii) model training and validation, and iv) evaluation metric. During training-validation, DenseNet outperformed other models, achieving 99.702% training accuracy and 77.68% validation accuracy. In the testing evaluation, DenseNet also led with an average testing accuracy of 93.8%, followed by ResNet at 92.6%, VGGNet and AlexNet both at 92.4%, and MobileNet at 89.7%. The results show that the DenseNet shows promise in classifying Agtron coffee-bean roast classification. Creator An entity primarily responsible for making the resource Irfan Asfy Fakhry Anto1, Jony Winaryo Wibowo2, Aris Munandar2, Taufik Ibnu Salim2 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 Agtron level Classification Coffee-bean roast Convolutional neural networks Performance analysis https://repository.horizon.ac.id/files/original/e34d5bb716dc0b4651909bcaf070676a.pdf 67485892dd25563e6e63a71cea0e4fba 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 Design and analysis of a new scheme of the FOSTA for DFIG based wind turbine Subject The topic of the resource Active and reactive power Doubly fed induction generator Fractional calculus theory Super twisting algorithm Wind turbine Description An account of the resource An super-twisting algorithm (STA)-based controller was designed and implemented in this study to achieve precise control over the stator active and reactive power of a doubly fed induction generator (DFIG)-equipped wind turbine device. The fractional calculus theory (FCT) allowed the STA to maximize its effectiveness and performance. A distinct form is sent to the FCT-based STA controller. The stator flux orientation technique uses control that is independent of stator active and reactive powers. In order to achieve a quick system with sufficient precision and a robust control strategy, the hybrid method control is based on the fractional-order super twisting algorithm (FOSTA) and FCT. To demonstrate the performance, efficacy, and resilience of the stated nonlinear approach, a number of simulations are provided. Creator An entity primarily responsible for making the resource Kheira Belgacem, Houaria Abdelli, Mebarka Atig, Abdelkader Mezouar 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