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检索条件"机构=Computer Science Department BINUS Graduate Program - Doctor of Computer Science"

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Mapping the Landscape of Social Learning: Integrating Key Success Factors, Challenges, and Digital Platforms

Mapping the Landscape of Social Learning: Integrating Key Su...

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International Conference on Information Management and Technology (ICIMTech)

作者： Marcel Meyliana Harco Leslie Hendric Spits Warnars Tirta Nugraha Mursitama Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Doctor of Research in Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

This research develops a conceptual model to understand the influence of digitalization on social learning, especially in the context of community empowerment. This model maps the relationship between factors that influence success, challenges faced, and variations in the use of digital platforms in social learning. By using a literature study approach, the research results show that effective communication, collaboration and empowerment are the main keys to the success of social learning in society. Key challenges identified include limited access to information, resource constraints, and digital literacy barriers, all of which require specific strategies to overcome. By examining the relationship between success factors, challenges, and use of digital platforms, this research provides new insights that support the development of more effective strategies for using digital technology to support social learning that leads to community empowerment. This research has great potential to increase people's access to learning opportunities and information, allowing them to be more active in the social learning process.

关键词： Information resources Collaboration Information management Digital intelligence Context modeling

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Service Oriented Fuzzy Decision Model for Health Insurance Claim

Service Oriented Fuzzy Decision Model for Health Insurance C...

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Ditdit Nugeraha Utama Sumiatie Susanto Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

Insurance claim is a fascinating issue to study. A potential loss for the insurance company is major coming from this issue. Thus, many studies performed already to answer such an issue. This study takes aim to develop a computational decision model based on service technology. Four fundamental methods operated in constructing the model and its implementation in service technology. The analytical hierarchical process (AHP) and fuzzy logic methods are two methods benefited to construct the model; where the AHP used to prioritize thirteen parameters considered and the fuzzy logic with its inference capability operated to generate the decision. Object oriented is an analysis and design method to analyze and design the model implanting it in service oriented architecture (SOA). Then, SOA conception functioned to deploy the model in the service architecture. Ultimately, the suggested framework comprising three layers of service-oriented architecture (SOA), namely business process, service interface, and application, has been established, alongside the integration of eight essential services that connect these three applications. The model demonstrates simulation outcomes indicating that 31.47% of claims are categorized as low risk and have been approved, 17.64% of claims are considered moderate risk with currently pending decision status (requiring additional investigation), while 50.89% of claims are classified as high risk with also pending decision status.

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A Smart Fuzzy Decision Model for Anticipating Household Expenditure Shifts in Response to Fluctuating Fuel Prices in Indonesia

A Smart Fuzzy Decision Model for Anticipating Household Expe...

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International Conference on ICT For Smart Society (ICISS)

作者： Frans Fericia Jeremy Ansellino Gunawan Ditdit Nugeraha Utama Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

Analyzing the impact of fuel price increases in Indonesia is crucial for making informed strategic decisions. This paper presents a smart fuzzy decision model aimed at predicting changes in household spending in response to fuel price fluctuations. The model utilizes fuzzy logic as its main method, enabling it to capture the intricate relationships between household spending and fuel prices. In addition, the proposed model incorporates various factors that can potentially influence household spending. By simulating prediction results under different fuel price increments ranging from 0% to 30%, the model provides valuable insights for policymaking concerning fuel pricing and offers strategies to mitigate the impact of fuel price fluctuations on household welfare.

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Repurposing transfer learning strategy of computer vision for owl sound classification 7

Repurposing transfer learning strategy of computer vision fo...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Gunawan, Kevin William Hidayat, Alam Ahmad Cenggoro, Tjeng Wawan Pardamean, Bens Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Accurately predicting an owl species based on its sound can be helpful for owl conservation. To build an accurate model for owl sound classification, deep learning is currently the most preferred algorithm, due to its excellent performance for modeling audio data. However, deep learning is generally underperformed for a small dataset, which is the case for recognizing scops owl sound. To overcome the issue, we proposed a transfer learning strategy, which is common for computer vision tasks, that can alleviate overfitting in a deep learning model for the owl sound classification. In our approach, we propose a neural network architecture consisting of the backbone of a EfficientNet model pre-Trained on the massive ImageNet database. The model takes the sound input that has been converted as two image representations: Spectrogram and Mel Frequency Cepstral Coefficients. Our strategy enables the use of a relatively small size of pre-Trained image classification model, which is widely available, for transfer learning in owl sound classification. Deploying the lightweight model in an automatic sound classifier provides a fast and accurate tool for various owl conservation purposes. © 2022 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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BERT-based Topic Modeling Approach for Malaria Research Publication 4

BERT-based Topic Modeling Approach for Malaria Research Publ...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Budiarto, Arif Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665473279

Malaria is a communicable disease with half of the global population at risk due to its high morbidity and mortality rates. A massive number of studies are dedicated to malaria research, so it plays a key role in formulating the proper prevention strategy and effective malaria treatment. With the overwhelming number of updated publications in the field, an unsupervised text mining approach such as topic modeling may provide an alternative method for the malaria researcher to keep pace with new insights. In this work, we collect metadata of malaria publications from the PubMed database to perform BERT-based topic modeling to find well-defined topics regarding malaria research. The method is largely based on the popular BERTopic pipeline. We compare the performance of three different language models to generate document embeddings from the data. The dimension reduction and the density-based clustering algorithm are used to cluster the embeddings. The topic representation is computed based on the semantic similarity of the class TF-IDF representation. The substance of the resulting topics is then manually annotated based on the top words of each topic. We demonstrate that by merging initial topics into larger topics using hierarchical clustering and manual content-based examination, the evaluated coherence measure can be further improved, thus enhancing the topic's interpretability. Our modeling result is able to extract ten major topics recurring in the malaria research publication published from 2017-2022. The result provides preliminary insight to understand the dynamics and patterns of malaria research over the years © 2022 IEEE.

关键词： Diseases

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Classification Prediction of Heart Disease Using Machine Learning Techniques

Classification Prediction of Heart Disease Using Machine Lea...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Kevis Setiawan Jonathan Putu Satria Beratha Maria Susan Anggereainy Afdhal Kurniawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School Of Computer Science Bina Nusantara University Jakarta Indonesia

Heart disease is also called a common one of global health concerns. A lot of research has been done before to predict someone whether has a heart disease or not by machine learning. In this study, we use five machine learning techniques as comparison which machine learning technique has a most accuracy to recognize heart disease in someone's condition. In this case, we are using UCI Cleveland Dataset as a sample and the result shows that the Support Vector Machine and K-Nearest Neighbor gives the most accuracy which is 85% along with many aspects respectively.

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Fundamental Components of Microlearning for Sustainable Quality Education: A Systematic Literature Review 8

Fundamental Components of Microlearning for Sustainable Qual...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Rahutomo, Reza Hiererra, Siti Elda Arifin, Yulyani Suprayogi, Muhamad Nanang Pardamean, Bens School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Humanities Psychology Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

E-learning was disrupted by a new concept of education named microlearning that carried various digital learning materials in convenient learning paths that were customizable according to users' necessity by using digital application. The competitive advantage of microlearning has been published but caused ubiquitous bias definition. This study aims to identify and analyze fundamental components of existing microlearning research and implementations in e-learning platforms. 42 research articles from SCOPUS database were collected from 2018 to 2022. They were reviewed to discover fundamental components of microlearning that must be considered in its implementations. The result of this study uncovered 'Content's variety' the most fundamental components. In contrast, this study finds that advance technology is the least discussed but required in supporting specific usage of microlearning. Limitation of this study lies on the determination of database and keyword usage and the methodology. This study recommends to consider combination of microlearning components to concept a specialized microlearning for sustainable quality education. © 2023 IEEE.

关键词： E-learning

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Deaf Helper Mobile Application for Interaction of Hearing Disorders Communities 2

Deaf Helper Mobile Application for Interaction of Hearing Di...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Setyawan, Adrian Naphan, Gilbert Xervaxius Dynata, Kevin Friry, Jovano Edmund Warnars, Harco Leslie Hendric Spits Bina Nusantara University School of Information System Information Systems Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

People with hearing loss in this world have not received much serious attention from the authorities. This makes these sufferers confused in choosing learning media to interact with and isolated from their social environment. This application was created to help people with hearing loss to be noticed and understood from the way they communicate using sign language through the mobile application called Assistant for the Deaf, which has many features such as registration, interactive videos, sign language translator, forums, customer service, library, information, history, events, donations, and shops. The application is designed using use case diagrams and class diagrams modeling the database, and the implementation used Android Studio and MySQL database. © 2022 IEEE.

关键词： Audition

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Data Mining and Bioinformatics Approaches to Elucidate Bacterial Communities in the Extreme Environment

Data Mining and Bioinformatics Approaches to Elucidate Bacte...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Nirwantono, Rudi Asadi, Faisal Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for many industrial applications. However, the main problem is that majority of the bacteria are unculturable, hampering the exploration of bacteria from different environments. Metagenomics approach which employs Next Generation Sequencing technology could help study bacteria by utilizing 16S rRNA marker gene. This study aims to demonstrate data mining and bioinformatics approaches to analyze 16S rRNA sequencing data. The raw sequencing data of 16S rRNA was collected from biological database. Then, the data were trimmed, denoised, and clustered to generate Amplicon Sequence Variants (ASVs). Diversity (alpha and beta) and taxonomic analyses were then conducted to elucidate the bacterial diversity and taxonomic profile of ASVs. The results of this work showed that Shannon and PD indices in China's hot spring were higher than Singapore and the USA. Furthermore, a significant difference was observed in the PD index. The unweighted UniFrac distance also showed there was a significant difference of the bacterial communities between the three locations. In addition, the taxonomic investigation unveiled prevalent bacterial groups in the ecosystem, namely Proteobacteria, Chloroflexi, Cyanobacteria, and Crenarchaeota. The research outcomes have the potential to serve as a foundational resource for subsequent bacterial metagenomic research, particularly the hot spring environment. © 2023 IEEE.

关键词： Bacteria

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Strengthening IT Governance in the Crypto Marketplace: Leveraging Penetration Testing and Standards Alignment

Strengthening IT Governance in the Crypto Marketplace: Lever...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Yulianto, Semi Krisnanik, Erly Hartawan, Muhammad Syarif Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia University of Pembangunan Nasional Veteran Information System Department Jakarta12450 Indonesia University of Krisnadwipayana Faculty of Technology Jakarta13077 Indonesia Asia e University Ict Department Malaysia

ISBN: (纸本)9798350307306

This research explores the vital role of penetration testing in cybersecurity, specifically its alignment with ISO 27001:2022, COBIT 2019, and NIST CSF standards in the context of crypto asset exchange management. These sectors grapple with rapidly evolving threat landscapes, necessitating robust cybersecurity strategies. However, the integration of penetration testing with these standards remains underexamined. Through a comprehensive approach, including literature reviews, surveys, case studies, and interviews, this study investigates the incorporation of penetration testing into an organization's cybersecurity. It unveils the current state of penetration testing practices, interprets ISO 27001:2022, COBIT 2019, and NIST CSF perspectives, and pinpoints integration challenges and opportunities. This study offers practical insights and best practices for integrating penetration testing into cybersecurity frameworks by emphasizing alignment. It underscores the importance of aligning penetration testing with information security standards in the crypto asset exchange management sector, enhancing security, proactively identifying vulnerabilities, and effectively mitigating risks in the context of growing crypto asset importance. This research provides invaluable guidance for safeguarding digital assets in this evolving domain. © 2023 IEEE.

关键词： Cybersecurity

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