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检索条件"机构=School of Data and Computer Science"

共 16881 条记录，以下是71-80 订阅

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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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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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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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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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Enhancing Online Learning Experience: An Ensemble Approach for Classifying Student Adaptivity Level 4

Enhancing Online Learning Experience: An Ensemble Approach f...

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

作者： Mutiara, Belinda Florencia Ariyanto, Gabrielle Felicia Gunawan, Alexander Agung Santoso Setiawan, Karli Eka Bina Nusantara University Data Science Program Computer Science Department School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376111

The study investigates the increasing demand of online learning as a means of addressing education issues in the context of the COVID-19 epidemic. Online learning requires several adaptations for teaching methods, learning methodologies, and devices needs. The flexibility of both teachers and students is essential to these adaptations. This research uses dataset collected from a survey about student adaptability level in online education. Carrying out the preprocessing is a challenge as the data used in this research has imbalanced value on the target category. Based on this problem, the aim of this research was created, namely to categorize students' adaptivity levels in online learning and also focuses on finding out approaches that can provide solutions to overcome imbalanced values in the dataset. The model uses ensemble methods - Bagging, Boosting, and Voting with machine learning algorithms. There are two models that stand out, with Soft Voting obtaining the best performance with 9 0% accuracy. © 2024 IEEE.

关键词： Federated learning

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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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Breast Cancer Image Classification Obtained Through Dynamic Thermography using Deep Learning 4

Breast Cancer Image Classification Obtained Through Dynamic ...

引用

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

作者： Timadius, Ethan Daniel Wongso, Reyner Baihaqi, Thirza 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)9798350376111

This paper addresses the vital task of diagnosing breast cancer early and accurately. It acknowledges the difficulty in detecting minor variations in breast cancer patterns through visual examination alone, particularly when analyzing gray scale images. To overcome this challenge, our research concentrates on using image processing models, particularly Convolutional Neural Networks (CNNs), and enhances them with a refined approach to classification. The research methodology encompasses rigorous data collection, preprocessing, and strategic feature selection to enhance model robustness. To address challenges related to data intricacies, the study incorporates transfer learning, refining the model's training process for improved accuracy. The paper not only seeks to elevate diagnostic precision and minimize errors but also aims to provide healthcare professionals with efficient tools for breast cancer detection. Building upon prior research, the study underscores the potential of deep learning methodologies in advancing breast cancer diagnosis. Furthermore, the fine-tuning process involves adapting a pre-trained model to the intricacies of breast cancer patterns, optimizing its performance. The ultimate goal is to contribute to the development of reliable diagnostic tools that empower medical professionals in the fight against breast cancer. The paper concludes by summarizing key findings. Additionally, the model achieved an accuracy of 84%, highlighting its effectiveness in breast cancer detection. The paper also emphasizes the broader significance of leveraging deep learning for enhanced breast cancer detection. © 2024 IEEE.

关键词： Breast Cancer Convolutional Neural Network Deep Learning Image Classification Thermal Image

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

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

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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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Viral Melodies: Exploring the Factors Influencing Music Virality in TikTok Engagement 4

Viral Melodies: Exploring the Factors Influencing Music Vira...

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

作者： Rahardjo, Evelyn Zefanya Alifa, Jasmine Mutia Setiawan, Sharon Zefanya Gunawan, Alexander Agung Santoso Setiawan, Karli Eka Bina Nusantara University Data Science Program Computer Science Department School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376111

In the digital era and the evolution of social media platforms like TikTok, understanding the factors influencing content virality has become increasingly crucial. Therefore, this research aims to delve into the musical variables contributing to the popularity of content on TikTok, including aspects such as artist, beat of music, and the number of shares. Focusing on content that successfully garnered over 5, 0 0 0 likes, this study employed a data analysis approach involving the librosa library for music tempo extraction and logistic regression to identify relationships among these variables. The research findings indicate that the most significant variable influencing the virality of music content on TikTok is the number of shares. This highlights that the phenomenon of virality is not solely dependent on musical characteristics like artist or beat but is more influenced by social interactions through video sharing among users. The findings suggest that when someone shares a video with others who may have similar music preferences, the video is likely to receive likes, even if it does not appear on the user's For You Page (FYP). Thus, the virality of content on TikTok can be explained by the existence of social connections among friends that trigger the viral spread of the video. These results provide valuable insights into the mechanisms behind the success of conten/t on this platform. © 2024 IEEE.

关键词： content virality data analysis music selection tiktok user engagement

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Analyzing Nickel Stock Price Prediction: A Comparative Study of Long Short-Term Memory and Convolutional Neural Network Models

Analyzing Nickel Stock Price Prediction: A Comparative Study...

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

作者： Irwanto, Ferry Hafizh Sasongko, Muhammad Farrell Febrian, Stevanus 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

This research delves to predict PT Vale Indonesia Tbk stock price as an experiment on Indonesian stock using three models: naïve, LSTM, and 1D-CNN. Our analysis emphasizes the importance of matching model architectures to data properties. We compare models' performance with fiveday window for predict one-day prediction output. Interestingly, the single-layer LSTM outperforms the 1D-CNN even with similar hyperparameters, showcasing its strength in capturing long-term temporal dependencies crucial for nickel prices. While the 1D-CNN excels at identifying short-term patterns, its limited receptive field hinders long-term dependence. Recognizing the potential of both models, we encourage exploring hybrid architectures combining LSTM and CNN strengths for further improvement in financial forecasting. The experiment result shows single-layer LSTM outperforms a 1D-CNN with similar settings. ©2024 IEEE.

关键词： Convolutional neural networks

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