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Innovative web-based microteaching model: To improve the teaching skills of prospective informatics teachers in vocational high schools

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Social Sciences and Humanities Open 2025年 11卷

作者： Sugihartini, Nyoman Elmunsyah, Hakkun Nurhadi, Didik Rahmawati, Yuni Vocational Education Doctoral Program Post Graduate School Universitas Negeri Malang Malang Indonesia Informatics Education Study Program Department of Informatics Faculty of Engineering and Vocational Universitas Pendidikan Ganesha Bali Indonesia

Prospective informatics teachers in vocational schools (VS) must master content knowledge, pedagogical skills, and technology integration to achieve learning goals. Therefore, informatics teachers in VS need to innovate to increase students' creativity. However, many prospective informatics teachers experience difficulties in this regard. This research aims to develop and implement the Web-Based Microteaching (WBM) model to improve the teaching skills of prospective informatics teachers at VS. This study uses research and development methods with the ADDIE model approach. The research results show that the WBM model effectively improves prospective informatics teachers' teaching skills, especially in planning lessons and practising basic teaching skills. This innovation is a relevant learning solution in the digital era and positively contributes to the professional development of informatics teachers. The WBM model is an innovation to provide a more intensive and structured practical experience by utilizing web technology as the main media. Through web-based learning, prospective teachers can access material, exercises and feedback independently, which is expected to accelerate pedagogical mastery. © 2025 The Authors

关键词： Microteaching model Prospective informatics teacher Teaching skills Vocational high schools Web-based

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Deep Learning for Cyber Security: Deephoax Detection

Deep Learning for Cyber Security: Deephoax Detection

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： SY. Yuliani Yakub Rivo Juicer Wowor David Agustriawan Irmawati Dinar Ajeng Kristiyanti P.M. Winarno Rosilah Hassan Roshayu Mohamad Randy Zahran Razzaq Department of Informatics Universitas Multimedia Nusantara Tangerang Indonesia Information System Study Program Universitas Multimedia Nusantara Tangerang Indonesia Center for Cyber Security (CYBER) Universiti Kebangsaan Malaysia Selangor Malaysia College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Informatics UIN Sultan Maulana Hasanuddin Banten Indonesia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

The increasing prevalence of deep hoaxes, such as fake news and phishing schemes, poses a significant threat to cybersecurity, undermining trust and spreading misinformation. In Indonesia, surveys indicate that more than 60% of people exposed to hoax news believe that it is true, emphasizing the urgent need for robust detection methods. Traditional cybersecurity approaches often struggle to keep pace with the growing scale and sophistication of these attacks. To address this challenge, this research investigates the use of deep learning techniques, specifically focusing on text-based hoax detection in the Indonesian language. The study fine-tunes IndoBERT, a pretrained deep learning model optimized for Indonesian text, to enhance the accuracy and scalability of hoax detection. The IndoBERT model was trained on a balanced dataset of 29,552 articles, comprising both hoax and real news content, collected from the Mafindo API and Kaggle's Indonesia News Dataset. The model was fine-tuned using supervised learning and evaluated using several key metrics, including accuracy, F1-score, precision, and recall. The results demonstrate that IndoBERT outperforms existing state-of-the-art approaches, achieving an accuracy of 98.51%, an F1-score of 98.44%, and a precision of 98.23% on the test set. These results highlight the effectiveness of IndoBERT for hoax detection, which offers a scalable solution to improve cybersecurity defenses against deceptive content. This research contributes to the integration of advanced deep learning models into cybersecurity systems, addressing the evolving landscape of cyber threats.

关键词： Deep learning Measurement Training Surveys Adaptation models Technological innovation Accuracy Computer security Fake news Videos

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Connectivity of single neurons classifies cell subtypes in mouse brains

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Nature Methods 2025年第4期22卷 861-873页

作者： Liu, Lijuan Yun, Zhixi Manubens-Gil, Linus Chen, Hanbo Xiong, Feng Dong, Hongwei Zeng, Hongkui Hawrylycz, Michael Ascoli, Giorgio A. Peng, Hanchuan New Cornerstone Science Laboratory SEU-ALLEN Joint Center Institute for Brain and Intelligence Southeast University Nanjing China School of Biological Science & Medical Engineering Southeast University Nanjing China Tencent AI Lab Bellevue WA United States UCLA Brain Research and Artificial Intelligence Nexus Department of Neurobiology David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States Allen Institute for Brain Science Seattle WA United States Center for Neural Informatics Bioengineering Department and Neuroscience Program Krasnow Institute for Advanced Study George Mason University Fairfax VA United States Shanghai Academy of Natural Sciences Fudan University Shanghai China

Classification of single neurons at a brain-wide scale is a way to characterize the structural and functional organization of brains. Here we acquired and standardized a large morphology database of 20,158 mouse neurons and generated a potential connectivity map of single neurons based on their dendritic and axonal arbors. With such an anatomy–morphology–connectivity mapping, we defined neuron connectivity subtypes for neurons in 31 brain regions. We found that cell types defined by connectivity show distinct separation from each other. Within this context, we were able to characterize the diversity in secondary motor cortical neurons, and subtype connectivity patterns in thalamocortical pathways. Our findings underscore the importance of connectivity in characterizing the modularity of brain anatomy at the single-cell level. These results highlight that connectivity subtypes supplement conventionally recognized transcriptomic cell types, electrophysiological cell types and morphological cell types as factors to classify cell classes and their identities. © The Author(s) 2025.

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Convolutional Neural Network for Cataract Clasification Using Primary Dataset 9

Convolutional Neural Network for Cataract Clasification Usin...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Totok Suryawan, I Gede Putra, I Wayan Murmayasa Adi Informatics Engineering Study Program Faculty of Technology and Informatics Denpasar Indonesia

ISBN: (纸本)9798350383010

Cataracts are the main factor causing blindness in Indonesia. Traditional diagnostic methods involving clinical examination by an ophthalmologist using a slit-lamp and visual tests have many challenges such as limited resources, time, and medical expertise may not be available in all locations, especially in less developed areas. Utilizing medical image technology is a wise choice because it allows early detection and monitoring of the development of this cataract disease Technology is needed to identify medical images that have valuable information so that early detection or monitoring of cataract development can be carried out. CNN as a part of deep learning has the ability to perform automatic feature extraction so that it can identify eye images. A CNN model for recognizing cataracts in eye images taken directly from Bhayangkara Hospital in Denpasar Bali has been developed in this research. The model developed is equipped with a dropout layer technique to prevent overfitting. The test results show that the CNN developed in this research produces very good performance with an average accuracy level of 92% on public dataset images, and 94% on primary dataset images. Tested using the primary dataset and public dataset, this CNN model shows very good performance because it can correctly identify an average of 94% of eye images in the primary dataset, and 92% of eye images in the public dataset.. © 2024 IEEE.

关键词： Convolutional neural networks

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Secure E-Voting System for Student Associations in Private University Using Paillier Algorithm 21

Secure E-Voting System for Student Associations in Private U...

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21st IEEE Student Conference on Research and Development, SCOReD 2023

作者： Tenusa, Ivan Surbakti, Eunike Endariahna Tobing, Fenina Adline Twince Kusnadi, Adhi Universitas Multimedia Nusantara Informatics Study Program Informatics and Engineering Faculty Indonesia

ISBN: (纸本)9798350318821

The representatives of student associations at Private University are entrusted with the responsibility of empowering, facilitating, and nurturing the growth of the students, lecturers, and alumni within their respective programs. If the elected representatives do not align with the general consensus, problems are bound to arise. Therefore, student participation during the annual voting period is of utmost importance. When voting participation is low, the risk of the majority voice not being heard increases. Data from the General Election Commission in the private university reveals that among the four student associations in the Tech and IT Faculty, three of them have a participation rate lower than 50%.This research aims to develop an E-Voting system for Student Associations within the university, with the intention of replacing the currently utilized Google Form system and enhancing student participation in future voting periods. While constructing an electronic voting system, the security and confidentiality of participants are vital. The Paillier homomorphic encryption algorithm is employed in the system to encrypt and decrypt voting messages sent by voters, ensuring their confidentiality. This algorithm was chosen due to its relative recency, speed, and relevance to this research in comparison to other algorithms. A user acceptance test conducted using the Technology Acceptance Model (TAM) has demonstrated that the application has been successfully developed, with user assessments of 87.50% for perceived ease of use, 72.27% for perceived usefulness, 74.74% for behavioral intention, and 86.46% for actual usage. © 2023 IEEE.

关键词： Students

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Design Technology of Geographical Information System of Rice Pandanwangi Cianjur Prediction Factors Products 1

Design Technology of Geographical Information System of Rice...

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1st Paris Van Java International Seminar on Computer, Science, engineering, and Technology, PVJ ISComSET 2020

作者： Musrifah, Ai Adikusuma, Andri Sutono Informatics Engineering Study Program Faculty of Engineering University of Suryakancana Indonesia

Cianjur is one of the renowned rice producers, particularly with the presence of Pandanwangi rice that can be grown in the Cianjur area. Pandanwangi has become one of Cianjur City's icons, the rice supremacy created by Pandanwangi Cianjur's tenacity and distinctive scent (pandanus fragrance). The Interest of pandanwangi was very high, both within the town of Cianjur and outside the town of Cianjur, but this was inversely proportional to the desires of the groups of farmers who appeared to prefer to plant rice other than Pandanwangi, on the grounds that the planting of Pandanwangi had a long harvest and that the likelihood of crop failure was also very high. Problems with information flows that are not well integrated between farmers' groups, farmers' group organizations, and the Agriculture Department in adjusting land details, planting time, planting location, estimating total harvest and harvesting time. To solve this problem, a framework must be built that can integrate the information needed for Pandanwangi Cianjur Harvest Prediction with Pandanwangi farmers' regional analysis based on geographic information system technology, using Google Maps Service. © Published under licence by IOP Publishing Ltd.

关键词： Harvesting

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Optical character recognition and long short-term memory neural network approach for book classification by librarians 6

Optical character recognition and long short-term memory neu...

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6th International Conference on Mathematics, Science, and Education, ICMSE 2019

作者： Rosita, Y.D. Sukmaningtyas, Y.N. Study Program of Informatics Engineering Universitas Islam Majapahit Indonesia

The book is classified by librarians that use Decimal Dewey Classification (DDS) System. It is used for cataloging and indexing books. DDC has three divisions, a ten, a hundred, and a thousand. The book subject is reflected in each division. Commonly, to know the book content, librarians read the book title. Then, they identify the book index in DDC system. Nevertheless, it requires more time. To read the book title, Optical Character Recognition (OCR) aids them to get the book title efficiently that convert the image of the book cover into the text-editable. Librarians use a web camera to scan the book cover, especially the book title area. There are three steps for pre-processing, the lowercase changing, the useless word removing, and tokenizing. To detect the book categories, Long Short-Term Memory Neural Network is good implemented in this research. It is almost used for text classification. In this research, It gives high performance that achieves more than 92% accurately. © Published under licence by IOP Publishing Ltd.

关键词： Brain

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Work system method as an e-learning design framework in private universities 3

Work system method as an e-learning design framework in priv...

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3rd International Conference on Vocational Education and Technology, IConVET 2020

作者： Supriana, C.E. Informatics Engineering Study Program Pasundan University Bandung Indonesia

The development of information and communication technology, especially the internet provides opportunities to improve the ability of lecturers to deliver courses to students without being limited by distance and time. This can be done by utilizing e-learning which will accelerate the transfer of knowledge and skills. E-learning is distance education that utilizes various digital tools and platforms. These tools and platform are used to support interaction, lectures and delivery of instructions between lecturers and students. This study will discuss the use of work system methods and the use of e-learning technology in a business entity. The work system methods are used to design information systems. Meanwhile e-learning technology in a business entity serves lecturers and students, conduct lectures at private universities in increasing knowledge dissemination, educational affordability and the use of technology and information as its support. The result of this research is the works system method model which refers to the analysis of data from the internet accessibility survey of students, analysis of its components, the system development cycle, the revision of the vision, mission and resources in private universities and recommendations of opportunities for the development of higher quality e-learning implementation. © Published under licence by IOP Publishing Ltd.

关键词： Students

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Villages Status Classification Analysis Involving K-Means Algorithm to Support Kementerian Desa Pembangunan Daerah Tertinggal dan Transmigrasi Work programs 1

Villages Status Classification Analysis Involving K-Means Al...

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1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Hasugian, Paska Marto Hutahaean, Harvei Desmon Sinaga, Bosker Sriadhi Silaban, Saranom Informatics Engineering Study Program STMIK Pelita Nusantara Computer Science and Informatics Education Study Program Faculty of Engineering Universitas Negeri Medan Medan20221 Indonesia

Data mining is a technique of extracting information that has not been known before in a collection of data in the database. Data mining has been applied in various fields that require extracting information, some of the work that can be generated with data mining is classification, prediction, and data grouping. In this study, an analysis of village data collection was carried out to explore the potential or knowledge of the data that has been presented with the main objective of producing a grouping of village status. To support clustering or grouping activities the K-means algorithm used with the general process is to carry out the modeling process without supervision and is one of the methods for grouping data with a system partition, with the principle of allocating each data to the centroid or the closest average, work steps conducted in support of this research is to collect data related to the analysis of data grouping and proceed with the calculation process in accordance with the work steps of the K-means algorithm, the amount of data used as a test of 303 villages scattered in the old Padang regency. The results of calculations by displaying a new group of Cluster 0 is occupied by 120 villages, cluster 1 with a total data of 123 villages, Cluster 2 with a total of 6 villages, cluster 3 with a total of 33 villages while cluster 4 with a total of 21. © 2020 Published under licence by IOP Publishing Ltd.

关键词： K-means clustering

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Designing knowledge sharing systems to support integrated eco-city planning and management 5

Designing knowledge sharing systems to support integrated ec...

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5th International Seminar on Sustainable Urban Development, ISoSUD 2020

作者： Supriana, C.E. Informatics Engineering Study Program Faculty of Engineering Pasundan University Bandung Indonesia

The local government of a city as an organization depends on decision makers in government management to support the vision and mission of the city to achieve eco-city goals based on data and information from several knowledge domains. Knowledge management is seen as an increasingly important scientific discipline that encourages the creation, sharing and utilization of knowledge in an organization. Knowledge management is also related to the concept of intellectual capital, the most valuable organizational resource. Knowledge sharing systems are designed to help decision makers and stakeholders share their knowledge. Most knowledge management systems in organizations are designed to share the explicit knowledge of individuals, organizations and finally distributed to the community. This research is the approach in the development model that can be used in the governance to achieve eco-city goals. Complex knowledge about eco-cities requires policies that are based on current knowledge and can be implemented immediately with a thorough understanding of all aspects. The results of this study are understanding the problem of knowledge sharing as a valuable organizational asset, designing a knowledge sharing systems model, and improving systems to improve the quality of knowledge. © Published under licence by IOP Publishing Ltd.

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