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Identifying Factors for Supporting Early Warning Flood using Clustering Approach And Geo-Spatial Analysis 8

Identifying Factors for Supporting Early Warning Flood using...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Duykers, James Lewi Ardana, Kidung Yulistiani, Risma Irwansyah, Edy Fitrianah, Devi Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Floods in Jakarta are very frequent and caused by many factors, from very high rainfall to flash floods. This natural disaster has greatlyimpact the society, from economy to health problems. In this research, we used ArcGIS Pro with unsupervised clustering method HDBSCAN to create a prototype that would make it easier for people to find out the area that is prone to floods. We received a lot of positive input from many respondents who had seen our prototype design and how it can assist people as the early warning to floods. We analyzed the factors lead the flood early warning system, those factors are high density population distance from river to the high-density population area, drainage system, rainfall intensity, and flood history. We will group flood point with the unsupervised clustering method. © 2023 The Authors. Published by Elsevier B.V.

关键词： clustering early warning system flood HDBSCAN Jakarta unsupervised learning

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"Swap Try": Proposed Design and Development of a Charging Station Map Mobile Application for Electric Vehicles

"Swap Try": Proposed Design and Development of a Charging St...

引用

2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Priatna, Kevin Kostaman, Michael Varian Aryanto, Michelle Angela Radika, Natasha Grace Gabriel, Nelsen Halim, Patrick Roberto Andryani, Nur Afny Catur Alam, Islam Nur Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350307306

Climate change issues have become a worldwide concern. The transportation sector contributes significantly to greenhouse gas emissions. Electric vehicles (EVs) offer a potential solution. However, the lack of charging infrastructure hampers adoption. Instead of focusing on EV development which has been widely discussed, this study emphasizes the need to address the charging infrastructure support. leveraging the ubiquity of mobile devices, the design and development of mobile applications that support surrounding charging station detection is proposed. The software development process uses the Waterfall method. The development of the application is built on a mobile platform, integrating authentication, maps, and location APIs. The app is tested using usability testing, measured based on functionality, components testing, and 10 key heuristics. The results of the usability testing highlighted the efficiency, memorability, and satisfaction of the app. To enhance the effectiveness of the proposed solution, the research recommends improving user testability and feedback to contribute to more efficient utilization of EVs and charging stations with continuous expansion of charging networks, incorporation of renewable energy sources for charging, and policy incentives to encourage EV adoption. © 2023 IEEE.

关键词： Climate change

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Transforming Sign Language using CNN Approach based on BISINDO Dataset

Transforming Sign Language using CNN Approach based on BISIN...

引用

2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Ahmad, Nizhamuddin Wijaya, Eko Saputra Tjoaquinn, Calvin Lucky, Henry Iswanto, Irene Anindaputri Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350307306

The rapid growth of technology has a significant role in developing technologies that can support communication for hearing or speech impaired individuals that enables a real-time translation into visual or written language visually. These rapid growth and advancement create an opportunity to connect the gap between impaired individuals and others for the sake of fostering the society. Sign language is commonly used by hearing or speech impaired people to communicate with others. BISINDO is Indonesian Sign Language used by deaf individuals in Indonesia. Based on BISINDO proposed a convolutional neural network model to detect and recognize sign language to accommodate a communication between impaired people. CNN is a deep leaning algorithm designed specifically to handle processing and analyzing visual data in terms of images and video. It consists of various convolutional layers that extract and analyze local features and patterns to allow object recognition and image classification. In addition to that CNN is the best possible and ideal algorithm due to their capabilities of capturing information, learning patterns and extraction with variety of hand gestures while achieving them in real time. Hence CNN is used in our model, Since the main objective of this paper is to create the most accurate and efficient model from an existing dataset. We obtained the results by using CNN, by gathering necessary data, data processing, model training and testing. Based on CNN we are able to achieve an accuracy of 82.56%. © 2023 IEEE.

关键词： Audition

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Abstractive Text Summarization Using BERT for Feature Extraction and Seq2Seq Model for Summary Generation

Abstractive Text Summarization Using BERT for Feature Extrac...

引用

2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Widowati, Kania Galih Budiman, Nicholas Foejiono, Kezia Purwandari, Kartika School of Computer Science Bina Nusantara University Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350369359

Humans are very easy to access various existing data about text using search engines and text generate applications. Text summarization is a technological development of the Natural Language Processing branch which takes points and main information from a paragraph. This is done to increase efficiency in understanding a paragraph or document. In this study, we used the type of abstractive text summarization, which is a summarization resulting from deeper sentence understanding. That the resulting summarization will produce a new summary that is not always in the input text, so this will increase the reader's understanding. In text summarization, various methods have been used such as Support Vector Machine (SVM), Long Short Term Memory (LSTM), Bayesian Networks, and so on. In this study, we used pre-trained model Bidirectional Encoder Representations (BERT) derived from transformers, and Sequence to Sequence (Seq2Seq) and the dataset used was the CNN Daily Mail dataset. The pre-trained BERT model is used to perform feature extraction, the results will be forwarded by Seq2Seq for summarization. We also conduct evaluations using ROUGE, by comparing the results of the summary model with the results of the summary made by humans. ROUGE's evaluation results show that typing accuracy in shorter articles is not significantly different from that in longer articles. © 2023 IEEE.

关键词： Feature extraction

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Facial Emotion Recognition with InceptionV3 CNN Architecture

Facial Emotion Recognition with InceptionV3 CNN Architecture

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Timothy, Jason Prasetio, Raymond Tandi, Darwin Lucky, Henry Iswanto, Irene Anindaputri Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350307306

The development of deep learning algorithms in recent years has pushed researchers to explore many different topics, one of the most popular being emotion recognition. The emotions that have evolved in the human face have a significant impact on decisions and disputes about a variety of topics. The automatic extraction of these emotions from face photos can aid in human-computer interaction and a variety of other applications. Emotion recognition plays a huge role in many different fields, such as education to analyze a student's facial expression when listening to lectures to help teachers evaluate their teaching method, and even other fields, such as medicine & security, to help analyze irregular behaviors through emotion recognition. By utilizing deep neural networks, the ability to classify patterns and learn complex features is becoming easier. This paper describes how to utilize a deep learning algorithm, the Convolutional Neural Networks (CNN) with InceptionV3 architecture commonly used for Image Recognition to classify seven different emotion classes (angry, afraid, disgusted, happy, neutral, sad, and surprised) with high accuracy. The architecture was chosen instead of other architectures, such as Resnet50 and VGG19, due to the efficiency and less computing power it needs. The Karolinska Directed Emotional Faces (KDEF) dataset was used to train the model which consists of 980 pictures of human faces, each labeled with one of the 7 emotion categories. With fine-tuning of the hyperparameters, the model achieves a maximum accuracy of 91.33% © 2023 IEEE.

关键词： Emotion Recognition

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Machine Learning Approaches in Detecting Autism Spectrum Disorder 8

Machine Learning Approaches in Detecting Autism Spectrum Dis...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Daniel Dominic, Nicholas Cenggoro, Tjeng Wawan Pardamean, Bens Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classification between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 related papers to answer four research questions, i.e., what are the most used data inputs, brain atlases, and machine learning models for ASD classification, as also to discover the significant parts of the brain correlated with the ASD. It was eventually found that functional connectivity matrix, Support Vector Machine, and CC200 are the most frequently used data input, model, and brain atlas, respectively. Researchers also concluded that the posterior temporal fusiform cortex, intracalcarine cortex, cuneal cortex, subcallosal cortex, occipital pole, and lateral occipital cortex are the brain regions highly correlated with ASD. © 2023 The Authors. Published by Elsevier B.V.

关键词： autism spectrum disorder brain atlas brain region machine learning

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Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model 8

Sentiment Analysis for TikTok Review Using VADER Sentiment a...

引用

8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

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 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app;there are criticisms and suggestions, one of which is reviewed via the TikTok app on the Google Play Store. The reviews were extracted and then used for training a sentiment analysis model. The VADER sentiment method was utilized to offer the review's initial labeling (positive, neutral, and negative). The result revealed that most reviews were classified as positive, meaning that the data were imbalanced and challenging to handle in further analysis. Therefore, Random Under-sampling (RUS) and Random Over-sampling (ROS) methods were deployed to deal with that condition. The labeled reviews were subsequently pre-processed using tools such as case folding, noise removal, normalization, and stopwords before being used for training a Support Vector Machine (SVM) model for sentiment classification. The SVM trained without resampling produced the most favorable results, with an F1-score of 0.80. © 2023 The Authors. Published by Elsevier B.V.

关键词： Google Play Store Sentiment Analysis SVM TikTok VADER Sentiment

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Optimizing Pneumonia Classification on Pediatric Chest X-Ray Images Using ConvNeXt Network 9

Optimizing Pneumonia Classification on Pediatric Chest X-Ray...

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

作者： Prasetyo, Simeon Yuda Prabowo, Abram Setyo Santy Pepita, Patricia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Pneumonia is a major worldwide health concern, particularly among children, and early and correct diagnosis is critical for successful treatment. This study addresses the challenges in classifying pediatric pneumonia on chest X-ray (CXR) images, where issues such as limited image data and non-standardized interpretation persist. Utilizing machine learning and deep learning techniques, notably Convolutional Neural Networks (CNNs), provides potential answers. The research explores the optimization of pneumonia classification in pediatric CXR images using the ConvNeXt network, an advanced CNN architecture. Through experiments utilizing five pretrained ConvNeXt models, the study evaluates transfer learning and fine-tuning methodologies. Results demonstrate the superior performance of ConvNeXt Large, achieving 95.97% accuracy with transfer learning, underscoring the robustness of deeper ConvNeXt architectures. Insights demonstrate that transfer learning is useful in improving classification accuracy. Additionally, the study emphasizes the practical significance of these findings in improving diagnostic accuracy and streamlining pediatric pneumonia detection in clinical settings. The inclusion of a confusion matrix provides a comprehensive understanding of model performance. Overall, ConvNeXt models present promising prospects for pediatric pneumonia detection, heralding a significant advancement in medical imaging and disease diagnosis. © 2024 IEEE.

关键词： Pediatrics

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A Systematic Literature Review:Virtual Reality's in Decreasing Public Speaking Anxiety (PSA)

A Systematic Literature Review:Virtual Reality's in Decreasi...

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2023 International Conference on Information Technology and Computing, ICITCOM 2023

作者： Salsabilahadi, Sheila Atindriya Putri, Maisyita Sakinah Ismiarti, Fitria Santoso Gunawan, Alexander Agung Pramudya, Fabian Surya School of Computer Science Bina Nusantara University Mathematics Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350359633

Virtual reality (VR), which is becoming increasingly popular these days, is used to treat several anxiety conditions. In this study, we present a method that uses virtual reality to cure public speaking anxiety. Public speaking anxiety (PSA) is a term used to describe a person's fear of speaking in front of a large audience or a big group of people. Our research will investigate whether VR technology can be used to treat PSA. We found publications on Google Scholar that were relevant to PSA and virtual reality using a keyword-based search technique, and we then conducted a thorough evaluation of the literature ⌝⌝⌝on those works. We found that the most of articles use samples that include more than two participants and use various PSA scale levels in virtual reality to treatment. Participants' anxiety levels decreased after each exposure, indicating that VR-based exposure could be used as an alternative treatment for anxiety related to public speaking © 2023 IEEE.

关键词： Virtual reality

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Emotional Intelligence and Responsible Social Media Behavior 3

Emotional Intelligence and Responsible Social Media Behavior

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Gita Gayatri, Nyoman Ayu Anggraini Kusumajati, Dian Bina Nusantara University School of Computer Science Department of Computer Science Jakarta Indonesia Bina Nusantara University Character Building Development Center School of Information System Jakarta Indonesia

ISBN: (纸本)9798350367492

The rapid development of today's world has seen the pervasive use of social media platforms, which significantly affects human behavior. Emotional intelligence plays a crucial role in this context. It includes self-awareness, recognizing one's own emotions, managing emotions, recognizing the emotions of others, and interacting effectively with others, thereby influencing how individuals navigate the digital world. Social media serves as a digital territory where various emotional signals are expressed through text, emojis, and interactions. This study aims to determine the impact of emotional intelligence on the responsible use of social media among students at bina nusantara University and its implications for achieving the Sustainable Development Goals (SDGs), particularly the Quality Education indicator. The findings reveal a positive correlation between self- awareness and social media usage. This suggests that the students in the study can recognize their emotions and understand their moods when using social media, which affects their behavior. The study underscores the importance of incorporating emotional intelligence development into educational programs to enhance awareness and promote responsible behavior in the use of digital media in the digital age. © 2024 IEEE.

关键词： Ambient intelligence

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