2 10 26 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/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/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/a9e12d4e8e315f228d4f6f7d92ae2412.pdf e5557ac2652495b3870cfd123e5fbd60 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 Enhancing handover management in 5G networks with encoder-decoder LSTM for multistep forecasting Subject The topic of the resource 5G Encoder-decoder Handover LSTM/Bi-LSTM Reference signal received power Description An account of the resource The continuous evolution of wireless communication networks, fueled by advancements in 5G and the envisioned potential of 6G technologies, has introduced significant challenges in mobility management and handover (HO) optimization. The frequent HOs due to network densification, particularly at high frequencies like millimeter waves (mmWave) and terahertz (THz) bands, can lead to increased latency, and potential service disruptions. To address these issues, artificial intelligence (AI) driven approaches are emerging as promising alternatives. This paper explores the use of deep learning techniques for predictive HO management. An encoder-decoder long short-term memory (ED-LSTM) model is proposed to generate multistep predictions of future reference signal received power (RSRP) values. The model was trained and evaluated on two distinct real-world drive-test datasets. The results demonstrate that the proposed ED-LSTM model achieves lower prediction error, with a mean absolute error (MAE) of 2.07 for dataset 1 and 2.33 for dataset 2, and a mean absolute percentage error (MAPE) of 2.80% for dataset 1 and 2.96% for dataset 2. Overall, the ED-LSTM outperforms the bidirectional LSTM (BiLSTM) and standard LSTM (S-LSTM) model, achieving improvements of 33–38% on dataset 1 and 48-50% on dataset 2 in terms of MAE and MAPE, respectively Creator An entity primarily responsible for making the resource Zineb Ziani1,3, Mohammed Hicham Hachemi1,4, Bouabdellah Rahmani1,3, Mourad Hadjila2,4 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 https://repository.horizon.ac.id/files/original/af9c6b181e270bf861165cf27ec7b6e8.pdf 73439c59437db69da624b6b1242432f1 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 Optimization of principal component analysis and k-nearest neighbors in cultivation area classification red onion Subject The topic of the resource K-nearest neighbor Optimization Principal component analysis Red onion Region classification Description An account of the resource This research aims to increase the effectiveness in classifying shallot cultivation areas through the combined application of principal component analysis (PCA) and k-nearest neighbors (KNN) methods. Shallot is an important agricultural commodity, and identification of optimal areas for its cultivation is essential to support food self-sufficiency. Onion cultivation is generally done in the highlands. One of the areas with shallot cultivation in North Sumatra Province is Berastagi, Karo Regency. This research was conducted by determining the spatial extent of upland land. In the use of data there are 2 types of data that will be used: land suitability dataset and land condition dataset for each region. The PCA method is utilized to simplify the data structure by reducing the number of dimensions and removing insignificant attributes, while KNN was used to classify regions based on their suitability for shallot cultivation. This research produces a classification map that can be used to identify the most optimal areas for shallot cultivation. The test results with the regional spatial dataset using precision, recall and fi-score testing accuracy value 0.92%, and macro avg value 0.94%, weighted avg value 0.93%. Creator An entity primarily responsible for making the resource Arif Ridho Lubis1, Purwa Hasan Putra1, Fahdi Saidi Lubis2 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/ef0e23c5fae7418e1ff4f5ead181fd2b.pdf ff72ead44f42742fe18966d8fb0f8e71 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 The effectiveness of bentonite in reducing soil resistance in acidic water swampland Subject The topic of the resource Acidic swamp land Bentonite Grounding electrode Grounding resistance Mixture effectiveness Potential hydrogen Description An account of the resource This study aims to evaluate the effectiveness of bentonite mixtures in reducing grounding resistance in acidic swampy areas. The method used is an experiment comparing resistance before and after the addition of bentonite in various compositions (25%, 50%, 75%, and 100%), supplemented with linear regression analysis. The results showed that bentonite significantly reduced soil resistance in three types of electrodes: iron rebar, copper-coated iron, and galvanised iron. The highest reduction in resistance was achieved in iron rebar electrodes, from 35.93 Ω to 22.46 Ω (a 37% reduction) with the addition of 25% bentonite. Linear regression analysis showed a consistent negative relationship between the percentage of bentonite and grounding resistance, with a coefficient of determination (R²) varying between 26.40% and 73.39%. These findings indicate that bentonite is effective as a natural grounding material in acidic swampy areas. This research makes an important contribution to the development of more efficient and safer electrical systems in swampy areas and challenging environments, while also supporting the use of natural materials to reduce dependence on synthetic chemicals. Creator An entity primarily responsible for making the resource Dian Eka Putra1, Muhammad Irfan Jambak2, Zainuddin Nawawi2 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 Acidic swamp land Bentonite Grounding electrode Grounding resistance Mixture effectiveness Potential hydrogen 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/000a94d2049e0cc6457c7a693e22054f.pdf 233d5061dc009e63a42c62b620924bc0 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 Reconfigurable ultra-wideband hexagonal antenna with two notched-band features for wireless applications Subject The topic of the resource CST Microwave Studio Frequency reconfigurable Lumped element Notch filters Ultra-wideband Description An account of the resource Owing to the demand for frequency agility, a switchable ultra-wideband (UWB) hexagonal antenna was developed in this study. The proposed antenna features two notch filters introduced by two U-shaped slots on the patch to reduce interference from other wireless networks by rejecting the unique frequency bands. In addition, the proposed antenna comprises a hexagonal radiator attached to a feeding 50 Ω standard microstrip line. To fabricate the antenna prototype, a substrate (Rogers RT/Duroid 5880) with loss tangent and relative permittivity values of 0.0009, and 2.2, respectively, was used. Frequency and pattern reconfigurability were achieved by changing the electrical equivalent circuit of two positive-intrinsic-negative (PIN) diodes sandwiched within two U-shaped slots. The evaluation confirmed that the antenna operated within the D1&D2-ON configuration across the entire UWB range while, effectively filtering the wireless body area network (WBAN) (6.10–6.56 GHz) and radar application (9.16–10.79 GHz) bands when both diodes were OFF. The radiation efficiency and gain reached values of 92.9 % and 7.5 dB, respectively. The proposed design offers a robust performance with enhanced interference rejection. This makes it suitable for modern cognitive radio systems. Creator An entity primarily responsible for making the resource Khaled B. Suleiman1, Akrem Asmeida2, Shipun Anuar Hamzah3, Mohd Shamian bin Zainal4 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/72b0ef7dc6f000e040facbe7eeacb6e1.pdf cdcdf0746d3bce6d089f242970a109be 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 Escalating QoS by firefly optimization of CGSTEB routing protocol with subordinate energy alert gateways Subject The topic of the resource Clustering Firefly optimization General self-organized tree-based energy balancing Routing protocols Subordinate energy alert gateways Wireless sensor networks Description An account of the resource Wireless sensor networks (WSNs) comprise large numbers of sensor nodes that are highly constrained by limited battery power, making energy-efficient routing essential for sustaining network lifetime and service quality. Among existing solutions, the general self-organized tree-based energy balancing (GSTEB) protocol with clustering has been widely adopted for energy-aware communication. However, GSTEB and its clustered variant often suffer from energy imbalance, high packet loss, and reduced quality of service (QoS) due to excessive load on cluster heads (CHs). To address these challenges, this paper introduces an enhanced routing framework that integrates firefly optimization with clustered GSTEB (CGSTEB) and introduces subordinate energy alert gateways (SEAGs). The firefly algorithm is applied to optimize CH selection through a fitness function that balances residual energy and node proximity, ensuring efficient cluster formation and adaptive load distribution. Meanwhile, SEAGs establish a twohop communication model between CHs and the base station (BS), reducing CH energy consumption and preventing premature node failures. Simulation experiments conducted in NS2 demonstrate that the proposed firefly-CGSTEB with SEAG significantly improves QoS metrics, including network lifetime, energy utilization, throughput, and packet loss rate, compared with conventional CGSTEB. These results confirm the effectiveness of combining metaheuristic optimization with gateway-assisted routing for resilient and energy-efficient WSNs. Creator An entity primarily responsible for making the resource R. Madonna Arieth1, Ramya Govindaraj2, Subrata Chowdhury3, Thi Thu Nguyen4, Duc-Tan Tran5 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/c178b23d6e6eec473dc45482ee785cf3.pdf 07ead41b1ca1963b09321efe869390a2 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 Lightning studies on effects on distribution lines: a bibliometric analysis Subject The topic of the resource Bibliometry Distribution line Induced voltage Lightning PRISMA Description An account of the resource The study of lightning effects on distribution lines is of vital importance for the reliability and safety of electrical systems, as lightning is one of the main causes of failures. The purpose of this study is to perform a bibliometric analysis to evaluate academic productivity trends and research trajectories in this field. The methodology was based on a comprehensive search of the Scopus database, from which a total of 545 articles published between 1932 and 2024 were analyzed. For the analysis, the VOSviewer tool and the Bibliometrix library in R were used. The results reveal a constant increase in productivity since the 1970s, with Japan and China emerging as the most prolific countries. The research has evolved from early theoretical and experimental studies toward the use of advanced computational models and, more recently, the application of machine learning techniques for fault detection. In conclusion, the findings of this study provide a consolidated view of the field, which is fundamental for engineers to be able to design more robust protection systems and to guide future research toward model validation and the integration of renewable energy technologies. Creator An entity primarily responsible for making the resource Vladimir Henao – Céspedes1, Luis Fernando Díaz – Cadavid2 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 Bibliometry Distribution line Induced voltage Lightning PRISMA