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A review: Data pre-processing techniques used for diabetes prediction 9th

A review: Data pre-processing techniques used for diabetes p...

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

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Cmputer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

When processing datasets in diabetes classification, common problems included a large number of missing values, outliers, and dataset imbalance. To deal with those issues, this study analyzed 18 studies on diabetes classification with machine learning algorithms over the past 5 years. This revealed the important role of data pre-processing in creating effective classification models, as it was found that by using different data pre-processing techniques, the same model can provide different performance. The study identified K-Nearest Neighbor (KNN) and support vector machine (SVM) as superior methods for filling in missing values, achieving an accuracy of 98.49% and 94.89%, respectively. These approaches outperformed traditional methods such as median or mean replacement. However, the challenge of imbalanced data sets remains in all studies reviewed. The common evaluation metrics used to evaluate the created models in previous studies included accuracy, precision, specificity, sensitivity/recall, and F1 Score. Overall, this review showed that the role of data pre-processing is no less important than algorithm selection to improve the performance of machine learning models in diabetes classification. © 2024 The Authors.

关键词： Adversarial machine learning

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The Effect of Social Media Influencers on Business Sustainability: Understanding Customer Behavior Change Towards Sustainable Usage Patterns 6

The Effect of Social Media Influencers on Business Sustainab...

引用

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

作者： Herlina, Maria Grace Budiharto, Alexandra Li Lukito, Elizabeth Margaretha Iskandar, Karto Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

The study aims to fill existing research gaps by creating a new framework that explores the impact of social media influencers on sustainable usage patterns, focusing on business sustainability as a mediating factor. In this research, non-probability judgmental and purposive sampling methods were used. Due to the size and distribution of the population, sampling was also chosen based on the researcher's own judgment and experience. Structural equation modeling (SEM) is used as the data analysis method. Path coefficient analysis yielded significant findings. Social media influencers were found to influence business sustainability significantly. Apart from that, social media influencers have a significant negative affect on sustainable usage patterns. Business sustainability also significantly influences sustainability usage patterns, with a powerful influence. This enriches the dynamics of business sustainability, proposing new knowledge about how it influences this relationship. © 2024 IEEE.

关键词： Sustainable development goals

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Benchmarking CNN Models for Malaria Cell Detection

Benchmarking CNN Models for Malaria Cell Detection

引用

2024 International Conference on computer Engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Oktovianus, Louis Tikkhaviro, Jessica Kiyan Prasetyo, Simeon Yuda School of Computer Science Bina Nusantara University Tangerang Computer Science Department Tangerang Indonesia

ISBN: (纸本)9798350368802

Malaria is a life-threatening disease prevalent in tropical and subtropical regions, caused by Plasmodium parasites and transmitted by female Anopheles mosquitoes. Traditional diagnostic methods, such as manual microscopy and rapid diagnostic tests, face limitations in terms of accuracy and efficiency, necessitating more advanced diagnostic techniques. This study aims to evaluate and compare the performance of seven state-of-the-art convolutional neural network (CNN) architectures-VGG16, VGG19, DenseNet121, ResNet50, Xception, MobileNetV3, and EfficientNetB3-for the detection of malaria parasites in digitized blood smear images. The dataset used comprises thousands of annotated blood smear images, categorized into infected and uninfected cells. Each model's performance was assessed based on key metrics including accuracy, precision, recall, and Fl-score. EfficientNetB3 demonstrated superior performance with a diagnostic accuracy of 97.09%, significantly outperforming the other models. This study highlights the potential of deep learning models to enhance the diagnostic accuracy and efficiency of malaria detection, facilitating quicker and more reliable treatment interventions. The findings support the further development and deployment of these technologies, especially in resource-limited, malaria-endemic regions, thus potentially improving healthcare outcomes and reducing the burden of malaria globally. © 2024 IEEE.

关键词： Convolutional neural networks

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The Utilization of DistilBERT Features Space for Topics Suggestion in the Context of Learning Content

The Utilization of DistilBERT Features Space for Topics Sugg...

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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Jingga, Kenny Hidayaturrahman, Hidayaturrahman Bina Nusantara University School of Computer Science Computer Science Program Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350382914

Recommender system is one of the popular topics in artificial intelligence fields as it can widely be used in the *** service provider, e-commerce, e-learning, and many other fields can utilize recommender system to give the personalization for the users. This research will try to use recommender system to provide the recommended topics that are suitable to each learning content. As part of developing the most suitable recommender system, this research will also focus on the data pre-processing,as the data is still raw and contains too much unused information yet. Text vectorization or the embedding process was conducted to the dataset using DistilBERT, pre-trained BERT model. After the vectorization, the recommender system used cosine similarity from the result to discover the largest cosine similarity, which was used to determine the recommendation. Based on the experiment, using cosine similarity could do the recommendation well enough by giving the appropriate topics recommendation based on the content. For example, given the content: 'Chapter 2: Comments Chapter 2 of the book on C programming', the top 5 recommended topics were: 'Objective-C', 'C++', 'MATLAB','VBA', and 'Perl'. Based on the results, it can be considered that the recommender system had performed as expected. However,it still had a lot of areas that could be improved for the future research, especially in the data preprocessing. Other text vectorization models can be considered to be used, such as: BERT Multilingual, RoBERTa, and SpanBERT. Other consideration is the content preparation that will be used as the input for the system. Combination of DistilBERT and cosine similarity as the recommender system can be considered to be implemented for other areas. © 2023 IEEE.

关键词： Embeddings

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Optimizing Rice Variety Recognition Through Transfer Learning with Pretrained ResNet Models 4

Optimizing Rice Variety Recognition Through Transfer Learnin...

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

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

ISBN: (纸本)9798350376111

The identification of rice varieties holds paramount importance in addressing global food challenges, given that rice serves as a staple food source for more than half of the world's population. Accurate identification is crucial for ensuring food security and meeting the dietary needs of diverse populations. However, accurately distinguishing between rice varieties remains a complex task due to genetic mixing and hybridization, which can lead to variations in morphological and genetic traits among different varieties. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), offer promising solutions for improving rice variety classification by leveraging large datasets and sophisticated image processing techniques. This study conducts a comprehensive comparative analysis of ResNet50 and ResNet101 models in identifying rice varieties using transfer learning and fine-tuning approaches. Leveraging a dataset containing 75,000 high-resolution rice images representing various varieties, the models undergo thorough evaluation based on various performance metrics, including accuracy, precision, recall, and F1-score. Results highlight the effectiveness of pretrained ResNet models in accurately classifying rice varieties, with ResNet50 achieving the highest accuracy of 98.813% post-fine-tuning. These findings underscore the significance of fine-tuning pretrained models to enhance classification accuracy, contributing to the advancement of precision agriculture and sustainable food production practices. The insights gained from this study have implications for improving crop management practices, optimizing resource allocation, and ultimately, enhancing global food security. © 2024 IEEE.

关键词： Convolutional neural networks

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Implementation of A* and Dijkstra Algorithms for optimal Pathfinding: A Case Study of Nearest Hospital Location from bina nusantara University 6

Implementation of A* and Dijkstra Algorithms for optimal Pat...

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

作者： Wijaya, Febrio Dharma Bangun, Samuel Andreas Saputri, Hanis Amalia Sari, Azani Cempaka Computer Science Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia

ISBN: (纸本)9798331539603

In this study, we implement and evaluate A* and Dijkstra's pathfinding algorithms to find the nearest hospital from bina nusantara University (BINUS) in Jakarta, Indonesia. To address the urgent need for efficient routing in emergency situations, we first collect location data using Google Maps and model it in a graph structure. Both algorithms are then applied to determine the shortest path from the university to nearby hospitals. Dijkstra's algorithm consistently produces optimal paths, but its computational complexity increases with the number of nodes and edges, making it less efficient in densely connected urban areas. In contrast, the A* algorithm leverages heuristic estimates to prioritize nodes, reducing processing time and proving more effective for large-scale, dynamic navigation scenarios. Our evaluation shows that A* is more suitable for real-time pathfinding in cities, while Dijkstra’s algorithm is better suited for smaller or less complex networks. Future work could improve algorithm efficiency by incorporating real-time traffic data, developing advanced heuristics, exploring hybrid methods, and investigating alternative algorithms for urban navigation challenges. © 2024 IEEE.

关键词： Consensus algorithm

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Comparative Feature Extraction in KNN-Based Pneumonia Detection: PCA, HOG, SIFT, and LBP Analysis

Comparative Feature Extraction in KNN-Based Pneumonia Detect...

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2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Basit, Muhammad Banin Dzaki Shidqi, Muhammad Prasetyo, Simeon Yuda Setyo Purwanto, Eko Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331511470

Pneumonia remains a critical global health concern, underscoring the need for accurate, efficient diagnostic tools to support clinical decisions. This study evaluates the K-Nearest Neighbors (KNN) algorithm combined with three feature extraction methods - Principal Component Analysis (PCA), Histogram of Oriented Gradients (HOG), and Local binary Patterns (LBP) - for automated pneumonia detection in chest X-ray images. Using a dataset with both normal and pneumonia cases, each feature extraction method was assessed across K values from 1 to 9. Experimental results showed that HOG with K=9 achieved the highest accuracy at 96.51% and an optimal AUC of 99.30%, followed by PCA with an accuracy of 95.28% and an AUC of 9832%. LBP demonstrated limited effectiveness, with lower accuracy and AUC scores compared to HOG and PCA. These findings highlight the robustness of HOG and PCA as reliable, low-cost diagnostic approaches for pneumonia detection, particularly in resource-constrained settings. Future work could explore additional machine learning algorithms, hybrid feature extraction approaches, and broader dataset diversity to enhance model generalizability and clinical applicability. © 2024 IEEE.

关键词： Medical imaging

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Optimizing Carbon Capture Strategies through Advanced Analytics using Machine Learning 9th

Optimizing Carbon Capture Strategies through Advanced Analyt...

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

作者： Ziyaadaturrahman, Naufal Mahardhika, Garin Putra Fredyan, Renaldy Ibrahim, Muhammad Amien Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Carbon Capture Systems are a huge interest in the decrease of emitted carbon dioxide emission to combat climate change. Notwithstanding that the technologies so far have made huge strides in attempted successes. The gap subsists in the optimization of strategies for CCS technologies using machine learning methods to improve prediction accuracy and adequacy of fitted models. This gap justifies the purpose of the study, carried out to predict future CO2 emissions using two popular machine learning methods namely Linear Regression and Support Vector Regression. It uses the "EDA - CO2 Emissions Dataset" from Kaggle, cleaned and preprocessed for precision and reliability. Build and compare LR and SVR models based on performance measured with Mean Squared Error as the evaluation metric, along with Mean Absolute Error and R2 score. These results indicate that LR works better than SVR, it has a smaller value of mean squared error and mean absolute error, with a positive R2 score in terms of prediction accuracy and model fit. Hence, the present study suggests the use of linear regression while aiming to predict CO2 emissions. © 2024 The Authors.

关键词： Carbon capture and utilization

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Predictive Modelling on Caloric Expenditure: Exploring Machine Learning Techniques for Enhanced Analysis

Predictive Modelling on Caloric Expenditure: Exploring Machi...

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2024 International Conference on Control and Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Justin, Javier Tan, Kezia Revalina Edbert, Ivan Sebastian Suhartono, Derwin School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331511166

Predicting caloric expenditure accurately remains a significant challenges due to individual variability, which frequently results in mistakes in established approaches such as metabolic computation and self-reported data. This research tackles this issue by examining the efficacy of several machine learning methods for forecasting calorie expenditure. The study compares a variety of models, including Linear Regression, eXtreme Gradient Boosting (XGB), Gradient Boosting Regressor (GBR), Ada Regressor, ExtraTrees Regressor, Random Forest, and Stacking Regressor. The XGBoost model has outstanding performance characteristics, including a mean absolute error (MAE) of 8.494312, mean square error (MSE) of 159.6425, root mean square error (RMSE) of 12.6350, and a high R-squared (R2) score of 0.9603. These results demonstrate that machine learning algorithms can produce more precise forecasts of calorie expenditure than traditional methods. This study improves knowledge and implementation of predictive models in the realm of health by employing sophisticated modeling methodologies. © 2024 IEEE.

关键词： Adversarial machine learning

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A Systematic Literature Review: Challenges of Security Optimization while Maintaining a Good Accessibility in Internet of Things 3

A Systematic Literature Review: Challenges of Security Optim...

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

作者： Tampubolon, Devin Gunadi Widodo, Gerald Nathaniel Margaretha, Janet Sweetmay Gunawan, Alexander Agung Santoso Edbert, Ivan Sebastian School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

The Internet of Things (IoT), which has had a tremendous development in recent years, has altered many aspects of daily life by giving rise to commercial tools, home automation, wearable technology, and the framework for smart cities. The modern fantasy of having remote control over several parts of life has been realized because to this continuous connectivity and the ability of devices to acquire and interpret information. However, the various features and functionalities of IoT devices are a double-edged sword because they introduce a number of security flaws and dangers. Through a Systematic Literature Review (SLR), we explore vulnerabilities and their mitigation strategies based on prior research in the field. Notably, while the importance of security in IoT is paramount, especially considering the sensitivity of data involved, there exists a challenge in striking a balance between robust security and user convenience. It's imperative that while bolstering security, we do not compromise on user accessibility. This paper presents a comprehensive discourse addressing the dual challenges of security and accessibility in the IoT landscape. Our findings underscore the urgent need for layered security protocols, encryption, and user authentication to counteract the escalating cyber threats. We conclude by proposing a balanced solution that ensures both security and accessibility in the IoT ecosystem. © 2023 IEEE.

关键词： Internet of things

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