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Fake News Classifier with Deep Learning

Fake News Classifier with Deep Learning

引用

Industrial Electrical and Electronics (ICIEE), International Conference on

作者： Michael Febrianto Lu Renaldy Vincent Ciptadi Reyhan Nathanael Kevin Sam Andaria Abba Suganda Girsang Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665486231

In this decade, social media has become more frequently used because it is easy to see some news on the social media for example Instagram, Facebook, and other social media. However, the problem that often arises in social media problems is the accuracy of the news. There are many fake news circulating on social media. This paper aims to classify the fake news using random forest. This research uses data Kaggle “Fake News Dataset Combined Different Sources”. The accuracy of the Fake News Classifier using this model is is 0.9403, the Classification Report on this model is True Recall Score: 0.88 and the Fake Recall score is 0.98 while the Precision Score is 0.96 and 0.93 for each true and fake result, F1-Score is 0.92 and 0.95. At the end the model using self-inputted data is tested

关键词： Deep learning Social networking (online) Multimedia Web sites Forestry Real-time systems Data models Fake news

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Data warehouse for analysing music sales on a digital media store 2

Data warehouse for analysing music sales on a digital media ...

引用

2nd International Conference on computer, science, Engineering, and Technology, ICComSET 2019

作者： Ranti, Kiefer Stefano Tuapattinaya, Deyanara Chang, Calvin Girsang, Abba Suganda Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

Nowadays, every company knows that when making a decision that has a potential in affecting their assets, an accurately processed report is necessary in order to support the reasoning behind their decision. Generating a report for stakeholders quickly and accurately is highly required in assisting them making a data-driven decision. By developing a data warehouse, it is possible for a company to do a data-driven decision making to appeal to their customer segments. This paper proposes a data warehouse model design to analyse the sales data contained in the database. The method that was implemented for this particular data warehouse development is the nine-step methodology designed by Kimball. The results are then presented in pdf form and an interactive dashboard. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Data warehouses

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GAN.s Approach On Generating Synthetical Skin Image Dataset for Imbalance Dataset

GAN.s Approach On Generating Synthetical Skin Image Dataset ...

引用

International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Tri Agus Setiawan Yaya Heryadi Lukas Lili Ayu Wulandari Ilvico Sonata Computer Science Department BINUS Graduate Program -Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia - Atma Jaya Jakarta Indonesia

ISBN: (数字)9798350368970

ISBN: (纸本)9798350368987

The use of balanced medical datasets is essential to improve the precision and accuracy of machine learning models in the healthcare field. However, dataset imbalance often becomes an obstacle, especially in the diagnosis of skin diseases. This research aims to develop a Generative Adversarial Networks (GAN) method that is more effective in generating synthetic skin datasets to overcome problems in integrating medical datasets. The methods used include developing and training a GAN model to produce realistic synthetic skin images, with a focus on improving the quality and diversity of the synthetic data produced. Important results from this research show that the developed GAN model is able to produce synthetic skin images that are not only realistic but also able to balance the original dataset. The implications of these findings include improving the accuracy and performance of machine learning models in the diagnosis of skin diseases, as well as the potential use of these methods in other medical fields that face similar problems with imbalanced datasets.

关键词： Training Accuracy Machine learning Generative adversarial networks Reliability engineering Skin Data models Medical diagnostic imaging Diseases Synthetic data

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News Classification Based On News Headline Using SVC Classifier

News Classification Based On News Headline Using SVC Classif...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Goldius Leonard Fukriandy Sisnadi Nicholas Vigo Wardhana Muhammad Abdul Aziz Al-Ghofari Abba Suganda Girsang Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

News gives new insight and information from all over the world. News has many categories, such as politic, economy, science, and other common news categories. Every news will have their own category based on its content. The classification of news is usually done manually by inputting the category during the news posting. Some of the categories may be inputted incorrectly. The news classifier can be the solution for problem, but the news classifications out there are usually based on the news content. The classifier will receive the word vector inputs that are taken from the news content and try to classify it into one of certain categories. Unfortunately, news contents can be longer and harder to be processed rather than processing the news headline. The news headline is shorter and packs a decent information for the classifier to find out what category it is. Besides the news headline usage, the classifier also needs to be chosen correctly. In this paper, the SVC model will be tested using the news headline data to classify the news and compare with several other models, such as Linear Regression, Multinomial Naive Bayes, Decision Tree, and Random Forest. The common variables to be compared are the accuracy, recall, and precision to evaluate the SVC model.

关键词： Training Linear regression Static VAr compensators Data models Telecommunications Decision trees Random forests

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Restaurant Customer Feedback Sentiment Analysis using Aspect Embedding Long Short-term Memory Model

Restaurant Customer Feedback Sentiment Analysis using Aspect...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Yaya Heryadi Bambang Dwi Wijanarko Dina Fitria Murad Cuk Tho Kiyota Hashimoto Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta Indonesia Computer Science Department BINUS Online Learning Jakarta Indonesia Information Systems Department BINUS Online Learning Jakarta Indonesia Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Prince of Songkla University Phuket Thailand

In the past ten years, there has been a proliferation of unstructured textual data from a variety of fields, allowing for the analysis of the sentiment polarity of the text authors in each of those datasets. To analyze sentiment polarity from unstructured data, machine learning tasks such as aspect-based sentiment analysis are used. The public dataset of restaurant patron feedback was used as the input for this study's empirical results of aspect-based sentiment analysis utilizing the Aspect Embedding Long Short-Term Memory model. The primary experiment's results show that the aspect embedding long short-term memory model can predict aspect sentiment polarization from customer feedback to restaurant service and aspect text representation (aspect text embedding) as input with excellent performance. For example, the Aspect Embedding Long Short-term Memory model, which uses ReLU as its activation function in the Dense layer tends to achieve higher performance (0.97 average accuracies) than the same model equipped with Sigmoid, and Tanh functions which achieve 0.954 and 0.956 average training accuracy respectively.

关键词： Training computer science Sentiment analysis Analytical models Computational modeling Machine learning Predictive models

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Preliminary Evaluation of Kyokko for Inter-FPGA Communication Framework CIRCUS

Preliminary Evaluation of Kyokko for Inter-FPGA Communicatio...

引用

IEEE International Conference on Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS)

作者： Kaito Kitazume Norihisa Fujita Ryohei Kobayashi Taisuke Boku Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Center for Computational Sciences University of Tsukuba

In recent years, Field-programmable Gate Arrays (FPGAs) are gaining attention as computational acceleration devices in the field of high-performance computing. By implementing specialized circuits that can be customiz... 详细信息

ISBN: (数字)9798350383454

ISBN: (纸本)9798350383461

关键词： Conferences High performance computing Circuits Cluster computing Low latency communication Field programmable gate arrays

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Risk Assessment Analysis on Mobile Banking Using Cobit 5 Framework

Risk Assessment Analysis on Mobile Banking Using Cobit 5 Fra...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Joy Nashar Utamajaya Arief Ramadhan Edi Abdurachman Agung Trisetyarso Muhammad Zarlis Computer Science Department BINUS Graduate Program – Computer Science Bina Nusantara University West Jakarta Indonesia

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company's business goals. Banking companies include companies that use information technology such as Mobile Banking in their services. Mobile banking is a banking facility or service using mobile communication tools such as mobile phones, with the provision of facilities for banking transactions through mobile applications. Even though they have used a good SOP (Standard Operating Procedure), there are still many obstacles that occur, especially system problems and human errors. This can result in a high risk if it occurs continuously and will be fatal to the company's business processes. Therefore, COBIT 5 can be used as a benchmark for the IT assessment process on local bank mobile banking services. The purpose of the study is to determine the level of capability and strategy for improving risk management. The method used in this study is to focus on IT risk management with the COBIT 5 framework in the Evaluate Direct Monitoring (EDM) 3 domain, Align Plain Organize (APO) 12. From the results of the analysis, the calculation of the capability level is at Level 1 Performed Process with a score of 82.04%, thus the status has evidence as well as a systematic approach and this achievement is obtained significantly through the assessment of process attributes.

关键词： Systematics Standards organizations Banking Companies Mobile communication Mobile handsets Risk management

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Threat Modeling for Honeypot Deployment

Threat Modeling for Honeypot Deployment

引用

Information Technology International Seminar (ITIS)

作者： Kalpin Erlangga Silaen Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

ISBN: (数字)9798331521295

ISBN: (纸本)9798331521301

The increasing complexity of cyber attacks, including 0-day vulnerabilities and APTs, has rendered traditional defenses like firewalls and IDS/IPS insufficient. Honeypots have been proposed as a solution to detect and analyze new attack types by simulating vulnerable systems and capturing malicious activities. However, the deployment of honeypots introduces unique risks, necessitating a comprehensive threat modeling approach to mitigate potential drawbacks. This paper explores the intersection of threat modeling and honeypot deployment, identifying threats, and proposing effective mitigation strategies. We applied a threat model adapted from a simplified PASTA framework based on risk-centric and proposed mitigation plans such as network segmentation, outbound traffic filtering, resource monitoring, and regular updates. Additionally, we discussed alternative honeypot deployments outside the organization's internal network to avoid arising risks. We found that deploying high-interaction honeypots carries high risks due to a broader attack flow. The novelty of this study lies in adapting the attack-flow approach for honeypot threat modeling, providing a structured method to analyze and mitigate honeypot-specific threats. Future research directions include conducting long-term studies and detailed case studies to further optimize the interaction between honeypot deployments and threat modeling for enhanced security outcomes.

关键词： Threat modeling Seminars Firewalls (computing) Filtering Prevention and mitigation Complexity theory Security Information technology Monitoring Cyberattack

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Discipline, Impact, And Challenges Of Virtual Reality In Higher Education: A Systematic Literature Review 7

Discipline, Impact, And Challenges Of Virtual Reality In Hig...

引用

7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Sunardi Hidayanto, Achmad Nizar Meyliana Prabowo, Harjanto BINUS Online Learning Bina Nusantara University Computer Science Department BINUS Graduate Program - Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok16424 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Business School Doctor of Research in Management Bina Nusantara University Management Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

Virtual Reality (VR) has received attention since the trend of the Metaverse came after the pandemic era. Several studies look into how Virtual Reality can be used in higher education. because all the research comes from the education field. However, previous research is too narrow in using technology, but not the area, impact, and challenges of Virtual Reality itself. Therefore, the systematic literature review (SLR) from 2016 to 2022 is discussed in this work to research how discipline, impact, and challenges of Virtual Reality in Higher Education. From 702 papers retrieved, and 214 candidates, to determine the applicability of the study question, 62 papers were chosen. The result shows that most discipline areas are still in computer science, followed by education and public health. Most reports said Virtual Reality is still highly impacted in solving the problem, especially in public health and education. However, there are still some pitfalls: the challenge of using The Head Mounted Device makes Virtual Reality-Induced symptoms and effects (VRISEs) such as Cyber sickness, simulator sickness, Motion sickness, dizziness, and physical discomfort. The implication of the SLR will be considered for academic purposes in preparing for the Metaverse era, especially in higher education. © 2022 IEEE.

关键词： Virtual reality

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Examining the impact of attitude, availability, price, and quality of service on broadband Internet customer satisfaction

Examining the impact of attitude, availability, price, and q...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Ikrar Adinata Arin Dina Fitria Murad Harco Leslie Hendric Spits Warnars Meyliana Information System Department School of Information Systems Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Indonesia Jakarta

After the COVID-19 pandemic, broadband internet utilization is predicted to continue to grow and experience a significant increase. The pandemic has changed how people work, study, communicate, shop, and carry out many aspects of daily life. Research reported that since 2018, research on broadband internet has continued to increase until now. This quantitative study examines the impact of the four critical variables on the link between attitude, availability, pricing, and quality of service (QoS) on broadband internet customer satisfaction using the Structural Equation Model approach and the Partial Lease Square technique. Three hundred and sixty-one citizen respondents in DKI Jakarta Province were collected using the snowball sampling method facilitated by Google Forms. At the same time, the COVID-19 post-pandemic was still ongoing in Indonesia. Through rigorous statistical analysis, the results show that customer satisfaction with broadband internet services is influenced by all factors, from attitude towards broadband Internet, broadband Internet availability, broadband Internet price, and service quality. The implications of this research are significant for service providers. They provide actionable insights for developing effective strategies to improve customer satisfaction and increase broadband internet subscriptions.

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