1 10 26 https://repository.horizon.ac.id/files/original/43c23e84a99c37db8aef34b67e44a03e.pdf e0a61b1213dce8fd7b647e1c1ea09e40 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.2 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 comparative analysis of transfer learning models on suicide and non-suicide textual data Subject The topic of the resource Deep learning Media social Sentiment analysis Suicide Transformer model Description An account of the resource The rise of social media has allowed individuals to express themselves freely, increasing the visibility of mental health concerns, including suicidal tendencies. This issue is particularly significant, as suicide is one of the leading causes of death globally. The objective of this study is to develop a model capable of accurately detecting suicide-related textual data using advanced natural language processing techniques. To achieve this, we applied transfer learning models, including bidirectional encoder representations from transformers (BERT), robustly optimized bidirectional encoder representations from transformers (RoBERT), a lite BERT (ALBERT), and decoding-enhanced BERT with disentangled attention (DeBERTa). the dataset used in this research includes 232,074 posts from Reddit, categorized into suicide and non-suicide labels. Preprocessing steps such as removing HTML tags, special characters, and punctuation were applied, followed by stopword removal and lemmatization. The models were trained and evaluated using accuracy, precision, recall, and F1-score metrics. Among the models tested, DeBERTa demonstrated superior performance, achieving an accuracy of 98.70% and an F1-score of 98.70%. These findings suggest that transfer learning models, particularly DeBERTa, are effective in identifying suicidal ideation in textual data. Creator An entity primarily responsible for making the resource Merinda Lestandy1, Abdurrahim2, Amrul Faruq1, Muhammad Irfan1 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Jan 22, 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 Media social Sentiment analysis Suicide Transformer model https://repository.horizon.ac.id/files/original/784784735ae9aa463f155cd73969be6d.pdf e45ce44b10bbae68c491eb7cc28e889a 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.2 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 wideband microstrip antenna employing ring and hexadecagonal slots with parasitic elements for W-band applications Subject The topic of the resource Hexadecagonal slot Millimeter-wave Parasitic elements Point-to-point communications Ring slot Slotted patch W-band Description An account of the resource This article presents a monopole patch antenna, designed for operation in the W-band. The antenna is constructed on Rogers/RT 5880 dielectric material with dimensions of 3.4×4×0.16 mm³, a loss tangent of 0.0009, and a relative permittivity of 2.2. The initial design features a simple rectangular patch measuring 1.3542×1.0306 mm², powered by a microstrip line using an inset feed. To enhance the bandwidth and gain, two parasitic rectangular elements were added on both sides of the patch in addition to the incorporation of a circular ring slot with the ground plane. Further improvements in bandwidth and return loss were achieved by etching a hexadecagonal-shaped slot on the patch. Simulation results indicate that the optimized design achieves an impedance bandwidth of 28.54 GHz, ranging from 79.67 GHz to 108.21 GHz, centered at 88 GHz. The antenna also shows a maximum return loss of 59 dB and a voltage standing wave ratio (VSWR) of 1.0022. The radiation pattern is directional, with a peak gain of approximately 8.57 dBi, and a maximum directivity of about 8.6 dB, as predicted by the computer simulation technology (CST) frequency-domain solver. These advantageous characteristics make the proposed antenna a suitable choice for point-to-point transmission applications. Creator An entity primarily responsible for making the resource AbdulGuddoos S. A. Gaid1, Mohammed M. S. Qaid1, Mohammed A. E. Abdullah1, Mohammad Ahmed Alomari2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Jan 23, 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/efbd94446cbac57212f76186e6ee44be.pdf 665473207b52b1069c2c50304f5919fc 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.2 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 crop yield prediction using machine learning and deep learning: a comprehensive review Subject The topic of the resource Crop yield prediction Deep learning Machine learning Remote sensing Systematic literature review Vegetation indices Description An account of the resource The advancement of machine learning (ML) and deep learning (DL) techniques has significantly improved crop yield prediction, making it more accurate and reliable. In this review, the implementation of ML and DL algorithms for crop yield prediction is thoroughly investigated, focusing on their crucial role in enhancing crop productivity. Along with ML and DL algorithms examine, the review analyses the use of remote sensing technologies, such as satellite and drone data, in providing high-resolution inputs essential for accurate yield predictions. The study identifies the state of art algorithms, most used features, data sources and evaluation metrics, providing a comparison of ML and DL. The findings indicate that DL models are more effective with large datasets, while ML models remain robust for smaller datasets. The future directions are proposed to develop the generalised models for different crops and regions. The review aims to assist researchers by summarising state of art techniques and identifying the present. Creator An entity primarily responsible for making the resource Ayush Anand, Kavita Jhajharia Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Jan 22, 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 Crop yield prediction Deep learning Machine learning Remote sensing Systematic literature review Vegetation indices https://repository.horizon.ac.id/files/original/939d8dda2329beb61bb08e6381de0e53.pdf cb7ca2800963a76000845964e0864ec5 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.2 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 Adversarial-robust steganalysis system leveraging adversarial training and EfficientNet Subject The topic of the resource Adversarial attacks Deep learning EfficientNet Steganalysis Steganography Description An account of the resource Steganalysis aims to detect hidden messages within digital media, presenting a significant challenge in the field of information security. This paper introduces an adversarial-robust steganalysis system leveraging adversarial training and the powerful feature extraction capabilities of EfficientNet. We utilize EfficientNet to extract robust features from images, which are subsequently classified by a dense neural network to distinguish between steganographic and non-steganographic content. To enhance the system’s resilience against adversarial attacks, we implement a custom adversarial training loop that generates adversarial examples using the fast gradient sign method (FGSM) and integrates these examples into the training process. Our results demonstrate that the proposed system not only achieves high accuracy in detecting steganographic content but also maintains robustness against adversarial perturbations. This dual approach of leveraging state-of-the-art deep learning architectures and adversarial training provides a significant advancement in the field of steganalysis, ensuring more reliable detection of hidden messages in digital images. Creator An entity primarily responsible for making the resource Thakwan Akram Jawad1,2, Jamshid Bagherzadeh Mohasefi1, Mohammed Salah Reda Abdelghany3 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Jan 22, 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 Adversarial attacks Deep learning EfficientNet Steganalysis Steganography https://repository.horizon.ac.id/files/original/6d44aab05c9b9b41b421c821abcf33ee.pdf 38b3dacc5c7ccf115ae00245882d1171 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.2 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 the impact of sports activity intensity on muscle capacity through integrated biosensor technology Subject The topic of the resource Biosensors Electromyography Integrated Muscle Sport activity Description An account of the resource In the past few years, biosensor technology has paved the way for new insights into the physiological effects of physical exercise. Quantitative analysis, especially in the case of muscle capacity measurement, is the focus of studies to assess the impact of sports activities. Therefore, this study examines the impact of sports activity intensity on muscle capacity using an integrated biosensor system developed at Bandung State Polytechnic. Surface electromyography (sEMG) measurements were conducted on 30 participants aged 20–25 during various sports activities. Results showed a strong positive correlation (r=0.814) between sports activity frequency and muscle contraction, suggesting higher activity correlates with increased muscle activity. Conversely, the correlation during muscle relaxation was low (r=0.261), indicating independence from sports activity. In the future, it is expected that integrated biosensors will have the ability to concurrently measure and monitor various parameters like heart rate (via electrocardiogram), blood oxygen levels (via photoplethysmography), and blood pressure. The integrated biosensor system allows for comprehensive assessment and optimization of sports performance and injury prevention strategies. Creator An entity primarily responsible for making the resource Ervin Masita Dewi1, Nurista Wahyu Kirana1, Sugondo Hadiyoso2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Dec 26, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Biosensors Electromyography Integrated Muscle Sport activity https://repository.horizon.ac.id/files/original/aca7fbb70736d693042b1483cbafc135.pdf 08db2e06b253480fb0b6355a723e109e 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.2 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 Challenges in the technological adoption of document management systems Subject The topic of the resource Behavioural intention Document management system Effort expectancy Public administration Unified theory of acceptance and use of technology Description An account of the resource Incorporating information and communication technologies (ICT) in public organisations in keeping with digital government policies and access to ICT by citizens motivates public institutions to implement systems to provide better services to citizens. One of the most essential public services is the documentary process, which includes document management systems (DMSs) storing, organising, and managing the documentary flow. The acceptance and use of a DMS enabling digital signature in public institutions depends to a certain extent on a set of factors influencing user behaviour towards it. This paper reports the findings of a quantitative, cross-sectional, and correlational study examining the behavioural intention (BI) to use a DMS, employing three constructs of the unified theory of acceptance and use of technology (UTAUT). The research involved 998 workers from public institutions who participated in a survey, with quantitative data analysed using Spearman correlation. The results show that performance expectancy (PE), effort expectancy (EE), and social influence (SI) positively correlate with the BI to use a DMS and thus must be considered as relevant factors in DMS implementation in public institutions. The results provide relevant information to policymakers and DMS managers to promote the adoption of DMS in the digital transformation process that organisations are experiencing. Creator An entity primarily responsible for making the resource Sussy Bayona-Oré, Marlene Reyes Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Jan 22, 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/521c04989adceed40ae2f780d3058be2.pdf a1cc1459f9be1589ce3b8d745d8c9bfb 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.2 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 Comparing global system for mobile and G-NetTrack signal strength in drive test study Subject The topic of the resource Global positioning system network track Global system for mobile module Kolmogorov-Smirnov Mann-Whitney Normality test Received signal strength indicator Description An account of the resource The quality of wireless signals is determined by the received signal strength indicator (RSSI), which can be measured using various tools, including Android apps and global system for mobile (GSM) modules. To enhance the measurement of signal quality and communication reliability, this study aimed to develop a complementary instrument consisting of a microcontroller, global positioning system (GPS) module, GSM module, and microSD card. The firmware was developed using C++ code and compiled using the Arduino integrated development environment (IDE). In addition, this study utilized the Kolmogorov-Smirnov test (K-S test) normality and the Mann-Whitney U test to investigate any differences in RSSI quality between the GSM module and global positioning system network track (G-NetTrack). The results showed that the GSM module consistently produced higher RSSI values than G-NetTrack in most locations along the route. Furthermore, the K-S test normality suggested that the RSSI values obtained from both tools were not normally distributed, and the Mann-Whitney U test revealed a significant difference between the two samples, with G-NetTrack having lower values than the GSM module (U=14730, p<0.05). This study demonstrated that the GSM module provides stronger signal strength measurements than G-NetTrack during the drive test, and highlighted the importance of using appropriate statistical tests to analyze RSSI data. Creator An entity primarily responsible for making the resource Hollanda Arief Kusuma1, Dwi Eny Djoko Setyono2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Jan 22, 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/efe3cadfbc03687481da4ff60c07302a.pdf bc8cbcad5e4f9ff3c60b60c2d9a2e8c0 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.2 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 Comparison of word embedding features using deep learning in sentiment analysis Subject The topic of the resource Deep learning Sentiment analysis Social media Text classification Word embedding Description An account of the resource In this research, we use several deep learning methods with the word embedding feature to see their effect on increasing the evaluation value of classification performance from processing sentiment analysis data. The deep learning methods used are conditional random field (CRF), bidirectional long short term memory (BLSTM) and convolutional neural network (CNN). Our test uses social media data from Netflix application user comments. Through experimentation on different iterations of various deep learning techniques alongside multiple word embedding characteristics, the BLSTM algorithm achieved the most notable accuracy rate of 79.5% prior to integrating word embedding features. On the other hand, the highest accuracy value results when using the word embedding feature can be seen in the BLSTM algorithm which uses the word to vector (Word2Vec) feature with a value of 87.1%. Meanwhile, a very significant change in value increase was obtained from the FastText feature in the CNN algorithm. After all the evaluation processes were carried out, the best classification evaluation results were obtained, namely the BLSTM algorithm with stable values on all word embedding features. Creator An entity primarily responsible for making the resource Jasmir1, Errissya Rasywir2, Herti Yani3, Agus Nugroho2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Jan 22, 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/0701f43da5e58654647a06d5ebf94f12.pdf 826b450ffa06414eb923ebff7761874e 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.2 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 Decision support system in machine learning models for a face recognition-based attendance system Subject The topic of the resource Attendance system Decision making Decision support system Face recognition Machine learning Description An account of the resource This research aims to develop a predictive model using face recognition-based attendance data and integrating decision support system (DSS) theory with machine learning (ML) techniques to identify high-performing teachers at vocational high schools (SMKs). The novelty of this research lies in integrating theory with the use of face recognition data and ML algorithms to predict and identify high-performing teachers, thereby enhancing decision-making processes and teacher performance management in SMK schools. The dataset consists of SMK teachers' attendance data obtained through a face recognition attendance system, totaling 998 entries. This research employs sensitivity analysis concepts from DSS theory and classification approaches from ML models utilizing support vector machine (SVM), decision trees (DT), and random forest (RF). The models are trained and tested on Google Colab using Python, with data distribution guided by the Pareto principle. The research findings indicate that integrating DSS theory with ML contributes to innovation and benefits in improving decision-making and teacher performance management by successfully predicting high-performing teachers. Evaluation results show the highest accuracy rate of 98% with the RF model, making it the best predictive model compared to the other two models. Creator An entity primarily responsible for making the resource Joseph Teguh Santoso1,2, Danny Manongga2, Hendry2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Dec 26, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Attendance system Decision making Decision support system Face recognition Machine learning https://repository.horizon.ac.id/files/original/2c710e58e09fe0d1c12461130ef16824.pdf cf399e5e50c77b2711aa3d4c2b4c6220 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.2 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 of the automation system for the chemical water treatment plant of the oil refinery in Santiago de Cuba Subject The topic of the resource Decentralized control Human machine interface Level control Programmable logic controller Supervisory control Description An account of the resource Production processes in modern industry demand higher levels of quality and efficiency in their products. The “Hermanos Díaz” Oil Refinery Company of Santiago de Cuba, a fundamental pillar in the economic and social development of the eastern part of the country, has a chemical water treatment plant responsible for supplying processed water to the industry’s boilers. The current state of this plant supports the lack of optimal physical-chemical conditions in the water it delivers and, therefore, the gradual deterioration of the boilers. This work conceives an automation solution for the dosing, precipitation, and clarification processes of the chemical water treatment plant. Control systems were designed based on instrumentation proposals, enabling reliable measurements and practical actions. In addition, an algorithm of supervision and automatic control using a programmable programmable logic controller (PLC) is presented, making the plant capable of delivering a product in optimal conditions. Images were designed for local and remote process control using a human-machine interface (HMI) panel and a supervisory control and data acquisition (SCADA) system. Finally, an automation architecture with a decentralized periphery is proposed to ensure safety and accuracy in the system’s decision-making through communication protocols. Creator An entity primarily responsible for making the resource Raidel Fidel Linares-Vicente1, José Ricardo Núñez-Álvarez2, Mirlenis Montoya Alayo3, Grether Lucía Real-Pérez4,5 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Dec 26, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Decentralized control Human machine interface Level control Programmable logic controller Supervisory control