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The Explainable Analytics for Exploring Misdiagnoses 24

The Explainable Analytics for Exploring Misdiagnoses

引用

8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Pai, HAO-Ting Chung, Wen-Cheng Fang, Xin-Hong Hsu, Yu-Hsin Huang, Shu-Ting Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Bachelor Program of Artificial Intelligence National Yunlin University of Science and Technology Douliou Taiwan

ISBN: (纸本)9798400716874

Current AI-driven methods in healthcare show significant limitations in addressing misdiagnoses, often leading to serious consequences. This work highlights the inadequacy of state-of-the-art AI in managing diagnostic errors through experiments with public real-world datasets, namely "Bone Marrow Transplant: Children" and "Breast Cancer Wisconsin (Original)". We propose two novel scoring measures—Local-score and Global-score—to enhance the accuracy of Transparent Classification (TC). Experimental results indicate that Global-score improves recall but may reduce precision, whereas Local-score slightly lowers recall for better precision. These findings suggest that both Local-score and Global-score can effectively identify misdiagnoses, marking a substantial improvement over traditional AI methods. This research offers a new perspective in employing explainable AI to address the challenges of misdiagnoses, thereby improving patient care and medical diagnostic accuracy. © 2024 ACM.

关键词： Explainable artificial intelligence

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Regression-Based Exploration of Factors Influencing Movie Profits

Regression-Based Exploration of Factors Influencing Movie Pr...

引用

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

作者： Veronica, Lindawaty Wibowo, Tifara Beata Gunawan, Alexander Agung Santoso Setiawan, Karli Eka School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350366648

Accurate box office prediction is crucial for managing financial risks in film production. The internet has transformed consumer behavior, affecting marketing strategies. Critical online reviews, more than early revenue, predict a film's profit, while establishing film critics as the predictors. Hence, this study investigates the intricate relationship between ratings and movies profit. While also finding the best model to predict those variables. This research was conducted through a comprehensive process encompassing data collection and processing, feature selection, prediction modeling, and a detailed discussion of the conducted analysis. Several manual interventions were applied to the data to support further discoveries and analysis in this research. These interventions were carried out based on a solid foundation to enhance the depth of exploration and understanding within the study. The findings reveal that the random forest regression emerged as the most effective in this study, achieving the Mean Squared Error (MSE) of 768.40. Leveraging a dataset of 500 movies from ***, incorporating critics' ratings from ***. Through this research, industry professionals not only enhance their understanding but also identify factors that can be considered to increase a movie's revenue. © 2024 IEEE.

关键词： Prediction models

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HIV Inhibitory Activity Classification with Morgan Fingerprint and Integrated Graph-Based Deep Learning 12

HIV Inhibitory Activity Classification with Morgan Fingerpri...

引用

12th International Conference on Information and Communication Technology, ICoICT 2024

作者： Verna Siswanto, Jocelyn Angelina Sunarya, Caroline Calista, Agnes Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350363432

This research offers a new perspective on predicting the activity of the HIV virus from the Drug Therapeutics program (DTP) Antiviral Screen by using the molecular data represented in SMILES notation. The topic has significance as it focuses on a major global health issue using modern computational approaches and has the potential to uncover new antiviral drug candidates, which could eventually save lives and improve public health outcomes. The study addresses the data imbalance between two classes, active and inactive, and employs the Morgan Fingerprint method for feature extraction, along with the Graph Convolutional Network (GCN) and Graph Attention Network (GAT) as the baseline architectures and the fusion of GCN's and GAT's main features as the proposed architecture. The random oversampling technique is applied to alleviate dataset imbalances. However, even though it improved the training process, the performance of the model flopped when the test set was fed into the model. Combining the main features in GCN and GAT, the proposed model was able to do the classification task more accurately. The attention mechanism from GAT allows the model to focus more on the parts that are more relevant and ignore the irrelevant ones. It managed to outperform the baseline models. Despite a high overall accuracy of 94%, the fusion model exhibits significant disparities in precision, recall, and f1-score metrics, potentially due to class imbalance. Random oversampling led to improved training but compromised model performance on the test set. © 2024 IEEE.

关键词： Diseases

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Machine Learning for Imbalanced data in Telecom Churn Classification

Machine Learning for Imbalanced Data in Telecom Churn Classi...

引用

2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Dewi, Olivia Intan Permata Santiko, Vina Nathania Safa, Snow White Putri Gunawan, Alexander Agung Santoso Setiawan, Karli Eka Bina Nusantara University School of Computer Science Data Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376210

In the highly competitive telecommunications industry, customer churn poses a significant threat to revenue and long-term sustainability. To proactively address this challenge, machine learning (ML) techniques offer promising solutions for predicting and mitigating customer churn. This study applies ML algorithms to churn prediction, focusing on the challenges of imbalanced data. Using a publicly available Telecom dataset from Kaggle, the data was meticulously prepared through a multi-stage process, including Random Under-Sampling (RUS) to address class imbalance. The study employed a range of ML algorithms, evaluating their performance using key metrics such as recall, precision, F1score, and accuracy. The findings reveal valuable insights into how RUS can enhance model performance by improving the prediction of the minority class. XGBoost outperformed Logistic Regression and Random Forest in accurately identifying positive outcomes, suggesting its effectiveness in recognizing true instances of customer churn. The confusion matrix supports this observation, showing that the recall value produced by XGBoost slightly exceeded those for Logistic Regression and Random Forest. © 2024 IEEE.

关键词： Logistic regression

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Predicting Company Default Risks: A Machine Learning Perspective on Importance Features

Predicting Company Default Risks: A Machine Learning Perspec...

引用

2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Hanestan, Fanes Nathaniel, Fritz Kusuma, Raiyen Dewi Santoso Gunawan, Alexander Agung Setiawan, Karli Eka Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350353464

The 2008 Global Financial Crisis was a devastating event for financial market investors worldwide, as it came unexpectedly and affected every financial market in the world, causing millions of people to suffer massive loss of value of their pension funds and assets. The trigger cause of this Global Financial Crisis was the default of Lehman Brothers, one of the biggest companies in the United States. This paper is intended to prevent this event from happening again by predicting the most important variables causing company default. Company management and investors could take preventive measures when they see signs of these variables in their company. This experiment uses XGBoost, Support Vector Machine, and Random Forest machine learning model, with data retrieved from the Taiwan Economic Journal from 1999 to 2009. The experiment suggests the top 5 most important variables regarding company default are Continuous interest rate (after tax), Persistent EPS in the Last Four Seasons, Total debt/Total net worth, Interest Expense Ratio, and Net Value per Share, with an importance score of 0.447, 0.104, 0.038, 0.033, and 0.032, respectively. These findings show that features related to a company’s profitability and liabilities have a strong impact on a company’s risk of defaulting. ©2024 IEEE.

关键词： Decentralized finance

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Classification of Health Insurance Fraud Risk with Machine Learning

Classification of Health Insurance Fraud Risk with Machine L...

引用

2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Al-Ghazi, Muhammad Kent Bertrand, Ryan Destra, Muhammad Dzul Qarrnayn Gunawan, Alexander Agung Santoso Setiawan, Karli Eka Bina Nusantara University School of Computer Science Data Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Department School of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9798350376210

Health insurance is a useful service that can help its users gain lifesaving medical aid when they are in need. However, health insurance is also exploitable to insurance fraud through the falsification of information to increase the amount of reimbursement and cause massive loss of funds to the insurance provider. We propose the usage of machine learning to accurately determine potential health insurance fraud. The objective of conducting this research is to determine which features are the most important to determine healthcare insurance fraud. This research used a dataset provided in Kaggle titled Healthcare Provider Fraud Detection Analysis using Random Forest Classifier and Logistic Regression. The best-performing model in this test, the Logistic Regression, is then used to which features are the most important for the classification. Our research shows that the most important feature in detecting health insurance fraud is the amount of money reimbursed associated with a provider. The Logistic Regression model achieved an accuracy of 0.90, precision of 0.93, recall of 0.91, and an F1 Score of 0.90, outperforming the Random Forest model in comparative analysis. © 2024 IEEE.

关键词： Logistic regression

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Evaluating Public Opinion on the 2024 Indonesian Presidential Election Candidate: An IndoBERT Approach to Twitter Sentiment Analysis 10

Evaluating Public Opinion on the 2024 Indonesian Presidentia...

引用

10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Tanaja, Rafael Nicholas Widjaya, Andrew Johnny Gunawan, Alexander Agung Santoso Setiawan, Karli Eka School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

As social media significantly shapes societal norms and ethical paradigms, understanding real-time public sentiment provides valuable insights for political parties to evaluate their candidates, key issues, and campaign strategies. This study aims to gauge each presidential candidate's public sentiment from August 1, 2023, until October 31, 2023, using Twitter data. In this study, the authors would like to conduct an IndoBERT with a Fully Connected Layer to predict the sentiment analysis of Indonesian public sentiment toward the 2024 upcoming presidential election. The paper concluded that the IndoBERT model achieved an accuracy of 84.7%. In conclusion, the findings of this study lay the groundwork for more extensive analyses, enriching our comprehension of the intricate dynamics of public opinion in the political sphere. Furthermore, Sentiment Analysis is dynamic and will change as significant events occur. As we look to the future, integrating cutting-edge language models like IndoBERT with real-time social media data emerges as a transformative force in reshaping political analysis. © 2024 IEEE.

关键词： Tweets

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Image-Based Natural Disaster Classification with Deep Learning: A Comparative Analysis

Image-Based Natural Disaster Classification with Deep Learni...

引用

2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Alamsyah, Samuel Eka Djamhari, Simen Ferdinand Sudjono, Salomo Hendrian Gunawan, Alexander A.S. Setiawan, Karli Eka Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350353464

Natural disasters, including earthquakes, cyclones, floods, and wildfires, cause significant environmental damage and have emerged as a major global issue. These events can result in loss of life and disrupt communities and economy. Effective disaster management relies on timely and accurate information. With social media becoming a primary source of real-time information during such events, many images of these disasters are shared, though some may not be accurately labeled. Therefore, the purpose of this study is classifying natural disaster images, mainly cyclone, wildfire, flood, earthquake from social media images. This paper is to conduct a comparative analysis over 4 models namely VGG16, VGG19, EfficientNetB0, and ResNet-50. This paper uses a dataset that contains 4428 human-annotated images including cyclones, earthquakes, floods, and wildfires. Results concluded that ResNet-50 and EfficientNetB0 give the best result with both got over 96% average accuracy. ResNet-50 delivers more stable and more convergent results compared to EfficientNetB0, but it delivers slightly less classification accuracy. VGG16 and VGG19 are both outclassed in terms of accuracy, but better in training time, with VGG19 having a more convergent result. ©2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Tourism Industry Insights: LeveragingLDA and KMeans for Topic Extraction in Indonesian OTAs' Reviews 12

Enhancing Tourism Industry Insights: LeveragingLDA and KMean...

引用

12th International Conference on Information and Communication Technology, ICoICT 2024

作者： Castilani, Laurentia Alyssa Winata, Natasha Hartanti Nugraha, Nathania Christy Gunawan, Alexander Agung Santoso Setiawan, Karli Eka Wulandari, Lili Ayu School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363432

Indonesia's tourism sector, boosted by its captivating landscapes, has seen a rise in the popularity of Online Travel Agencies (OTAs) like Traveloka, ***, and Agoda. To effectively assist tourists, OTAs are anticipated to comprehend tourists' needs and interests, with insights gathered through scraping data from the Google Play Store. Through topic extraction from customers' reviews, this research aims to optimize the tourist experience and offer actionable insights for the tourism industry's marketing strategies. Furthermore, it endeavors to eliminate the reliance on brute force approaches in determining the candidate number of topics. To achieve the research goal, this research employs topic modeling, specifically Latent Dirichlet Allocation (LDA) enhanced through the K- means elbow method. The evaluation of the optimal topic number utilizes a coherence score, while human judgement serves as a quantitative metric for overall performance, with a remarkable validity rate across three platforms: Agoda (91%), *** (88%), and Traveloka (94%). This highlights the approach's effectiveness in accurately identifying and categorizing topics. In conclusion, the model accurately discerned essential topics, revealing that the majority of the reviews on these OTAs focus on the transaction and refund systems on each platform. Furthermore, this model offers promising recommendations to enhance the understanding and response to customers' reviews, facilitating faster and more effective responses. These insights are invaluable for continuous development and improvement strategies within the dynamic changes in the tourism industry across these influential platforms. © 2024 IEEE.

关键词： Sales

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Assessing IT Job Candidates Through Interpretable Machine Learning Model 10

Assessing IT Job Candidates Through Interpretable Machine Le...

引用

10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Sukardi, Andrew Saputra, Jayagatha Hariman, Sebastian Gunawan, Alexander Agung Santoso Setiawan, Karli Eka School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

The escalating demand for skilled IT professionals underscores the increasing significance of the recruitment process. Traditional methods often fall short in identifying individuals poised for success in the dynamic technology industry. This paper aims to address the challenges in recruiting proficient applicants by proposing an interpretable machine learning model for assessing IT job candidates. The study conducts experiments comparing various machine learning algorithms, including Logistic Regression, Random Forest, and Decision Tree, using data from the 'IT job applicant' public dataset. Evaluation methods such as the confusion matrix, accuracy, recall, and precision are employed to assess the model's performance. The findings reveal that the decision tree exhibits an accuracy exceeding 99.8%, establishing it as the top-performing model. The key contributing factor to this accuracy is identified as the 'ComputerSkills' feature. Additionally, within the decision tree, *** and Microsoft SQL Server are highlighted as influential factors positively impacting individual decision interpretation. © 2024 IEEE.

关键词： Adversarial machine learning

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