1 10 10 https://repository.horizon.ac.id/files/original/7352f83184cb748625a2b7552b31649f.pdf 72aff431a6d0a4b93efa7c46e544d4f5 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. 14 NO. 3 (2025): SEPTEMBER 2025 Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Topics, Trends, and Sentiments in Software Testing: An Analysis of Developers’ Engagement on Stack Overflow Subject The topic of the resource Software Testing, Stack Overflow, Topic Modeling, Sentiment Analysis, Developer Engagement, Machine Learning Testing, ChatGPT Description An account of the resource This study investigated software testing discussions on Stack Overflow from 2020 to 2024 to uncover key trends, topics, and developer sentiments. 14 key topics, including unit testing, machine learning testing, mobile testing (especially Flutter), and Docker testing were identified. The study revealed a decline in developer engagement, as the number of posts answered and with accepted answers decreased, particularly after 2022. Sentiment analysis showed a predominance of negative sentiments across most topics, especially in mobile and machine learning testing. While some topics like machine learning testing initially saw positive sentiment, this shifted toward frustration as the years progressed. These findings suggest that the rise of AIbased tools, such as ChatGPT, has affected the way developers engage with traditional forums like Stack Overflow. The decline in engagement and the prevalence of negative sentiments highlight the challenges developers face in software testing. This research points to the need for further investigation into how AI tools influence developer behavior and their reliance on peer support platforms. Additionally, it suggests exploring how sentiment analysis can be integrated into software testing tools to better address developer frustrations and improve support for testing emerging technologies. The study provides insights that could guide the development of more effective tools and frameworks to enhance the software testing process Creator An entity primarily responsible for making the resource Anthony Wambua Wambua Source A related resource from which the described resource is derived https://ijcit.com/index.php/ijcit/article/view/545 Publisher An entity responsible for making the resource available Department of Computer Science, Daystar University, Athi River, Kenya Date A point or period of time associated with an event in the lifecycle of the resource september 2025 Contributor An entity responsible for making contributions to the resource Fajar bagus W 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/86769aaf0aae824b63694df6186384b2.pdf 3a3f32cc39d49eb446f35a056f017c6b 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. 14 NO. 3 (2025): SEPTEMBER 2025 Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Development of an NFC-Based Tracking System for Production, Inventory, and Maintenance in Manufacturing Subject The topic of the resource NFC; Production Control; Inventory Management; Industry 4.0; Circular Economy Description An account of the resource This paper presents the development, implementation, and evaluation of a computational system for integrating production control, inventory management, and maintenance operations in a manufacturing company using Near Field Communication (NFC) technology. The proposed solution leverages NFC tags as a core element to ensure real-time tracking of products and components throughout the manufacturing process. The system architecture consists of NFC tags for product identification, a custom mobile application for data acquisition, and a centralized database for storage and analysis. The system workflow enables operators to register, monitor, and update the status of products using mobile devices, enhancing traceability, efficiency, and data accuracy. The solution was validated in a real-world manufacturing environment through functional tests and a System Usability Scale (SUS) questionnaire, which yielded an average usability score of 72.08, indicating a good level of user satisfaction. These results demonstrate the feasibility, usability, and practical benefits of the proposed system, which contributes to the digital transformation of industrial operations and aligns with Industry 4.0 principles. Creator An entity primarily responsible for making the resource Camile Lopes Franco de Macedo Source A related resource from which the described resource is derived https://ijcit.com/index.php/ijcit/article/view/527 Publisher An entity responsible for making the resource available Department of Informatics, Federal Institute of Education, Science and Technology of São Paulo (IFSP), Bragança Paulista, SP, Brazil Date A point or period of time associated with an event in the lifecycle of the resource september 2025 Contributor An entity responsible for making contributions to the resource Fajar bagus W 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/f7891fa230860a819d925efe54bee7af.pdf d5267aa96a6f85a6049bb1e82fd46135 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. 14 NO. 3 (2025): SEPTEMBER 2025 Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 with Deep Learning Methods For Predicting Stock Prices Subject The topic of the resource Stock price, Linear Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN Description An account of the resource Recently, machine learning has been an essential tool for analysis in diverse fields, including science, sports management, and economics. In particular, the stock market comprises a complex network of buyers and sellers engaged in stock trading. So, predicting stock prices has been developed using machine-learning techniques to significantly enhance such forecasts' accuracy. Recent advancements have improved the performance of several algorithms, such as Linear Regression, Support Vector Machines (SVM), and K-nearest neighbors (KNN) to predict stock prices. Stock price datasets typically contain information such as opening and closing prices, high and low values, dates, trading volume, and adjusted closing prices provided by Yahoo Finance. Based on the data, this research evaluates the prediction accuracy of each machine-learning method and presents the results through data visualizations, including box plots and tables. The compiled results will assist in identifying the most effective model for stock price prediction. Creator An entity primarily responsible for making the resource Colton Nutter, Nayeong Kong, Seonguk Kim Source A related resource from which the described resource is derived https://ijcit.com/index.php/ijcit/article/view/532 Publisher An entity responsible for making the resource available Division of Natural Science, Applied Science, and Mathematics Defiance College Defiance, OH 43512, USA Date A point or period of time associated with an event in the lifecycle of the resource september 2025 Contributor An entity responsible for making contributions to the resource Fajar bagus W 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/221010e9f9d4fa5f4ae0a47d8d9f5a26.pdf c86d693612efbf47358665ab257cd670 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. 14 NO. 3 (2025): SEPTEMBER 2025 Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Lightweight Proof-of-Stake Models for PrivacyPreserving Telemedicine Systems: A Systematic Review Subject The topic of the resource Proof of Stake (PoS), Telemedicine Systems, Lightweight Models, Data Privacy, Voting Mechanisms, Data Encryption Description An account of the resource Proof of Stake (PoS) models are energy-efficient and require limited computational power. These features are critical in telemedicine environments, where resource-constrained devices must handle sensitive data securely. The growing need for auditable and privacy-preserving data storage in telemedicine underscores the importance of PoS models optimized for lightweight devices while complying with strict regulatory requirements, such as the Health Insurance Portability and Accountability Act (HIPAA).This study was guided by two research questions: (i) Which PoS models are lightweight and suitable for telemedicine? and (ii) What features make lightweight PoS models effective for privacy and efficiency in telemedicine? To address these questions, a systematic literature review (SLR) guided by the PICOC framework was conducted to investigate lightweight PoS models that can enhance privacy in telemedicine systems. Out of 2,394 papers studies screened, 55 were included in the analysis. The findings identified Algorand, Ouroboros Praos, Tendermint, Nxt, and Casper CBC as promising candidates. Key enabling features included lightweight voting mechanisms, such as Byzantine Agreement protocols and Verifiable Random Functions, as well as cryptographic techniques like symmetric encryption and multiparty computation. Performance metrics evaluated included latency, throughput, energy efficiency, and battery consumption, with Grey Relational Analysis ranking Algorand highest due to its low latency, high throughput, and minimal energy consumption. Creator An entity primarily responsible for making the resource Denis Wapukha Walumbe Source A related resource from which the described resource is derived https://ijcit.com/index.php/ijcit/article/view/508 Publisher An entity responsible for making the resource available Department of Information Technology, Murang’a University of Technology Murang’a, Kenya Date A point or period of time associated with an event in the lifecycle of the resource september 2025 Contributor An entity responsible for making contributions to the resource Fajar bagus W 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/b392903e5237f590e727eb333afb5104.pdf f5825f78366809304ff11311b392853b 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. 14 NO. 3 (2025): SEPTEMBER 2025 Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Deep Learning Models for Detection of Lumpy Skin Disease with emphasis on Shifted Window Transformers Subject The topic of the resource Deep learning, Lumpy Skin Disease (LSD), Vision Transformer, CNN, livestock diagnosis, image analysis, model explainability, dataset imbalance, veterinary AI Description An account of the resource Lumpy Skin Disease (LSD) in cattle is an increasingly prevalent viral infection with significant economic impact. Traditional detection methods are often labor-intensive and delayed. In this study, five state-of-the-art deep learning (DL) architectures—ResNet50, EfficientNetB0, MobileNetV2, Vision Transformer (ViT-B16), and Swin Transformer Tiny (Swin-T)— were evaluated and compared for image-based LSD classification. Publicly available Kaggle datasets of infected and healthy cattle were used. All models were fine-tuned using transfer learning and tested for classification accuracy, F1-score, inference time, explainability (via Grad-CAM), and real-world deployability. Results show that Swin-T achieved the highest classification accuracy of 95.3%, while MobileNetV2 emerged as the most deployment-friendly model. Grad-CAM visualizations confirmed that transformer-based models captured relevant lesion features with greater spatial sensitivity than CNNs. The study highlights the promise of hybrid transformer-CNN models for practical livestock diagnostics, especially in resource-constrained environments Creator An entity primarily responsible for making the resource George Mwangi Muhindi Source A related resource from which the described resource is derived https://ijcit.com/index.php/ijcit/article/view/533 Publisher An entity responsible for making the resource available Department of Information Technology Murang’a University of Technology Murang’a, Kenya. Date A point or period of time associated with an event in the lifecycle of the resource september 2025 Contributor An entity responsible for making contributions to the resource Fajar bagus W 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/9507220515dc0fbf47ece92b0000c64c.pdf 418d321cdceb09c1ef0ad80708489d6f 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. 14 NO. 3 (2025): SEPTEMBER 2025 Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Trade-Off Between Anonymity and Accountability in Blockchain: A Framework for Secure and Compliant Systems Subject The topic of the resource Blockchain, Anonymity, Accountability, Privacy-Preserving Technologies, Regulatory Compliance, Zero-Knowledge Proofs, Adaptive Privacy-Accountability Control (APAC). Description An account of the resource Blockchain technology has revolutionized digital transactions by offering decentralization, transparency, and immutability. However, its inherent transparency often conflicts with the need for user privacy and anonymity, raising significant concerns regarding accountability, especially in regulatory and legal contexts. This study explores the delicate balance between anonymity and accountability in blockchain systems, proposing a framework that ensures both privacy and compliance with regulatory requirements. The research addresses key challenges in balancing these two aspects, evaluates the effectiveness of existing privacy-preserving technologies such as zero-knowledge proofs and ring signatures, and introduces the PrivacyAccountability Balanced Blockchain (PABB) Framework. This framework integrates Selective DeAnonymization, Self-Sovereign Identity (SSI), and the Adaptive Privacy-Accountability Control (APAC) Algorithm to dynamically adjust privacy levels based on regulatory conditions. Through theoretical analysis, mathematical modeling, and empirical validation, preserving privacy for 92% of transactions while enabling selective de-anonymization in high-risk cases, the study demonstrates that the APAC Algorithm effectively balances privacy and compliance needs. The findings suggest that privacy-conscious blockchain systems can coexist with accountability mechanisms, paving the way for ethical and legally sound blockchain applications. The study concludes that the PABB Framework offers a practical and scalable approach to achieving this balance, fostering trust among users and regulators alike. Creator An entity primarily responsible for making the resource Idris Olanrewaju Ibraheem Source A related resource from which the described resource is derived https://ijcit.com/index.php/ijcit/article/view/503 Publisher An entity responsible for making the resource available Department of Computer Science Al-Hikmah University Ilorin, Nigeria Date A point or period of time associated with an event in the lifecycle of the resource september 2025 Contributor An entity responsible for making contributions to the resource Fajar bagus W 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/f7cec0c10c41cfb404e3c91f43b49d77.pdf bf4abbd79b91bf2288abba0c9563ba3a 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. 14 NO. 3 (2025): SEPTEMBER 2025 Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Assessing IPv6 Readiness and Adoption in Palestine, A Case Study with Strategic Recommendations Subject The topic of the resource Ipv6 adoption, IPv6 Challenges, IPv6 Readiness, Palestine, ISPs, enterprises, end user Description An account of the resource This research examines the current state of IPv6 adoption in Palestine, comparing it to the global and regional adoption trends, particularly in Arab countries. While the global IPv6 adoption rate exceeds 45%, and Arab countries average 12.79%, Palestine remains at a negligible adoption rate. Through a mixed-method approach that includes experiments, technical analysis, and stakeholder interviews, the study highlights the readiness of end-user devices for IPv6, primarily due to international compliance standards. However, significant challenges exist within Internet Service Providers (ISPs) and large enterprises in Palestine, such as dual-stack resource demands, insufficient technical expertise, and lack of customer demand. This paper underscores the urgent need for a national strategy, stakeholder collaboration, and increased awareness to accelerate IPv6 transition in Palestine. Recommendations are made to address these obstacles and position Palestine for future compatibility in the global internet ecosystem Creator An entity primarily responsible for making the resource Ihab Y Atieh Source A related resource from which the described resource is derived https://ijcit.com/index.php/ijcit/article/view/500 Publisher An entity responsible for making the resource available College of Graduate Studies, Computer Science Department The University of Jordan Date A point or period of time associated with an event in the lifecycle of the resource september 2025 Contributor An entity responsible for making contributions to the resource Fajar bagus W 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/9101e4a71c73cd8a268053c301f7797a.pdf d3ed56592e850507220aa5e7b94cbf35 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. 14 NO. 3 (2025): SEPTEMBER 2025 Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Automated Techniques for Detecting Healthcare Associated Infections: A Review Subject The topic of the resource Healthcare-Associated Infections; Machine Learning; Deep Learning; Natural Language Processing; Transformer; Electronic Health Records Description An account of the resource Automated detection of Healthcare-Associated Infections (HAIs) faces major obstacles due to unclear medical documentation, scarcity of well-annotated data, and multiple symptoms that overlap between HAIs. This review investigates recent advances in using classical machine learning, deep learning, transformers, and natural language processing (NLP) methods in detecting healthcare-associated infections. It examines empirical studies from 2019 to 2025, focusing on models' performance based on various metrics, data issues, and ethical considerations. The study sought to assess and compare the performance of natural language processing (NLP) approaches of detecting Healthcare-Associated Infections (HAIs). Ethical and technical concerns such as data privacy and data imbalance, are critical barriers to implementation of NLP to detection of HAIs. The review underscores the promise of NLP to detection of HAIs while emphasizing the need for standardized metrics for evaluating HAI detection model and ethical frameworks of handling the datasets Creator An entity primarily responsible for making the resource Joseph Karuri Mwaura Source A related resource from which the described resource is derived https://ijcit.com/index.php/ijcit/article/view/540 Publisher An entity responsible for making the resource available School of Pure and Applied Sciences Kirinyaga University, Kenya Email: karurimwaura [AT] gmail.com Date A point or period of time associated with an event in the lifecycle of the resource september 2025 Contributor An entity responsible for making contributions to the resource Fajar bagus W 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/12f80be6da552e1890be6058ed7eaedf.pdf 7ed03dd9108fbc81242286a96a703ab8 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. 14 NO. 3 (2025): SEPTEMBER 2025 Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Lightweight 3D Convolutional Network for Hyperspectral–LiDAR Patch Classification Subject The topic of the resource imagery, LiDAR, 3D-CNN, data fusion, patch classification, class imbalance, remote sensing Description An account of the resource We propose a simple yet effective threedimensional convolutional neural network (3D-CNN) for urban land-cover classification using co-registered hyperspectral imagery (HSI) and LiDAR data. The network treats the entire spectral-LiDAR stack as a three-dimensional volume and uses a series of 3×3×3 convolutions to capture both spectral and spatial context simultaneously. LiDAR elevation data is added as an extra channel in the input. During preprocessing, each hyperspectral band and LiDAR DSM are normalized to zero mean and unit variance. Training uses small local patches (P×P) centered on labeled pixels, with random flips and 90-degree rotations, called dihedral augmentation, applied across all channels. To address class imbalance, inverse-frequency class weighting and label smoothing are included in the cross-entropy loss. Evaluation on the Houston2013 dataset shows that the model achieves high accuracy, a single model reaches an Overall Accuracy (OA) of about 0.90 and an Average Accuracy (AA) of about 0.92 over five runs. An ensemble of five runs improves these results to OA ≈ 0.912, AA ≈ 0.928, and a kappa coefficient (κ) of approximately 0.904. Classes with distinctive spectral and spatial signatures, like water, synthetic grass, and tennis courts, reach nearly 100% recall. Meanwhile, classes with similar appearances, such as highway and road, show higher confusion, with highway recall around 46.9%. These results confirm that combining spectral and three-dimensional structural information significantly enhances accuracy in urban classification Creator An entity primarily responsible for making the resource Junhua Ku Source A related resource from which the described resource is derived https://ijcit.com/index.php/ijcit/article/view/538 Publisher An entity responsible for making the resource available Qiongtai Normal University, Haikou, China Date A point or period of time associated with an event in the lifecycle of the resource september 2025 Contributor An entity responsible for making contributions to the resource Fajar bagus W 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/a30b76f5d71969f5632f63ae1693e6ea.pdf 72fce9f9089fe358412e783135dd30cf 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. 14 NO. 3 (2025): SEPTEMBER 2025 Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. 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 Aspect-Based Sentiment Analysis for Turkish Reviews Using Token and Sequential Classification Methods Subject The topic of the resource token classification; sequential model classification; aspect term; aspect-based sentiment analysis; deep learning, turkish Description An account of the resource Aspect-Based Sentiment Analysis (ABSA) aims to identify sentiments expressed toward specific aspects or attributes of entities in text. This study addresses the under-explored area of ABSA in the Turkish language by extracting aspect terms (targets) and their categories from customer reviews and determining the sentiment polarity for each aspect. Turkish, being a morphologically rich and structurally complex language, poses unique challenges that often hinder the direct application of methods developed for other languages. Hence, developing sentiment analysis approaches tailored to Turkish is of significant importance. We propose a two-stage pipeline: a token-level classification to recognize aspect terms and assign them to one of 12 predefined aspect categories, followed by a sequence-level (sentence-level) classification to predict sentiment (positive, negative, or neutral) for each identified aspect. We fine-tuned five transformer-based language models (BERT, ConvBERT, ELECTRA, DeBERTa, and DistilBERT) for aspect term and category extraction, and four models (BERT, ConvBERT, ELECTRA, DistilBERT) for sentiment classification. Experimental results on the SemEval-2016 Turkish ABSA Restaurant dataset show that the BERT model achieved the highest accuracy (92.20%) for aspect term and category identification, closely followed by ConvBERT (91.68%). For sentiment analysis, ConvBERT performed best with an accuracy of 86.91%, outperforming ELECTRA (85.34%), BERT (82.75%), and DistilBERT (77.48%). These findings demonstrate that pretrained transformer models can effectively handle fine-grained sentiment analysis in Turkish, substantially improving on previous approaches. The proposed pipeline and comparative results provide a novel benchmark for Turkish ABSA, with potential applications in analyzing Turkish customer feedback to glean actionable insights. Creator An entity primarily responsible for making the resource Metin Bilgin Source A related resource from which the described resource is derived https://ijcit.com/index.php/ijcit/article/view/485 Publisher An entity responsible for making the resource available Department of Computer Engineering Bursa Uludağ University of Turkey Bursa,Turkey Date A point or period of time associated with an event in the lifecycle of the resource september 2025 Contributor An entity responsible for making contributions to the resource Fajar bagus W 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