1 10 26 https://repository.horizon.ac.id/files/original/b60b316c4b4bd484d5f23fd7d52fb581.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.1 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/2ef38393877d4eed03e242c64c1591c0.pdf 020955bb8a62f1c1467ccd6b933f9740 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.1 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 dual-band rectangular shape incorporated into circular patch antenna for 2.4/5 GHz wireless local area network applications Subject The topic of the resource 2.4/5 wireless local area network Circular shape Dual-band antenna Inverted U-slots Omnidirectional antenna Patch antenna Rectangular shape Creator An entity primarily responsible for making the resource Suthasinee Lamultree1, Nattakarn Somsanook1, Wararak Narkkoht1, Chuwong Phongcharoenpanich2 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 Oct 18, 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 https://repository.horizon.ac.id/files/original/b7d034469f7b3dc2cc0fd84885bb336c.pdf 075cc0f6d2863fedbe15830c2e606186 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.1 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 Predicting big data analytics adoption intention among small and medium enterprises in the Philippines Subject The topic of the resource Artificial intelligence Big data analytics Philippines Small and medium enterprises Technology acceptance model Description An account of the resource Big data analytics (BDA) has increasingly become popular both in theory and practice in recent years. Globally, larger businesses have used BDA to collect, study, and evaluate vast volumes of data to identify market trends and insights that lead to sound and intelligent business decisions. However, its adoption in small and medium enterprises (SMEs) is not fully maximized because of a variety of factors, including a lack of expertise and financial repercussions. As such, this paper seeks to delve into the predictors of BDA adoption intention among SMEs in a developing nation by extending the technology acceptance model (TAM). The quantitative surveys obtained from 438 SMEs were analyzed using partial least squares and structural equation modeling (PLS-SEM). The results revealed that perceived benefits, namely system quality, information quality, and predictive analytics accuracy, had positive relationships with perceived ease of use and usefulness, subsequently leading to attitude towards using BDA. Likewise, perceived security significantly influences perceived benefits, perceived ease of use, and attitude towards use of BDA. Further, attitude towards use was the most significant predictor of intention to adopt BDA among SMEs. Generally, the study indicates a positive interest in adopting BDA among Philippine SMEs. Creator An entity primarily responsible for making the resource Victor James C. Escolano1, Wei-Jung Shiang1, Alexander A. Hernandez2, Darrel A. Cardaña3 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 Nov 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 https://repository.horizon.ac.id/files/original/63722e684a0dc5d9b8c75503110f2b7b.pdf d55accc6df9a0351c88d22ed9d5999f8 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.1 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 Designing a marketplace system to assist incubators in higher education in fostering technopreneurship Subject The topic of the resource Business incubator Enterprise architecture Higher education Marketplace The open group architecture framework Description An account of the resource Technology-based startups play a vital role in boosting the Indonesian economy. However, many face sustainability challenges due to misalignment with market needs and a lack of comprehensive understanding of business processes. This condition demands the application of academic theories related to increasing productivity and highlights the need for innovative solutions. Universities can significantly support the development of these startups by leveraging business incubators to enhance competitiveness in the digital era. This research aims to propose a marketplace system architecture incorporating a business incubator unit (BIU), integrating scientific knowledge with societal business interests and talents in a university. The system is developed by the enterprise architecture (EA) methodology with the the open group architecture framework (TOGAF). This approach ensures a systematic design process that aligns with best practices. This study results in several strategic artefacts that provide guidance for creating a practical environment for entrepreneurs and translating theoretical insights into actionable business strategies. The artefacts can be rigorously tested and applied in real-world business incubator settings. The proposed system addresses critical challenges startup businesses face, such as enhancing business understanding and providing a fair foundation before entering the competitive market. Creator An entity primarily responsible for making the resource Cepy Slamet1, Ichsan Budiman1, Muhammad Insan Al-Amin2, Muhammad Ali Ramdhani1, Dudang Gojali3 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 Oct 18, 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 Business incubator Enterprise architecture Higher education Marketplace The open group architecture framework https://repository.horizon.ac.id/files/original/6dfd907d763222ae53820740bb226f62.pdf 7a674e4460b7a3d0c8ae0a8f04518541 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.1 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 Power system frequency control: instantaneous discrete testing for numerical relay using wavelet transform Subject The topic of the resource Frequency fault protection Multiresolution analysis Power system reliability Protective relaying Real-time digital simulator Wavelet transforms Description An account of the resource With today’s advanced technology and rapidly growing energy demands, the reliability of electrical power systems has reached an important level. With extensive monitoring and protection, system issues like voltage drops, power irregularities, and frequency variations can have destructive consequences on the power network. Therefore, as frequency relays play a critical role in protecting power generators and load equipment from power frequency shifts, relays have evolved from electromechanical to solid-state devices with ongoing optimization to handle integrated modern networks. Traditional numerical relays use Fourier transform to identify frequency changes, which necessitates numerous data samples and has limitations with transient waveform data. To address these challenges, this work proposes a new relay algorithm based on instantaneous discrete testing and wavelet transform for frequency analysis, aimed at enhancing relay performance. This new approach demonstrates promising advantages, including significant reductions in data sample requirements, compilation complexity, decision-making time, and improved handling of transient waveforms. Creator An entity primarily responsible for making the resource Emad Awada1, Akram Al-Mahrouk2, Eyad Radwan3, Tareq A. Alawneh1, Aws Al-Qaisi4 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 Oct 27, 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 Frequency fault protection Multiresolution analysis Power system reliability Protective relaying Real-time digital simulator Wavelet transforms https://repository.horizon.ac.id/files/original/3f12d39186a158006e58448a09a50053.pdf 5fcddab19c9dcb12b9c1e4b1f363500c 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.1 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 Customer segmentation in e-commerce: K-means vs hierarchical clustering Subject The topic of the resource Brand interests Customer insights Customer segmentation E-commerce Hierarchical clustering K-means clustering Description An account of the resource Customer segmentation is important for e-commerce companies to understand and target different customers. The primary focus of this work is the application and comparison of K-means clustering and hierarchical clustering, unsupervised machine learning techniques, in customer segmentation for e-commerce platforms. Clustering leverages customer search behavior, reflecting brand preferences, and identifying distinct customer segments. The proposed work explores the K-means algorithm and hierarchical clustering. It uses them to classify customers in a standard e-commerce customer dataset, mainly focused on frequently searched brands. Both techniques are compared based on silhouette scores and cluster visualizations. K-means clustering yielded well-separated segments compared to hierarchical clustering. Then, using the K-means algorithm, customers are classified into different segments based on brand search patterns. Further, targeted marketing strategies are discussed for each segment. Results show three customer segments: high searchers-low buyers, loyal customers, and moderate engagers. The proposed work provides valuable insights into customers that could be used for developing targeted marketing campaigns, product recommendations, and customer engagement strategies to enhance the conversion rate, customer satisfaction, and, in turn, the growth of an e-commerce platform. Creator An entity primarily responsible for making the resource Sumit Kumar1, Ruchi Rani2, Sanjeev Kumar Pippal3, Riya Agrawal2 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 Sep 20, 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 https://repository.horizon.ac.id/files/original/1152234763da67b29b590bf1e3beb236.pdf a76164e75bcae6bfaa26a62ff49060b7 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.1 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 Enhanced sentiment analysis and emotion detection in movie reviews using support vector machine algorithm Subject The topic of the resource Emotion detection Movie review Sentiment analysis Support vector machine Text mining Description An account of the resource Films evoke diverse responses and reactions from audiences, captured through their reviews. These reviews serve as platforms for audiences to express opinions, evaluations, and emotions about films, reflecting the personal experiences and unique perceptions of the viewers. Given the vast volume of reviews and the distinctiveness of each perspective, automated analysis is essential for efficiently extracting valuable insights. This study employs the support vector machine (SVM) algorithm for classifying movie reviews into positive and negative categories. The dataset includes 50,000 IMDb movie reviews, split evenly between positive and negative sentiments. Each review is analyzed using the National Research Council Canada (NRC) emotion lexicon (NRCLex) to assign scores for emotions such as anger, disgust, fear, joy, sadness, and surprise. Subsequently, these reviews are further analyzed using term frequency-inverse document frequency (TF-IDF) for classification. The proposed algorithm achieves 90% accuracy, indicating its effectiveness in classifying sentiments in movie reviews. The study's findings confirm the potential of the SVM algorithm for broader applications in sentiment analysis and natural language processing. Additionally, integrating emotion detection enhances understanding of nuanced emotional content, providing a comprehensive approach to sentiment classification in large datasets. Creator An entity primarily responsible for making the resource Aditiya Hermawan1, Rico Yusuf1, Benny Daniawan2, Junaedi2 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 Oct 18, 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 Emotion detection Movie review Sentiment analysis Support vector machine Text mining https://repository.horizon.ac.id/files/original/0b7eda5504ed6e2ba0964edd6030ea99.pdf a50bb4497c1b42c65dc984ddeb155f22 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.1 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 Visible light communication for rapid monitoring of environmental changes using thin film solar cells Subject The topic of the resource Data transmission Energy efficiency Environmental monitoring Thin film solar cells Visible light communication Description An account of the resource This study investigates the use of visible light communication (VLC) for rapid environmental monitoring by leveraging thin film solar cells as signal receivers. VLC, which employs visible light for data transmission, presents an energy-efficient and eco-friendly approach for real-time monitoring. Thin-film solar cells, recognized for their efficiency and low-light performance, function both as environmental sensors and VLC signal receivers. We conducted experiments to evaluate the system's performance across various environmental conditions, such as light intensity and temperature changes. Our findings indicate that thin-film solar cells can swiftly and accurately detect environmental changes while maintaining a low bit error rate for VLC data. The system also shows high responsiveness to rapid light variations, making it well-suited for dynamic monitoring tasks like air quality, humidity, and forest fire detection. This research highlights VLC technology's significant potential for environmental monitoring applications requiring quick, real-time data transmission, and energy efficiency with thin-film solar cells. The integration of this technology promises to enhance environmental monitoring systems, contributing to climate change mitigation and improved environmental management, and sets the stage for developing advanced, sustainable solutions in wireless communication and ecological monitoring. Creator An entity primarily responsible for making the resource Soni Prayogi1,2, Wahyu Kunto Wibowo1,2 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 Oct 27, 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 Data transmission Energy efficiency Environmental monitoring Thin film solar cells Visible light communication https://repository.horizon.ac.id/files/original/89287afd736c1b50b1f3493b7a77093d.pdf 2b7c6c93eded931dd9d4e7a9d78a5a78 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.1 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 Fire detection and surveillance system with cloud-based alert to enhance safety in commercials and home Subject The topic of the resource Automation Fire detection Home safety Internet of things Water pump Description An account of the resource This study presents a comprehensive internet of things (IoT) solution for improving home automation and fire safety. It describes the design and construction of an all-inclusive house fire extinguishing system using an ESP8266 microcontroller to supply water, detect fires in real time, and monitor them remotely. The IoT fire safety system is currently under investigation for its potential to prevent fires. The system includes a servo motor for precise water distribution, an ESP8266 microcontroller for smooth performance and networking, a water pump for timely fire suppression, and a fire sensor for detecting heat and flames. The system architecture, software integration, and hardware parts are detailed. Field testing has shown that fire detection and suppression systems can effectively detect fires, reducing risks and damages associated with fires. The discussion section discusses the pros and cons of the recommended strategy, implications for home fire safety and automation, and areas for further research and development. The IoT-based domestic fire extinguishing system combines modern technologies with quick response time, real-time monitoring, and fast action capacity, addressing the urgent need for increased home fire safety measures. Creator An entity primarily responsible for making the resource Siddharth Swami1, Rajesh Singh2, Anita Gehlot2, Jasvinder Kaur3, Deepti Sharma4, Sameer Dev Sharma5, Devendra Singh6 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 Oct 27, 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 https://repository.horizon.ac.id/files/original/4a28d9545b4aad76b32d98ee4f91512f.pdf 98fa4f429b32f38ff7d71cd0d420b087 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.1 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 Hybrid optimization algorithm for resource-efficient and data-driven performance in agricultural IoT Subject The topic of the resource Data accuracy Data driven performance Internet of things network Optimization algorithms Resource efficiency Description An account of the resource The agricultural sector is undergoing a significant transformation with the adoption of the agricultural internet of things (IoT), yet it faces persistent challenges in optimizing resource efficiency and data-driven performance due to limitations in current optimization algorithms. This research assesses the effectiveness of four prominent algorithms such as ant colony optimization (ACO), genetic algorithms (GA), particle swarm optimization (PSO), and artificial bee colony (ABC) in addressing these challenges within agricultural IoT (AIoT). Introducing a novel hybrid optimization algorithm (HOA), we aim to overcome these limitations by prioritizing both resource efficiency and data-driven performance. Through a thorough evaluation, HOA demonstrates its superiority in enhancing both aspects, thereby establishing itself as a compelling solution for AIoT applications. The introduction of HOA sets the stage for sustainable, cost-effective, and data-driven precision agriculture, significantly enhancing resource efficiency and data accuracy within the IoT network. Creator An entity primarily responsible for making the resource Depa Ramachandraiah Kumar Raja1, Zuraida Abal Abas2, Goshtu Hemanth Kumar3, Chakana Ravindra Murthy4, Venappagari Eswari5 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 Nov 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