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Face Recognition as Base Protocol in Online Transactions 9th

Face Recognition as Base Protocol in Online Transactions

引用

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

作者： Aziz, Mohammad Fauzi Taraka, Gavin Sadiya Sidharta, Sidharta Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

From each to each year, from decades and decades, the fraud and scam are growing more and more powerful, although we cannot avoid it 100% since the fraud and scams using any method to make a crack of hole or other chances to steal that make a company and other e-commerce suffer losses in small or large numbers. This leads to new rules that can make e-commerce stand a chance and avoid the losses of either the e-commerce itself or the client that is trying to make online transactions. This paper has a goal to answer 5 research questions about face recognition as base protocol in online transaction. Method that we use is systematic literature review that has 7 steps from deciding research question until data synthesis. For the result from 5 research questions are answered very well, which is RQ1. How effective face recognition can decrease fraud identity on online transactions. RQ2. What kind of struggle and problem makes this technology unable to integrate with online payment. RQ3. What kind of perception and the acceptance of the user about the face recognition method for authentication in online transactions? RQ4. How far the technology is for face recognition can integrate with the system online payment transaction that increases the efficiency transaction with or without compromising security. RQ5. What kind of law of implication and etiquette from the user face recognition in online transactions and how can the system be made to follow the regulation that already exists. From that our conclusion is clear which is face recognition is safe and secure but since the technology is very well and good, then need proper technology to support it further. © 2024 The Authors.

关键词： Authentication Protocol

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Analysis Fake News Detection Models Using Swarm Intelligence 8

Analysis Fake News Detection Models Using Swarm Intelligence

引用

8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024

作者： Deliansyah, Heru Gumilang, Zaky Satya Alfayed, Muhammad Iswanto, Irene Anindaputri Hidayat, Muhammad Fadlan School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368970

The proliferation of fake news on social media platforms has become a significant societal challenge, undermining public discourse and democratic processes. This study proposes a novel approach to fake news detection by leveraging swarm intelligence techniques, specifically a Modified Ant Colony Optimization (ACO) algorithm, in combination with established machine learning models. Our method employs a four-stage process: data preparation, feature extraction using TF-IDF vectorization, feature selection via Modified ACO, and model evaluation. We compare the performance of Random Forest, XGBoost, and Support Vector Classifier (SVC) models before and after dimensionality reduction. The WELFake dataset, comprising 72,134 news articles, is used to train and evaluate the models. Results demonstrate that the proposed approach achieves high accuracy in fake news detection while significantly reducing computational time, particularly after dimensionality reduction. This research contributes to the development of more efficient and accurate fake news detection systems, addressing the critical need for robust tools to combat misinformation in the digital age. © 2024 IEEE.

关键词： Dimensionality reduction

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Synergizing Ensemble Techniques for Precision Breast Cancer Diagnosis: A Comparative Performance Analysis 10

Synergizing Ensemble Techniques for Precision Breast Cancer ...

引用

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

作者： Prasetyo, Simeon Yuda Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350365283

Breast cancer presents a significant health challenge globally, demanding effective early detection and diagnosis for improved patient outcomes. Leveraging machine learning techniques offers promising avenues for enhancing diagnostic accuracy, with methods like machine learning demonstrating remarkable performance. However, to further enhance precision in breast cancer diagnosis, this study explores the effectiveness of ensemble learning methodologies. Evaluating ensemble models comprising various classifiers, including Random Forest, AdaBoost, Bagging, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and Hist Gradient Boosting, the research aims to identify optimal configurations for accurately distinguishing between malignant and benign breast lesions. The experimental results reveal XGBoost as the optimal ensemble technique, with an impressive accuracy of 94.737%. Notably, XGBoost's scalability, efficiency, and robustness, coupled with features like gradient boosting and regularization techniques, contribute to its exceptional predictive capability. This comprehensive analysis provides detailed insights into the specific strengths of ensemble techniques, highlighting their potential to significantly enhance breast cancer diagnosis and patient outcomes. The findings lay a solid foundation for better clinical decision-making in breast cancer management, with the potential for substantial improvements in healthcare practices. © 2024 IEEE.

关键词： Lung cancer

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Comparative Analysis of Machine Learning, Deep Learning, and IndoBERT Models Using SPAMID-PAIR Dataset

Comparative Analysis of Machine Learning, Deep Learning, and...

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2024 International of Seminar on Application for Technology of Information and Communication, iSemantic 2024

作者： Halim, Jeremy Michael Hoshe, Prama Neszhua Soenarto, Sebastian Sutoyo, Rhio Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350366648

Spam comments on social media platforms like Instagram pose a significant challenge, disrupting genuine user interactions. This research investigates and compares the effectiveness of machine learning (ML), deep learning (DL), and IndoBERT models for detecting spam comments on Instagram using the SPAMID-Pair dataset. The central question addressed is how different models perform in classifying spam comments in the Indonesian language, particularly considering contextual relationships between comments and posts. Previous studies have explored various ML and DL models for spam detection, often neglecting the context provided by posts. This research aims to fill this gap by using a dataset that includes both comments and their corresponding posts, highlighting the importance of contextual analysis in improving spam detection accuracy. The methodology involved dataset preparation, preprocessing, data undersampling, and applying various ML, DL, and IndoBERT models. Results indicate that LightGBM, particularly with FastText and BERT embeddings, achieved high accuracy and F1 scores. Deep learning models like BiLSTM, CNN, and Transformer also demonstrated strong performance, with the IndoBERT-large-p1 model outperforming all. This study underscores the significance of advanced NLP techniques and contextual analysis in enhancing spam detection on social media platforms. © 2024 IEEE.

关键词： Contrastive Learning

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Feature Selection Using XGBoost on METABRIC Dataset for Survivability Breast Cancer Detection 9

Feature Selection Using XGBoost on METABRIC Dataset for Surv...

引用

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

作者： Kurniadi, Felix Indra Saputri, Hanis Amalia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Breast cancer is a highly lethal ailment that primarily affects women. The investigation of the survivability of breast cancer using different modalities remains an ongoing concern. One such approach involves analyzing the data obtained from The Molecular Taxonomy of Breast Cancer International Con- sortium (METABRIC). Regrettably, employing many modalities in the process results in an abundance of features in the data. We utilized the XGboost algorithm to carry out the process of feature selection by identifying the k-top features. We conducted a comparison of the findings using various machine learning clas- sifiers, including K-Nearest Neighbor, Support Vector Machine, Random Forest, and XGBoost. The results indicate that utilizing XGBoost as a feature selection method enhances the performance of all classifiers. The highest performing classifier is achieved by XGBoost and Random Forest, resulting in an accuracy value of 0.727273. © 2024 IEEE.

关键词： Support vector machines

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

Enhancing Node Classification: Integrating Particle Swarm Op...

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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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Deep Learning Method for Sign Language Recognition: A Systematic Literature Review 9

Deep Learning Method for Sign Language Recognition: A System...

引用

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

作者： Nadita, Sekar Ayu Adelya, Lavender Nathania Susanto, Daniel Hendra Pangestu, Gusti School of Computer Science Bina Nusantara University Jakarta Computer Science Department 11480 Indonesia

ISBN: (纸本)9798350390025

This systematic literature review will discuss and explore the implementation of different types of artificial intelligence models including deep learning in recognizing and interpreting sign language. Nowadays, communication is an important aspect of human life. However, the communication ability of each human being is not the same, some have limitations in communicating such as deaf and speech impaired people so they need tools such as sign language. Unfortunately, not everyone can understand sign language and it has become a limitation in communication. This encourages researchers to continue developing various systems to solve this problem. In this review, we conducted an in-depth evaluation of 14 published articles that have been published with a focus on developing artificial intelligence systems for sign language recognition. The review was conducted by looking at the models, techniques, accuracy, and datasets used in each article. The expected results will be used as considerations for the creation of more advanced systems in the future. The results show the challenges of AI in understanding complex sign language gestures and expressions, but also show positive progress with various AI models, where ResNet-50 shows the highest accuracy rate reaching 99.98%. © 2024 IEEE.

关键词： Adversarial machine learning

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Performance Comparison of Machine Learning Methods for Flood Prediction 9th

Performance Comparison of Machine Learning Methods for Flood...

引用

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

作者： Linus, Josel Tanjaya, Vincent Diana Kurniawan, Afdhal Computer Science Department School of Computer Science Bina Nusantara University Jakarta14480 Indonesia

Flood is a disaster that causes problems. It causes problems such as destruction of property and disruption of civil activities. Predicting floods is a solution to prevent the devastating disaster and is crucial for mitigation. This study shows a comparison of various machine learning classification methods for flood prediction using a dataset consisting of humidity, wind speed, wind direction, location data, and other related features. We evaluated the performance of logistic regression, Naïve Bayes, K-Nearest Neighbors (KNN), decision tree, and random forest models based on metrics such as accuracy, precision, recall, and the area under the receiver operating characteristic curve (ROC-AUC). The results indicate that random forest achieved the highest accuracy at 95.902%, followed by KNN with 95.130%, decision tree with 91.448%, logistic regression with 79.513%, and naïve bayes with 77.256%. This comparative analysis highlights the strengths and limitations of each model, providing valuable insights for selecting the most appropriate approach for flood prediction. The results highlight the role of machine learning in creating an effective way of predicting floods. © 2024 The Authors.

关键词： Prediction models

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Comparative analysis of convolutional neural networks and vision transformers for dermatological image classification 9th

Comparative analysis of convolutional neural networks and vi...

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

作者： Saputra, Verren Angelina Devi, Marvella Shera Diana Kurniawan, Afdhal Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Skin is one of the outermost parts of the human body and is frequently exposed to various diseases. Healthcare professionals have endeavored to treat these conditions. However, the conventional methods often fall short in effectively distinguishing between diseases that visually present with similar characteristics and appearances. Therefore, Deep Learning was developed to facilitate image classification using the Convolutional Neural Network (CNN) model. Although CNN has achieved success in the form of high accuracy in image detection, research continues for the development of sustainable technology. Deep Learning has led to the discovery of the Vision Transformer (ViT) which is considered a popular new discovery model with intelligence that rivals CNN in object detection, offering greater accuracy and performance. This paper compares ResNet152 as one of the best models in CNN with ViT in classifying skin diseases. This comparison applies the HAM10000 dataset with a large number of images reaching 10,015 images divided for training, testing, and validation processes. In the process of applying the model to the selected dataset, ViT achieved 98.28% accuracy, while the classification accuracy of ResNet152 was 96.70%. Other metrics also proved ViT's superiority over ResNet152. However, ViT has major drawbacks in computation time and potential overfitting. © 2024 The Authors.

关键词： Convolutional neural networks

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Early Melanoma Skin Cancer Detection Using Artificial Intelligence: A Comparative Review 9

Early Melanoma Skin Cancer Detection Using Artificial Intell...

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

作者： Aqmarina, Nazhira Dewi Christiano, Lin Dan Adiwinata, Regina Celine Pangestu, Gusti School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Cancer is a deadly disease if not treated seriously, including the most visible part of the body, the skin. Melanoma is a deadly type of skin cancer. If this type of cancer is not treated quickly, it is likely to spread to other parts of the body and cause death. Artificial Intelligence can help humans overcome the problem of Melanoma by detecting it so that it can be treated as quickly as possible. This paper focuses on finding the model with the highest accuracy and knowing the factors that influence the accuracy value. This research uses the Systematic Literature Review (SLR) method, where the authors will search for papers related to the topic and research questions, review, analyze, and provide detailed conclusions on these papers. Of the hundreds of papers covering the topic, eight that met the criteria were taken for further analysis to find the best model accuracy. Among the models that demonstrate a high degree of accuracy are VGG19, ResNet-18, and ResNet-50, with 97.5%, 94.47%, and 93.96%, respectively. The selection of a suitable dataset strengthens the accuracy of the model. The results show that the HAM10000, ISIC 2019, and ISIC 2020 datasets are three datasets that produce high accuracy when used for matching models. So, factors influence the production of precise accuracy, such as the selection of models and datasets. © 2024 IEEE.

关键词： Lung cancer

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