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Classification Prediction of Heart Disease Using Machine Learning Techniques

Classification Prediction of Heart Disease Using Machine Lea...

引用

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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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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Smart Insurance System Model Concept for Marine Cargo Business

Smart Insurance System Model Concept for Marine Cargo Busine...

引用

2021 International Conference on Data science and Its Applications, ICoDSA 2021

作者： Santoso, Cahyono Budy Prabowo, Harjanto Warnars, Harco Leslie Hendric Spits Fajar, Ahmad Nurul Bina Nusantara University BINUS Graduate Program-Doctor of Computer Science Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9781665443036

In this digitalization era, insurance companies have to face many new challenges, namely a very competitive market that has resulted in decreased margins and revenues and increased expectations from customers for insurance company services. Insurance companies are required to be fast-paced in providing accurate services and pricing. In addition, insurance companies must carry out marketing strategies that make customers feel valued so that strong engagement will arise. The focus of this research is on the field of marine cargo insurance, which is an insurance product that guarantees the loss of transportation of goods by sea. The purpose of this research is to analyze the underwriting and marketing processes in marine cargo business loss insurance and identify insurance models that can be smart and comprehensive solutions. The research method is to conduct a literature review to obtain previous problems and solutions related to this research topic. Then the researchers conducted observations and interviews with experts at an international insurance company in Jakarta. From this method, the main problems in this research are the underwriting process, product information services, and system recommendations that are less effective and efficient. This study proposes a smart insurance model concept by combining technological features, namely expert systems, chatbot, and recommend systems. It is hoped that this research will contribute to increasing underwriting and marketing productivity, as well as customer satisfaction in getting fast and appropriate product services. © 2021 IEEE.

关键词： Expert systems

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Combining DistilBERT and LSTM to Enhanced Detection of Depression in Social Media Text

Combining DistilBERT and LSTM to Enhanced Detection of Depre...

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International Conference on Information Management and Technology (ICIMTech)

作者： Felix Indra Kurniadi Ni Luh Putu Satyaning Pradnya Paramita Erna Fransisca Angela Sihotang Maria Susan Anggreainy Rongfang Zhan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Statistics 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 Department of Rehabilitation and Health Service School of Computer Science University of North Texas United States of America

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

Depression affects a significant number of people, and it has a significant impact not only on their lives but also on society as a whole. In light of this, we require more advanced methods that are capable of locating individuals who are depressed in a timely and accurate manner. This research investigates the effectiveness of Long Short-Term Mem- ory (LSTM) networks used in conjunction with DistilBERT, a small transformer-based model, in identifying indications of melancholy in posts made on social networking platforms. The text was cleaned up using a dataset that was obtained from Reddit, and then we used the mixed DistilBERT+LSTM model to evaluate how well it performed in comparison to the DistilBERT model by itself. The findings that we obtained indicate that both approaches produce results that are comparable but not flawless, with only slight variations in terms of accuracy, precision, recall, and F1 scores. The fact that this is the case demonstrates that there are issues with optimizing the model and displaying the data.

关键词： Micromechanical devices Accuracy Social networking (online) Mood Mental health Depression Transformers Data models Information management Long short term memory

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Server Cloud Based Security Surveillance Systems: a Design science Research

Server Cloud Based Security Surveillance Systems: a Design S...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Alexander Nurenie Arief Ramadhan Edi Abdurachman Agung Trisetyarso Muhammad Zarlis Computer Science Department BINUS Graduate Program – Computer Science Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia

In this research, the author addresses the prevalent issues faced by users of cloud services, especially those using Peer-to-Peer (P2P) technology, such as connection losses, security concerns, and poor video quality. The study does not delve into the complex problems at the data center level but instead proposes a novel Design science Research approach. This new method, tested with five clients in varied locations, utilizes port forwarding to establish a direct server-client connection, bypassing traditional P2P frameworks. The results show that this approach significantly improves quality over the P2P system. Feedback from clients before and after the experiment indicates a positive response to this new method. This research offers insights into overcoming P2P-related issues in cloud services and contributes to the development of cloud-based server science, presenting a cost-effective, independent alternative to data center-dependent P2P services, with plans for ongoing research in this area.

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Property selling system with support for validation and verification process 7

Property selling system with support for validation and veri...

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

作者： Iskandar, Karto Christianto Herlina, Maria Grace Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia

Due to the COVID-19 pandemic, today's property trade transactions through the website have become common. This phenomenon requires the availability of a trustable property commerce website. This research aims to produce a website trusted for sellers and buyers of property in conducting trade. So, we need to know what information is required by the property validation process so that sellers and buyers feel safe committing the transactions. This website will help increase public trust because it is supported by a verification and validation process by third parties regarding property information. The research method begins with distributing questionnaires to discover the needs of property sellers and buyers, then developing a website using the waterfall methodology of the Laravel application framework. The result of this research is to establish a set of information needed for the property validation process that needs to be verified before conducting transactions. Finally, this web application can increase trust by 59.5%, whereas 37.6% gave the maximum value and 8.7% of respondents gave only moderate weight after being tested on 173 respondents. © 2022 Elsevier B.V.. All rights reserved.

关键词： Websites

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Automatic Smart Crawling on Twitter for Weather Information in Indonesia 8

Automatic Smart Crawling on Twitter for Weather Information ...

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

作者： Purwandari, Kartika Perdana, Reza Bayu Sigalingging, Join W.C. Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Although Twitter is a popular platform for social interaction analysis and text data mining, it faces challenges with geolocation automation. To address this problem, the researchers propose the utilization of a Support Vector Machine (SVM) model to develop an automated Twitter crawling system. The system aims to collect data related to weather in Indonesia by employing Twin, a Python-based Twitter scraping software. To overcome null geolocations, the study incorporates aliases created based on the common practice of Indonesian users mentioning the country's location in tweets. The results demonstrate that the SVM model, combined with automated smart crawling, achieves an 85% accuracy rate. © 2023 The Authors. Published by Elsevier B.V.

关键词： smart crawling Social media SVM Twitter weather

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Survey of Online Exam Proctoring Model to Detect Cheating Behavior based on Face Recognition

Survey of Online Exam Proctoring Model to Detect Cheating Be...

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International Conference on Systems and Informatics (ICSAI)

作者： Sucianna Ghadati Rabiha Iman Herwidiana Kartowisastro Reina Setiawan Widodo Budiharto Computer Science Department BINUS Graduate Program – Computer Science. Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Computer Science. Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Computer Science. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665474498

Exams are an important component of any educational program, including online education. In any test, there is a possibility of cheating, so its detection and prevention is important. This study aims to conduct an in-depth study of the online exam monitoring model approach based on facial recognition used to detect cheating. Based on the inclusion and exclusion criteria designed, 13 selected studies were obtained. From these studies, we conducted further analysis regarding the Face Detection Method, Face Recognition Method, Initial Feature, Behavior Analysis and Evaluation Metrics used in each study so as to provide answers to research questions. the most frequently used Face detection method was Viola-Jones with a presentation of 20%, then CNN and MTCNN with a total presentation of 21%. The most widely used face recognition method in selected studies is CNN and metrics Accuracy is one of the most frequently used evaluations with a percentage of 33%. While the features that are usually used to detect cheating during online exams include facial motion and head pose which occupies the first position. The second is eye movement, then multiple faces gaze estimation and facial expression is in third place. Other features that also play a role in analyzing cheating behavior are mouth detection, facial vector, landmark location, gesture and posture.

关键词： Measurement Analytical models Face recognition Mouth Estimation Feature extraction Behavioral sciences

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Performance Analysis of Distributed Database System in Cloud Computing Environment

Performance Analysis of Distributed Database System in Cloud...

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

作者： Nicholas Prasetio Daniel Ferdinand Brandon Christopher Wijaya Maria Susan Anggreainy Afdhal Kumiawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Computer Science Bina Nusantara University Jakarta Indonesia BINUS Graduate Program School of Computer Science Bina Nusantara University Jakarta Indonesia

This research discusses the performance evaluation of distributed database systems in a cloud computing environment Cloud computing environments allow data and applications to be stored and deployed on infrastructure located in different parts of the world. However, the use of distributed database systems in cloud computing environments can cause performance issues, such as complex data access and factors such as network latency, security, and scalability that affect system performance. Therefore, performance evaluation of distributed database systems is necessary to ensure effective data management across the infrastructure. The purpose of this research is to measure and understand the performance of distributed database systems in a cloud computing environment. This is important because proper performance evaluation is needed to ensure distributed database systems can operate effectively and efficiently in such environments. This research will analyze the features of distributed database systems, factors that affect performance, how to measure and compare system performance, and how to improve system performance. Analyzing the performance of distributed database systems in a cloud computing environment can help users choose the most appropriate and efficient cloud computing platform for their business needs and improve operational efficiency.

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Testing Approach for IoT System (Case Study: Air Quality Monitoring System)

Testing Approach for IoT System (Case Study: Air Quality Mon...

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International Conference on Information Management and Technology (ICIMTech)

作者： Theresia Herlina Rochadiani Haryono Soeparno Ford Lumban Gaol Yulyani Arifin Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Informatics Department Pradita University Tangerang Indonesia

The Internet of Things (IoT) was evaluated as part of this study. The IoT system in this study is the air quality monitoring system. The system consists of three layers, three end devices with air quality monitoring sensors as the perception layer, the LoRaWAN network as the network layer, and the monitoring website as the presentation layer. Before being made available to the public, IoT hardware and software must undergo testing. For IoT systems, there are no established standards or quality criteria. This study's goal is to evaluate the methodology for an air quality monitoring system. Website speed is measured using Pingdom tools. The website received a performance grade of B. Pingdom offers advice for enhancing website performance. Sucuri is utilized as a security test tool to evaluate the website's security, and consequently, no malware was discovered. The latency and data dependability are monitored to assess the system's performance fully. The outcome demonstrates that all system components consistently and accurately receive data with a latency of less than 1 s.

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