3 10 26 https://repository.horizon.ac.id/files/original/bb67e21080ea25111ce8e75fe8e3409d.pdf 4e92f017110238652c46b0ac3edb12a4 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 Detecting fake news through deep learning: a current systematic review Subject The topic of the resource Deep learning Machine learning Misinformation detection Natural language processing Systematic review Description An account of the resource This systematic review explores the domain of deep learning-based fake new detection employing advanced search practices on Scopus and Web of Science (WoS) databases with keywords “fake news,” “deep learning,” and “method.” The study encompasses 33 articles categorized into three main themes: i) dataset and benchmarking for fake news detection, ii) multimodal approaches for fake news detection, and iii) deep learning applications and techniques for fake news detection. The analysis reveals the significance of curated datasets and robust benchmarking in improving the efficacy of fake news detection models. Additionally, the review highlights the emergence of multimodal approaches that integrate textual and visual information for improved detection accuracy. The findings clarify the essential role of deep learning applications, emphasizing the development of sophisticated models for automated identification of fake news. This systematic study adds to a thorough grasp of current research trends and offers insightful information for future developments in the field of deep learning-based false news identification. Creator An entity primarily responsible for making the resource Idza Aisara Norabid, Masita Jalil, Rozniza Ali, Noor Hafhizah Abd Rahim 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 Deep learning Machine learning Misinformation detection Natural language processing Systematic review https://repository.horizon.ac.id/files/original/c54e868a9c131c100efa39635f945cdb.pdf b9504e835c7c4dcb55a4c05a6dcb218a 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 K-Means clustering interpretation using recency, frequency, and monetary factor for retail customers segmentation Subject The topic of the resource Customer segmentation Industrial and innovation K-Means algorithm Machine learning Recency, frequency, and monetary analysis Small and medium enterprises Description An account of the resource Efforts to retain customers represent a crucial customer relationship management (CRM) strategy in every business, offering the potential to enhance profits, particularly for small and medium enterprises (SMEs). In the context of this study, which focuses on the transaction dataset of retailers in a developing market, Indonesia, the emphasis has predominantly been on customer attraction rather than the implementation of customer retention strategies. The primary objective of this research was to scrutinize customer transaction data within the dataset. The K-Means clustering (KMC) method, integrated with recency, frequency, and monetary (RFM) attributes, was employed to classify customers and formulate effective strategies for customer retention. Conducted through a descriptive research method with a quantitative approach, the study involved sequential stages of data preprocessing and RFM analysis for comprehensive data analysis. The outcomes revealed the identification of 5 distinct clusters with associated strategies based on the RFM scores obtained. These strategies, tailored to each cluster, serve as valuable insights in industrial and innovation for marketing and business strategic teams, offering practical approaches to customer retention that can lead to increased benefits for SMEs. Creator An entity primarily responsible for making the resource Agung Nugraha1, Yutika Amelia Effendi2, Nicholas1, Zejin Tao1, Mokh Afifuddin3, Nania Nuzulita4 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/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 https://repository.horizon.ac.id/files/original/f49777059e48c6e45abdddfc2dd7839e.pdf 0f9f35b6f7c8e41d15712164dfb721b6 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 Temperature response analysis between PD and PI controls applied to infant incubators Subject The topic of the resource Baby incubator DS18B20 sensor LM35 sensor Proportional and derivative Proportional and integral Description An account of the resource Premature infants, born with low birth weight, require specialized care and isolation due to their vulnerability to infections in public settings. Baby incubators, classified as life support equipment, play a crucial role in safeguarding these infants by maintaining a consistent temperature and humidity similar to the mother’s womb. This study compares the temperature control systems in baby incubators, specifically proportional and derivative (PD) control versus proportional and integral (PI) control. LM35 and DS18B20 sensors were employed in the study. Results from PD control using the LM35 sensor show a rise time of 5 min and 40 sec, a settling time of 25 min, and an overshoot of 2.2 °C. The DS18B20 digital sensor, under PD control, achieves a rise time in 6 min and 30 sec, a settling time of 23 min, with an overshoot of 1.2 °C. For PI control with the LM35 sensor, there’s a 3 °C overshoot, a 5-minute rise time, and a 30-minute settling time. The DS18B20 sensor under PI control exhibits a 2.7 °C overshoot, a 5-minute rise time, and a 29-minute settling time. PD control demonstrates lower overshoot and faster response but longer rise times than PI control. Future research explores fuzzy control systems and proportional integral derivative (PID)-fuzzy hybrid control. Creator An entity primarily responsible for making the resource Abd. Kholiq, Lamidi, Farid Amrinsani, Anisia Yunita Maulani Argumery, Hafizh Aushaf Mahdy 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 Baby incubator DS18B20 sensor LM35 sensor Proportional and derivative Proportional and integral https://repository.horizon.ac.id/files/original/477586c8defb3f17d596d6e93a8aa228.pdf e81b95430bc9e9e6ffbc3d04e8461284 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 Imposing neural networks and PSO optimization in the quest for optimal ankle-foot orthosis dynamic modelling Subject The topic of the resource Ankle-foot orthosis Modeling Neural network Particle swarm optimization System identification Description An account of the resource Individuals with abnormal walking patterns due to various conditions face significant challenges in daily activities, especially walking. Ankle-foot orthosis (AFO) devices are crucial in providing essential support to their lower limbs. Accurately modeling the dynamic behavior of AFO systems, particularly in predicting ground reaction forces, is a complex yet vital task to ensure their effectiveness. This research develops dynamic models for AFO systems using advanced modeling techniques, employing both parametric and non-parametric approaches. Parametric methods, such as particle swarm optimization (PSO), and non-parametric methods, like multi-layer perceptron (MLP) neural networks, are utilized through system identification methods. According to the findings, the MLP neural network continuously generates objective results and performs exceptionally well in correctly detecting the AFO system, attaining a noticeably lower mean squared prediction error of 0.000011. This research highlights the potential of advanced modeling techniques, particularly MLP neural networks, in enhancing AFO system modeling accuracy. Although parametric techniques like PSO are useful, the MLP approach performs better, offering insightful information about modelling AFO systems and indicating that non-parametric techniques like MLP neural networks have potential to further AFO creation and control. Creator An entity primarily responsible for making the resource Annisa Jamali1, Aida Suriana Abdul Razak1, Shahrol Mohamaddan2 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 Ankle-foot orthosis Modeling Neural network Particle swarm optimization System identification 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