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检索条件"机构=Data Science Program Computer Science Department School of Computer Science"

共 70027 条记录，以下是361-370 订阅

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Enhancing Node Classification: Integrating Particle Swarm Optimization with Graph Convolutional Networks 4

Enhancing Node Classification: Integrating Particle Swarm Op...

引用

4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Nasari, Maulin Pradana, Rilo Chandra Tarigan, Gabriel Asael Masaling, Nikita Ananda Putri Tedjasulaksana, Jeffrey School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

The effectiveness of Graph Neural Networks (GNNs) in processing graph-structured data gained widespread recognition, and these models have applications in a wide range of fields. Even though GNNs have a lot of potential, optimising GNN models for particular tasks is still challenging. Within the scope of this research, a method is presented that combines Particle Swarm Optimization (PSO) and Graph Convolutional Networks (GCN) to enhance the performance of node classification. Using the Amazon computers and Amazon Photo datasets, which are both made up of product nodes that are connected to one another. The methodology includes data preprocessing with PyTorch Geometric, strategic data partitioning, and a robust hyperparameter optimization process using PSO. The modification was able to achieve an accuracy of 88.7% on the Amazon computers dataset and 94.9% on the Amazon Photo dataset, which is higher than the traditional GCN baselines. Our GCN-PSO model demonstrated significant performance enhancements. These findings highlight the effectiveness of PSO in fine-tuning GCN hyperparameters, which ultimately results in outcomes that are more accurate and efficient. When it comes to improving graph-based data analysis, the research highlights the potential of combining GCNs with optimization techniques such as PSO. This method could be extended to other graph learning challenges in subsequent research, and alternative optimization strategies could be investigated to further push the limits of GNN performance. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Performance Analysis of Heart Disease Prediction using Kernels in Support Vector Machine Model 4

Performance Analysis of Heart Disease Prediction using Kerne...

引用

4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Anggreainy, Maria Susan Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350376111

Linear data is a collection of data that is linearly separated by a straight line and nonlinear data is a collection of data that is not separated by a line. Usually, non-linear data is separated by irregular curves. However, for non-linear data. Support Vector Machine (SVM) is difficult to classify data for non-linear data. The solution is to use Kernel Trick. Kernel Trick is a simple method used to map low-dimensional nonlinear data and transform it into a higher-dimensional space. The goal is to make it easier to classify data by finding a hyperplane that can separate the dataset linearly well. This research is to create a program that can predict heart disease using the Support Vector Machine (SVM) model with Kernel Tricks. Heart disease prediction is very important because the heart is one of the most important organs in the human body. The results indicated that the SVM model with the Radial Basis Function (RBF) Kernel achieved the highest accuracy compared to the Linear Kernel and Polynomial Kernel. © 2024 IEEE.

关键词： Support vector regression

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Stock Price Prediction With the Informer Model 9

Stock Price Prediction With the Informer Model

引用

9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Yulistiani, Risma Kurniadi, Felix Indra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

The capacity to foresee future stock prices can substantially augment the potential profits of investors;thus, the subject of stock price prediction assumes significance within the investment industry. The present study employs the Informer model for the purpose of predicting stock prices. The results obtained from this research demonstrate that the Informer model's performance is contingent upon the dataset employed, suggesting that its applicability might be limited to particular categories of stock market data. The optimal outcomes were achieved using the BNI dataset, suggesting that although the model exhibits promise, it must be tailored to specific datasets and selected accordingly in order to optimize its performance. © 2024 IEEE.

关键词： Prediction models

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A Survey On Indonesian Hoax Analyzer and Fake News Detection Using Deep Learning Techniques 6

A Survey On Indonesian Hoax Analyzer and Fake News Detection...

引用

6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Laimeheriwa, Jonathan Iswanto, Irene Anindaputri Oktriani, Wiwi Hidayat, Muhammad Fadlan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

In today's digital world, where people consume information quickly through social media like Facebook, Instagram, and Twitter, there's a growing concern about fake news. It's crucial to know whether information and its sources are reliable, especially as people may disregard real news if it doesn't align with their beliefs. This survey aims to explore the landscape of fake news in the context of Indonesia, focusing on the development and implementation of hoax analyzers and fake news detection using deep learning techniques. Our review shows that deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models like IndoBERT have demonstrated effectiveness in identifying misinformation within Indonesian content, with promising accuracy and efficiency metrics. However, challenges persist, including the need for extensive data, computational resources, and tailored approaches to handle Indonesian language complexities. This survey highlights the importance of advancing these detection systems to help reduce the spread of misinformation and support informed decision-making in Indonesian society. © 2024 IEEE.

关键词： Tweets

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A Survey Study of Social Media User Awareness Towards Personal Identifiable Information's data Security in Indonesian Millennial Generation 3

A Survey Study of Social Media User Awareness Towards Person...

引用

3rd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2023

作者： Christie, Gabriel Alexander Ivan Trevan, Javier Achmad, Said Junior, Franz Adeta Nadia School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

Online Social Network is a network communication platform where users have profiles that can be uniquely identified by the content sent. This content can be produced, consumed, and interacted with by other users. To connect with other users on social media, users must register by providing Personally Identifiable Information (PII) to social media platforms. PII is specific information that can identify or track individuals directly. This specific information may include your name, address, social security number, or other identifying code numbers such as telephone numbers, email addresses, and others. Personal identifiable information leakage is a problem in data security. Basically, every individual does not want their personal data to be known by anyone. Utilizing a sample size of 50 respondents, this study aims to ascertain the percentage of individuals who are aware of PII security on social media. This research will use quantitative methods by distributing questionnaires. The questionnaire in this study uses a social media attribute design. The results of the survey indicate that many respondents are unaware of the security of their data and have a limited understanding of how their personal data is managed by technology companies, particularly the 80% of non-IT respondents. © 2023 IEEE.

关键词： Social networking (online)

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Raising Cyber Security Awareness to Reduce Social Engineering Through Social Media in Indonesia 3

Raising Cyber Security Awareness to Reduce Social Engineerin...

引用

3rd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2023

作者： Reuben, Nathanael Irawan, Rionel Jovann, Rainer Nathanael Achmad, Said Junior, Franz Adeta Nadia School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

In this day and age, there has been a surging increase in the use of social media such as Instagram, Facebook, Twitter, TikTok, etc. This kind of technology is useful to entertain and communicate in our daily life. But there is also a negative side to this technology, and one of them is social engineering. People will sometimes trick you into getting information that you shouldn't give to strangers, and sometimes people fall for this trick. This research aims to investigate the impact of social engineering infographics on the Indonesian populace's awareness of the issue. The t test reveals significant differences between the two survey groups that were informed beforehand about social engineering and those that were not. Infographics have a positive effect on individuals, making them more aware of social engineering cases. © 2023 IEEE.

关键词： Cybersecurity

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Multi-layer Perceptron Approach for Diabetes Risk Prediction using BRFSS data 10

Multi-layer Perceptron Approach for Diabetes Risk Prediction...

引用

10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Prasetyo, Simeon Yuda Izdihar, Zahra Nabila Bina Nusantara University School of Computer Science Computer Science Department Jakarta1480 Indonesia

ISBN: (纸本)9798350365283

The escalating prevalence of diabetes worldwide, affecting approximately 425 million individuals by 2020, underscores the urgent need for accurate predictive models to address this pressing public health challenge. In this study, we investigate the efficacy of Multi-layer Perceptron (MLP) models with varying units across two hidden layers - specifically, configurations with 5, 10, 15, and 20 neurons per layer. Leveraging the BRFSS dataset for diabetes risk prediction, our meticulous evaluation of these configurations unveils compelling insights. Notably, the MLP model attains its peak accuracy of approximately 84.91% with 5 neurons in Hidden Layer 1 and 10 neurons in Hidden Layer 2. These findings underscore its potential for robust predictive modeling. Furthermore, despite minor fluctuations, our analysis highlights the model's reliability and effectiveness in achieving high predictive accuracy. This research offers valuable contributions to the field of diabetes risk prediction, providing insights into the optimal configuration of MLP models for accurate and reliable predictions, thus aiding in the development of improved diagnostic tools and preventive strategies to mitigate the global burden of diabetes. © 2024 IEEE.

关键词： Prediction models

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Comparative Analysis of Machine Learning Algorithms for Laptop Value Estimation 9th

Comparative Analysis of Machine Learning Algorithms for Lapt...

引用

9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Hansel, Kent Christopher Tanoto, Vincentius Axelle Suri, Puti Andam Fajar, Muhamad Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The increasing prevalence of laptops in homes, schools, and workplaces makes accurate value estimations essential to guide buyers in making informed decisions. This study compares the effectiveness of four machine learning algorithms-Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM)-in predicting laptop prices based on specifications such as brand, processor speed, RAM size, storage capacity, screen size, and weight. The performance of these models is evaluated using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. Results indicate that MLR and RF provide superior performance, achieving the lowest RMSE of 0.02 and the highest R2 score of 1.0, demonstrating their suitability for datasets with linear feature relationships. The findings highlight the significance of selecting appropriate models based on the nature of the dataset, suggesting that while ANN and SVM are potent for complex, non-linear data, MLR and RF are preferable for linear datasets. © 2024 The Authors.

关键词： Multiple linear regression

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DDoS Detection using Machine Learning Approach 9th

DDoS Detection using Machine Learning Approach

引用

9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Janivasya, Raihan Putra Rachmawati, Ika Dyah Agustia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Distributed Denial of Service (DDoS) attacks pose a significant threat to online services by flooding targets with unusually high volumes of traffic or data, disrupting services and causing financial and reputational damage. This paper explores the workings and impact of DDoS attacks, with a variety of methods used by attackers to exploit vulnerabilities in the target infrastructure. To address these risks, this paper advocates the application of Machine Learning (ML) techniques. ML allows computers to learn patterns from data and make decisions autonomously and offers a proactive defense against DDoS attacks. Using UNSW-NB15 dataset, this study evaluates the performance of eight ML algorithms Logistic Regression, KNN, SVM, Random Forest, Decision Tree, LSTM, MLP, and GRU, the evaluation of this study is based on Accuracy, Precision, Recall, and F1 Score metrics. The results show that Random Forest is the most effective algorithm, showing superior performance across all metrics with 97.68% accuracy. This explains the potential of Machine Learning, specifically the Random Forest algorithm. can be considered to improve security and reduce the risk posed by DDoS attacks. © 2024 The Authors.

关键词： Denial-of-service attack

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IoT Door Locking a Review 9

IoT Door Locking a Review

引用

9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Yulianto Rahayu, Anita School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Traditional mechanical locks need a key for locking or unlocking the door, with the fundamental drawback of the possibility of losing the key. Researchers have proposed replacing the mechanical key with electronic one, also equipped with a sensor, in order to improve the fully mechanical system of door locks. The Internet of Things (IoT) scheme was also implemented for better control and monitoring via the cloud network. IoT schemes usually involve several components such as sensors, microcontrollers, and network components. The sensor is used to read the state of the environment. It can also be used to capture the biometric characteristics of the users authorized to access the system. For some types of sensors, it is not possible to connect them directly to a computer to collect the result of the reading of the sensor data. Thus, microcontrollers are needed. However, there are still a few papers that provide a summary of what the main component is and what other authors have proposed in their research. The aim of this study is to briefly summarize which sensor types, microcontrollers and communication protocols have recently been used to improve IoT-based door locks, especially for indoor environments. Whether the use of a single sensor or multiple sensors is also discussed. The types of microcontrollers used in recent research are also discussed. © 2024 IEEE.

关键词： Locks (fasteners)

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