1 10 26 https://repository.horizon.ac.id/files/original/a2fd5a7840246b8227575841c28dee7a.pdf 0cc089dd0ca84a4ce154c2266a95cc2b 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 Particle swarm optimization-optimized integrator backstepping for the control of electric wheelchairs velocity Subject The topic of the resource Electric powered wheelchair Integrator backstepping control Optimization Particle swarm optimization Permanent magnet synchronous motors Description An account of the resource Most people suffering from temporary or permanent disabilities rely on wheelchairs or electric powered wheelchairs (EPW) to maintain autonomy of movement. To address different EPW control challenges, several studies have investigated this kind of robot. This paper focuses on the optimization of the integrator backstepping control parameters of the EPW. The system operates using two permanent magnet synchronous motors (PMSM), noted for their great efficiency, substantial torque, minimal noise, and robustness. At first, the dynamic model for both EPW-motors is showned. After that, a nonlinear integrator backstepping command based on Lyapunov’s second technique, which combines the choice of the energy function with the control laws, was applied to the resulting global model. To ensure optimal performance, the control parameters were tuned by means of an optimization approach. Specifically, the particle swarm optimization technique (PSO) was employed to search for the optimal parameters (gains) of the integrator backstepping controller. In order to assess the performance of the optimized backstepping–based control approach, numerical simulations were conducted to illustrate the evolution of both electrical and mechanical velocity- related variables. Creator An entity primarily responsible for making the resource Djamila Boubekeur1,2, Khayreddine Saidi3,4, Mohammed Messirdi3, Abdelmadjid Boumédiène3 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 powered wheelchair Integrator backstepping control Optimization Particle swarm optimization Permanent magnet synchronous motors https://repository.horizon.ac.id/files/original/e16ba8ecd7580cea0a093663377d32da.pdf fcbc47453787ab1a3f57ddb0ab693224 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 Object detection and tracking with decoupled DeepSORT based on αβ filter Subject The topic of the resource Deep learning DeepSORT High order tracking accuracy Object detection Object tracking Video surveillance Description An account of the resource With the rapid growth of the population, the demand for autonomous video surveillance systems has substantially increased. Recently, artificial intelligence has played a key role in the development of these systems. In this paper, we present an enhanced autonomous system for object detection and tracking in video streams, tailored for transportation and video surveillance applications. The system comprises two main stages: detection stage; this stage employs you only look once (YOLO)v8m, trained on the KITTI dataset, and is configured to detect only pedestrians and cars. The model achieves an average precision of 97.3% and 87.1% for cars and pedestrians classes respectively, resulting a final mean average precision (mAP) of 92.2%. Tracking stage; the tracking component utilizes the DeepSORT algorithm, which originally incorporates a Kalman filter for motion prediction and performs data association using cosine and Mahalanobis distances to maintain consistent object identifiers across frames. To improve tracking performance, we introduce two key modifications to the original DeepSORT: architecture modification and Kalman filter replacement. The tracking tests are carried out on KITTI and MOTChallenge Benchmarks. The final order tracking accuracy (HOTA) scores achieve 77.645 and 54.019 for Cars and Pedestrians classes respectively in the KITTI-Benchmark and 45.436 for the Pedestrians class in the MOTChallenge-Benchmark. Creator An entity primarily responsible for making the resource Lakhdar Djelloul Mazouz, Abdessamad Kaddour Trea, Tarek Amiour, Abdelaziz Ouamri 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 DeepSORT High order tracking accuracy Object detection Object tracking Video surveillance https://repository.horizon.ac.id/files/original/fa2e17a3a0ee87718a01faacf3105550.pdf 655bc19389a0ca93b888ebcfe71a9112 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 Machine learning-based reconstruction of missing rainfall extremes: a comparative analysis with classical models Subject The topic of the resource Bias correction Expert team on climate change detection and indices Extreme rainfall Machine learning Rainfall data reconstructing Satellite rainfall data Spatial interpolation Description An account of the resource The limited availability of daily rainfall data remains a key challenge in rainfall data analysis. This study assesses the effectiveness of spatial interpolation and bias correction techniques using satellite-derived rainfall data to fill missing observations in the Banten and Jakarta regions. Three interpolation methods inverse distance weighting (IDW), kriging, and spline were compared. Nine statistical and machine learning-based bias correction methods were applied to climate hazards group infrared precipitation with station data (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP), and global precipitation measurement integrated multi-satellite retrievals for GPM (GPM IMERG). Performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), bias, Pearson correlation (R), and Kling-Gupta efficiency (KGE) in the expert team on climate change detection and indices (ETCCDI) extreme index. The research findings indicate that CHIRPS with quantile mapping (QM) bias correction delivers the best performance, followed by random forest regression (RFR) as the most accurate machine learning method. In spatial interpolation, IDW stands out as the leading method. Testing the extreme index ETCCDI confirms that CHIRPS-QM consistently outperforms machine learning and interpolation methods. In general, CHIRPS-QM and IDW represent the most effective combination of techniques for reconstructing daily rainfall, particularly extreme events. This study uniquely integrates spatial interpolation and bias correction in a unified evaluation. Creator An entity primarily responsible for making the resource Yanuar Henry Pribadi1, 2, Tania June1, I Putu Santikayasa1, 4, Supari3, Ana Turyanti1 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 Bias correction Expert team on climate change detection and indices Extreme rainfall Machine learning Rainfall data reconstructing Satellite rainfall data Spatial interpolation https://repository.horizon.ac.id/files/original/f0d0533e5bc7774d5920938c0ba5f23e.pdf 616b98abdc4eb6e61db3aaad7511c6ec 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 Retrieval-augmented generation for Arabic legal information: the family code case study Subject The topic of the resource Arabic-natural language processing Large language model Legal accessibility Moroccan law Retrieval-augmented generation Semantic search Description An account of the resource This document describes the implementation and evaluation of a retrieval-augmented generation (RAG) system to improve access to and understanding of Moroccan law, particularly the family code in Arabic. The research addresses the drawbacks of the widely used linguistic model applied to complex legal terminology in Arabic and aims to help citizens access crucial legal data. We built a new custom dataset with 2.5 k question-answer pairs while preprocessing and using the BGE-m3 embedding model in this experiment. Performance metrics, such as mean reciprocal rank (MRR), Recall@k, and F1-score, indicate that the RAG approach is effective compared to the use of standalone large language models (LLMs). Moreover, an evaluation on metrics such as the blue score, fidelity, response relevance, and contextual relevance indicated that the matching of meanings and context were well captured, which signifies a very good semantic understanding. The research highlights the need for language-specific model specialization in Arabic and presents its main challenges, such as dialectal variations and appropriate evaluation measures. The results indicate that well-developed RAG systems offer a promising approach to improving access to legal information in Arabic-speaking practice communities and to guiding future research and development in this field. Creator An entity primarily responsible for making the resource Jamal Hrimech, Mohammed Mghari, Youssef Zaz 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 Arabic-natural language processing Large language model Legal accessibility Moroccan law Retrieval-augmented generation Semantic search https://repository.horizon.ac.id/files/original/1549671500209318f0fe8a34687fb23a.pdf fba67015203010cc7577acb3f8351e29 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 Metamaterial-enhanced four-port MIMO antenna for 5G communications at 28/38 GHz Subject The topic of the resource Electromagnetic software Equivalent circuit MmWave technology Multiple-in multiple-out antenna Planar antenna Description An account of the resource This work presents a novel compact four-port multiple-in multiple-out (MIMO) antenna enhanced with metamaterial unit cells for 5G millimeter-wave (mmWave) applications at 28 and 38 GHz. Compact MIMO antennas at mmWave bands often suffer from high mutual coupling, which degrades isolation and diversity performance. To address this, the proposed design integrates metamaterial loading around each radiating element to effectively suppress coupling, enhance isolation, and improve overall efficiency. The antenna, measuring 27×27×0.8 mm³, is implemented on a flexible FR4_epoxy substrate (εr=4.4), enabling compatibility with portable and embedded devices. Full-wave simulations performed in both ANSYS high-frequency structure simulator (HFSS) and computer simulation technology (CST) studio suite confirm the effectiveness of the approach, achieving an exceptionally low envelope correlation coefficient (ECC) (0.0001), a fivefold reduction in channel capacity loss (CCL), and a wide impedance bandwidth of 25.90–34.93 GHz with |S11| below −10 dB in both operating bands. The design also exhibits stable directional gain and low sidelobes. Compared with recent compact MIMO antennas reported in the literature, the proposed configuration offers significantly improved isolation, bandwidth, and mechanical flexibility. These features make it a strong candidate for integration into high-capacity 5G modules, portable terminals, and compact internet of things (IoT) communication systems. Creator An entity primarily responsible for making the resource Remili Fatima1, Bouttout Farid1, Djellid Asma2 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 Electromagnetic software Equivalent circuit MmWave technology Multiple-in multiple-out antenna Planar antenna https://repository.horizon.ac.id/files/original/17fa7f044f6ff0cb49e2a5f22c4f354d.pdf c3165617452a390863f3f2e37a3f5be5 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 Novel fractional order sinusoidal oscillators using operational trans resistance amplifier Subject The topic of the resource Active element Continued fraction expansion Fractional order Frequency of oscillation virtual ground Sinusoidal oscillator Sinusoidal signal Description An account of the resource The design of fractional order circuits in very large-scale integration (VLSI) domain is gaining the interest of many researchers. At the same time design of fractional circuits using the current mode devices is attracting the research community. In this paper, several possible fractional order sinusoidal oscillators using operational trans resistance amplifier (OTRA) as a basic building block is presented. The necessary condition for the frequency of oscillation and condition for oscillations is derived. Fractional order operator sα is the most crucial one to be approximated. In this paper, the fractional order element is approximated by the continued fraction expansion (CFE). The approximation is carried out up to fifth order. The circuits are tested with the simulation software named LTspice. The results agree with the theoretical one. The proposed circuits offers a frequency of 15 MHz, 20 MHz, and 25 MHz which is higher in value as compared to the existing circuits. The proposed circuits finds applications in bio medical, communication circuits. Creator An entity primarily responsible for making the resource Battula Tirumala Krishna1, Vanitha Kakollu2, Manchala Madhusudhan Prasad3 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 Active element Continued fraction expansion Fractional order Frequency of oscillation virtual ground Sinusoidal oscillator Sinusoidal signal 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 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/477c09cd30446be7c2e7e866c27a526e.pdf 2cc14c12bbc54e2369ad79d71183b6ad 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 Watermarking on spread multi-frame data video using discrete wavelet transform hybrid and frame ratio message variance Subject The topic of the resource Discrete wavelet transform Frame ratio Spread multi-frame Video Watermarking Description An account of the resource The exponential growth of video sharing demands secure and imperceptible watermarking methods. This study presents a video watermarking framework using discrete wavelet transform (DWT) with hybrid sub-band embedding and multi-frame allocation to balance imperceptibility, capacity, and robustness. Watermark bits are adaptively distributed across low-low (LL), low-high (LH), and high-low (HL) sub-bands of selected frames, with uncompressed audio video interleave (AVI) ensuring coefficient integrity. Experiments on 640×360 videos show LL-only embedding achieves high imperceptibility (peak signal-to-noise ratio (PSNR) > 38.6 dB, structural similarity index measure (SSIM) ≥ 0.9945, and bit error rate (BER) = 0), while LL-dominant hybrids increase capacity with slight robustness trade-offs. Embedding in LH and high-high (HH) sub-bands raises distortion vulnerability. Under cropping, BER rises from 0.005 to 0.205 (0–50%), and normalized correlation (NC) drops from 0.998 to 0.802, remaining acceptable for ≤30% cropping. The scheme resists joint photographic experts’ group (JPEG) compression quality factor 20–80 (Q20–Q80), resizing (≥70%), and mild Gaussian blur (3×3), maintaining efficient decoding under higher payloads. Future work may apply error-correction coding and redundancy-aware embedding for improved resilience. Overall, the proposed method offers a secure, adaptive, and efficient solution for video authentication and covert communication. Creator An entity primarily responsible for making the resource Ilham Firman Ashari, Sarwono Sutikno 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 Discrete wavelet transform Frame ratio Spread multi-frame Video Watermarking 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