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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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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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Towards Adapting CLIP for Gaze Object Prediction

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Proceedings of the ACM on Human-computer Interaction 2024年第ISS期8卷 155-164页

作者： Chen, Dazhi Gou, Gang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

This paper aims to investigate the problem of gaze object prediction in single images. We propose an application-friendly network based on CLIP for gaze object prediction. To avoid domain bias, we utilize a shallow feature adapter that transfers pre-trained features to target-oriented ones. Secondly, we introduce a pooling attention block to exploit the joint representation of multimodal elements, reducing gaze point deviation. Additionally, we introduce a loss that measures the prediction quality by comparing the distribution difference between the model's predictions heatmaps and the ground truth. Extensive experiments demonstrate the superior performance of our model compared to previous models. We will provide the method code at: https://***/fadaishaitaiyang/***. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Prediction models

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

Breast Cancer Image Classification Obtained Through Dynamic ...

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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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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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Leveraging XGBoost and Feature Importance for NBA Salary Classification

Leveraging XGBoost and Feature Importance for NBA Salary Cla...

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

作者： Rafif, Musyaffa Ayman Daksa, Rayhan Prawira Indrajaya, Gede Sanjaya Gunawan, Alexander A S Setiawan, Karli Eka Bina Nusantara University Data Science Program Computer Science Department School of Computer Science Jakarta11480 Indonesia Bina Nusantara University Computer Science Department School of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9798350376210

Focusing on feature importance, this study utilized the XGBoostClassifier to predict NBA player salaries within categorized salary bands. Oversampling techniques ensured balanced class representation, while feature importance analysis identified Minutes Played, Age, Points Scored, Games Started, and Turnovers as key factors influencing model predictions. The XGBoostClassifier achieved a balanced accuracy of 83% across all salary tiers, demonstrating its ability to navigate NBA player valuation complexities. Focusing on these key features further boosted average accuracy to 85%, highlighting the potential for optimization through prioritizing impactful attributes. Our findings suggest valuable avenues for future research, including leveraging feature importance for model refinement, expanding the dataset size, incorporating more relevant features, and investigating outliers for deeper insights into the multifaceted dynamics of NBA player salary classification. © 2024 IEEE.

关键词： Wages

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Spatio-Spectral Graph Neural Networks 38

Spatio-Spectral Graph Neural Networks

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Geisler, Simon Kosmala, Arthur Herbst, Daniel Günnemann, Stephan Department of Computer Science Munich Data Science Institute Technical University of Munich Germany

Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning on graph-structured data. However, key limitations of -step MPGNNs are that their "receptive field" is typically limited to the -hop neighborhood of a node and that information exchange between distant nodes is limited by oversquashing. Motivated by these limitations, we propose Spatio-Spectral Graph Neural Networks (S2GNNs) - a new modeling paradigm for Graph Neural Networks (GNNs) that synergistically combines spatially and spectrally parametrized graph filters. Parameterizing filters partially in the frequency domain enables global yet efficient information propagation. We show that S2GNNs vanquish over-squashing and yield strictly tighter approximation-theoretic error bounds than MPGNNs. Further, rethinking graph convolutions at a fundamental level unlocks new design spaces. For example, S2GNNs allow for free positional encodings that make them strictly more expressive than the 1-Weisfeiler-Leman (WL) test. Moreover, to obtain general-purpose S2GNNs, we propose spectrally parametrized filters for directed graphs. S2GNNs outperform spatial MPGNNs, graph transformers, and graph rewirings, e.g., on the peptide long-range benchmark tasks, and are competitive with state-of-the-art sequence modeling. On a 40 GB GPU, S2GNNs scale to millions of nodes. © 2024 Neural information processing systems foundation. All rights reserved.

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Trends in The Sustainability of Related Blockchain Technologies and Bitcoin 5

Trends in The Sustainability of Related Blockchain Technolog...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Pavitra, Mutyala Bardhiya, Himanshu S Gourshettiwar, Palash Computer Science and Design Sawangi India Artificial Intelligence & Data Science Sawangi India Computer Science and Medical Engineering Sawangi India

ISBN: (纸本)9798350383522

Based on an open-source software-powered peer-to-peer payment mechanism, Bitcoin is a digital currency that operates independently of a central bank. Its advantages over fiat money include cheaper transaction costs, increased security, and scalability. Scientists are becoming interested in it despite concerns about illicit usage and societal repercussions. This paper aims to describe and assess existing patterns inside the literature about the sustainability of bitcoin, considering social, economic, and environmental implications. Based on the study, it is evident that the entire system of money transitioning would need an exorbitant quantity of energy to the new cryptocurrency to operate the virtual currency system and mine new bitcoins, meaning that bitcoin is likely to remain a niche currency. A fragmented and democratic maintained public record of transactions is built on blockchain technology, which may open up new and difficult prospects. Block-layer and new state-driven cryptocurrency, the sharing of medical data, the production and distribution of energy in microgrids at the citizen level, and blockchain technology may all gain from the broad adoption of blockchain-based transactions. Bitcoin and associated blockchain technologies have the potential to transcend the concerns raised by many critics when seen as a catalyst for social change. © 2024 IEEE.

关键词： bitcoin digital money energy usage monetary transactions sustainability

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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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