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Ransomware Resilience: Investigating Organizational Security Culture and Its Impact on Cybersecurity Practices against Ransomware Threats

Ransomware Resilience: Investigating Organizational Security...

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Informatics Engineering, science & Technology (INCITEST), International Conference on

作者： Semi Yulianto Benfano Soewito Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

The increasing threat of ransomware attacks in an interconnected and digital world presents important hurdles for cybersecurity, prompting further examination into how organizational security culture affects their ability to withstand these malicious threats. This study examines the impact and importance of organizational security culture in ransomware threat mitigation. We have surveyed five organizations, giving points from 1 to 5 for each question on cultural factors, leadership support, employee awareness and involvement (compliance), communication, behavior change or BBS practice incorporation, learning/training delivery methodology, and Size and industry impact on their security culture. The results indicate that a robust security culture, with leadership and effective communication, builds stronger resilience to ransomware. Organizations with jointly accountable cybersecurity and training practices are highly efficient. Suggestions range from establishing a secure environment to increasing management buy-in and integrating compliance. This work helps advance our understanding of ransomware resilience and highlights the key role of organizational security culture. The resulting learnings can help to inform organizations' security posture, ultimately helping them better protect their defenses against ransomware threats.

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An Analysis of Hate Speech Detection Techniques 4

An Analysis of Hate Speech Detection Techniques

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Krisdianto, Ricky Apriani, Ivana Halim, Peter Miracle Anggreainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Departement Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Hate speech is any act of provoking or insulting another person or group based on their ethnicity, religion, race, gender, sexual orientation, physical ability, or other characteristics. This can be done in a variety of ways. Because of this, a study was conducted that focused on ascertaining the most effective technique for identifying hate speech. It was discovered that LSTM, a form of recurrent neural network (RNN) that is particularly good at modeling sequential data, is the most successful technique to apply. Many natural language processing (NLP) activities may be performed with it, including hate speech detection, which makes LSTM have advantages over other techniques, both in terms of long-term memory mechanisms. LSTM, also able to handle long and complex text sequences, capable of recognizing more complex patterns, able to understand and consider the context in the text, to recognize the meaning of a word or phrase in a broader context. Then, based on the dataset obtained from the Kaggle website with a total of 24783 data, then the dataset is divided into training data and validation data, with a ratio of training data to validation data. of 80%: 20%. Then, the testing step results in an accuracy value of 87.10%. © 2023 IEEE.

关键词： Speech recognition

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Model Prediction Using Random Forest Algorithm and Time Series Analysis Parts Distribution with in Indonesia Automotive Industry 8

Model Prediction Using Random Forest Algorithm and Time Seri...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Andy Achmad, H. Ramadhan, Arief Warnars Harco Leslie, H. Budiharto, Widodo Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Telkom University School of Computing Bandung40257 Indonesia

ISBN: (纸本)9798350342604

Car spare parts are parts of a car that consist of components that form a certain unity and function, there are some parts that are damaged more often and therefore must be replaced more often. In this research, to predict the availability of spare parts using the times series moving average prediction method with random forest. The dataset used in this research is 19,068 consisting of location, transaction number, transaction date, part number, quantity and odo-km. Distribution of spare parts classified based on recapitulation of spare part distribution is RAE=54.5541% and RSE=63.8888%, distribution of spare parts based on location is RAE=55.5541% and RSE=65.2118%, maintenance based on ODO-KM is RAE=33.0949% and RSE=41.2241 % and distribution quantity RAE=33.749% and RSE=36.2317%. The results of spare part distribution analysis using the Confusion Matrix with probability accuracy are TP Rate=0.903, FP Rate=0.067, Precision=0.911, Recall=0.903, F-Measure=0.893, MCC=0.857, ROC Area=0.983 and PRC Area=0.948. The aim of this research is to determine the factors needed to build a machine learning-based spare parts distribution model, implementation, measurement and prediction in the automotive industry in Indonesia. © 2023 IEEE.

关键词： Automotive industry

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Social Network Analysis and PowerBI Dashboard to Determine E-Wallet Marketing Strategies

Social Network Analysis and PowerBI Dashboard to Determine E...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Rita Rita Sheila Monica Miracle Aurelia Abba Suganda Girsang Virienia Puspita Global Business Marketing BINUS Business School Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program- Master of Computer Science Bina Nusantara University Jakarta Indonesia Communication Department BINUS Graduate Program - Master of Strategic Marketing Communication Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

The rapid growth of digital payment platforms like OVO, ShopeePay, and GoPay in Indonesia has driven the need for businesses to optimize marketing strategies by analyzing customer interactions through social media. This study leverages Social Network Analysis (SNA) and Power BI to extract insights from Twitter data, offering a comprehensive view of how these platforms engage with consumers and shape brand perceptions. By applying sentiment analysis and classification using IndoBERT results, this study aims to identify key influencers, assess the sentiment around these brands, and visualize marketing communication patterns. The integration of SNA and BI tools provides a detailed, data-driven approach to improving business decision-making in the competitive digital payment market, by utilizing them to evaluate e-wallet tweet activity and user interactions in Indonesia and identifying key periods and regions for targeted marketing. Strategies like loyalty programs and segmented campaigns are recommended to enhance user engagement, address security concerns, and maximize market reach during culturally significant periods such as Eid and the holiday season.

关键词： Java Sentiment analysis Negative feedback Social networking (online) Online banking Shape Companies Security Web sites Multimedia communication

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Abstractive Indonesian News Summarization Using BERT2GPT

Abstractive Indonesian News Summarization Using BERT2GPT

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

作者： Maulin Nasari Ayu Maulina Abba Suganda Girsang Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

This study examines the necessity of employing BERT2GPT for single-document summarization in the current age of escalating digital data. The primary focus of this work is on the abstractive technique, which tries to generate versatile summaries that surpass the primary sentences of the original content. This study employs two models, namely BERT and GPT -2, for the purpose of the summarization system. The paper introduces the BERT2GPT model, which merges the bidirectional characteristics of BERT with the generative powers of GPT. The findings indicate that the BERT2GPT method successfully catches significant information and linguistic nuances, hence enhancing the quality of the generated summaries. The corresponding average values for Rl, R2, and RL are 0.62, 0.56, and 0.60, respectively.

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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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Envisioning Risk Management for Smart Contracts: A Conceptual Model

Envisioning Risk Management for Smart Contracts: A Conceptua...

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Informatics Engineering, science & Technology (INCITEST), International Conference on

This study proposes a smart contract risk management model built on the NIST Risk Management Framework (RMF) to help identify, assess, and manage the risks of smart contracts. While smart contracts are beloved as a means to automate and disintermediate business processes, their security vulnerabilities can be critical. The main issue discussed in this paper is the lack of a holistic approach to risk management smart contracts. The resulting framework consists of six steps: Risk identification, assessment, prioritization, mitigation, testing, and continuous monitoring (and was developed through reviewing existing literature on smart contract security and the NIST RMF). It is recommended that a case study be performed to prove the proposed model's effectiveness in managing the risks of smart contracts and minimizing financial losses and reputational harm. The paper presents a risk management framework for smart contracts to increase trust and adoption to enhance security while reducing financial losses and reputation damage. This has wider implications for the security of smart contracts and can be used as a starting point for future work. This study is expected to significantly contribute to smart contract security by introducing an organized way to address these contracts' risks using the NIST RMF.

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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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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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The University's Integrated ICT Infrastructure: UTAUT2 Implementation

The University's Integrated ICT Infrastructure: UTAUT2 Imple...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Akbar, Komputerio Ramadhan, Arief Abdurachman, Edi Trisetyarso, Agung Zarlis, Muhammad Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665468763

Post-pandemic and globalization have accelerated the implementation of Industry 4.0, aided by rapid technological and information advancements. Industry 4.0 has significant consequences for all institutions in developing ICT infrastructure, one of which is educational institutions. Despite the enthusiasm in all educational institutions for using ICT to improve teaching and learning, particularly in universities, integrating technology is becoming problematic. Integrating ICT infrastructure in universities is a multidimensional process involving technology and curriculum, pedagogy, institutional readiness, lecturer competence, and long-term financial planning. Because of this, it is essential to explain the adoption of technology within the scope of this study to make the most of investments made in the integrated ICT infrastructure of the university using the UTAUT2 method. © 2022 IEEE.

关键词： Industry 4.0

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