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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...

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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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AI-Based Video Analysis for Driver Fatigue Detection: A Literature Review on Underlying Datasets, Labelling, and Alertness Level Classification 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Ariansyah, Dedy Rahutomo, Reza Elwirehardja, Gregorius Natanael Asadi, Faisal Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Reduced alertness because of fatigue or drowsiness accounts for a major cause of road accidents globally. To minimize the likelihood of alertness reduction-related crashes, a video-based detection emerges as a non-intrusive method and can offer high accuracy for the countermeasure development. Many Artificial Intelligence (AI) techniques have been developed to aid in video analysis for fatigue detection. However, the issues in AI-based video analysis for fatigue countermeasure development are rarely discussed. This paper reviews current video-based countermeasure development in the literature and highlights some reliability issues of AI-based fatigue countermeasure development in terms of underlying dataset, labelling method, and alertness level classification. The result serves as insights and consideration to academics and industrialists in developing a high reliable AI-based video analysis for driver fatigue countermeasure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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BERT and RoBERTa Models for Enhanced Detection of Depression in Social Media Text 9th

BERT and RoBERTa Models for Enhanced Detection of Depression...

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

作者： Kurniadi, Felix Indra Paramita, Ni Luh Putu Satyaning Pradnya Sihotang, Erna Fransisca Angela Anggreainy, Maria Susan Zhang, Rongfang Computer Science Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Statistic Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11530 Indonesia Department of Rehabilitation and Health Service University of North Texas United States

Depression is a disease that affects everyone, both young and old. This mental illness not only affects the surrounding environment but everyone. Depression is characterized by deep sadness, behavioral changes and many other actions that are risky for people. In this research we try to solve the problem of detecting depression using Natural Language Processing (NLP) approaches, these two methods are Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Approach (RoBERTa), where these two methods are used to detect posts made in reddit. The dataset is taken from Kaggle. The results obtained found that the average use of BERT and RoBERTa resulted in a high accuracy value of around 98% and with a well balanced precision, recall and F1-Score ratio. This research shows that there is a possibility of using BERT and RoBERTa in depression detection. © 2024 The Authors.

关键词： BERT depression natural language processing RoBERTa

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Question Classification of University Admission using Named-Entity Recognition (NER) 10

Question Classification of University Admission using Named-...

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10th International Conference on Information Technology, computer, and Electrical Engineering, ICITACEE 2023

作者： Yossy, Emny Harna Suhartono, Derwin Trisetyarso, Agung Budiharto, Widodo Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta11480 Indonesia Bina Nusantara University Computer Science Department School of Computer Science Jakarta11480 Indonesia Bina Nusantara University Mathematics Department School of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9798350322729

The university admission process can be overwhelming for applicants and admission officers, with many questions being asked and answered every year. The manual classification of these questions can be time-consuming and may lead to errors, making exploring automated solutions to this problem essential. In recent years, advances in natural language processing (NLP), including question classification. The research proposes an architecture for a question classification system using the Indonesian Language in closed-domain question answering. The purpose of this research is to find out the classification of questions. We created a system with the Named-Entity Recognition method to recognize the next word that will be used as a candidate type of question and candidate answer. The type of question used is a factoid question. Research makes patterns and rules for extracting important words as features to determine the taxonomy of question classification. Datasets are collected from one private university in Indonesia. Data sources from Binus Online Learning websites and data on questions frequently asked by users, especially prospective students. The results of the question classification are presented using descriptive statistics. The results show that the question classification with users' most frequent question type is 'What' by 63.3%, and the answer category is 'tuition fees' by 22.4%. It can be concluded that the most frequent question type asked by users is 'What', and the most frequent topic of interest is 'tuition fees'. Organizations can use this information to improve customer service by providing more relevant information and resources about tuition fees. © 2023 IEEE.

关键词： Natural language processing systems

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Decision theory and risk simulation analysis for optimizing profit in paylater services

Decision theory and risk simulation analysis for optimizing ...

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2022 International Conference on ENTERprise Information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2022

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

The projected increase in PayLater utilization reaches up to five million people by 2025. To optimize the yearly profit from their PayLater service, fintech companies must examine all possible risks before a unanimous decision is taken. Therefore, we proposed a unified decision framework derived from decision theory and the Monte Carlo simulation technique. Two schemes were coined: (1) a decision-making scheme, and (2) a risk simulation scheme. Throughout experiments, the framework was able to estimate several alternative decisions and their impacts, analyze the causes of failure and delays in the development of the PayLater service, and execute Monte Carlo simulations in up to 10,000 trials. Outputs of this study will benefit decision-makers in the fintech initiative before launching their PayLater products. © 2023 Elsevier B.V.. All rights reserved.

关键词： Fault tree analysis

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Data mining for the global multiplex weekly average income analysis

Data mining for the global multiplex weekly average income a...

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作者： Dominic, Nicholas Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

The box office (BO) income had significantly declined up to 80% in 2020, as the COVID-19 pandemic emerged. To minimize further financial risks, multiplex (multiple cinema complexes) owners need to analyze their potential income for each movie, each week. Therefore, we developed a proper data mining strategy that allows multiplex owners to analyze and discover insights on how successfully produced movies could be. The methodology comprises (1) data loading and exploration, (2) data cleaning, (3) data selection, integration, and transformation using Pentaho, (4) data mining in which the results were stored in the MySQL database, and (5) pattern evaluation and presentation using Qlik Sense as the Business Intelligence (BI) dashboard. Based on our data mining methodology, we revealed that drama, comedy, action, and thriller are favorite genres. We also found that DreamWorks Animation and Pixar Animation Studios are both the most popular production houses, even Apatow Productions and Escape Artists still have the biggest revenue on average. © 2022 The Authors. Published by Elsevier B.V.

关键词： Animation

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Performance Analysis of Distributed Database System in Cloud Computing Environment 9

Performance Analysis of Distributed Database System in Cloud...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Prasetio, Nicholas Ferdinand, Daniel Wijaya, Brandon Christopher Anggreainy, Maria Susan Kumiawan, Afdhal Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program School of Computer Science Jakarta11530 Indonesia

ISBN: (纸本)9798350370126

This research discusses the performance evaluation of distributed database systems in a cloud computing environment Cloud computing environments allow data and applications to be stored and deployed on infrastructure located in different parts of the world. However, the use of distributed database systems in cloud computing environments can cause performance issues, such as complex data access and factors such as network latency, security, and scalability that affect system performance. Therefore, performance evaluation of distributed database systems is necessary to ensure effective data management across the infrastructure. The purpose of this research is to measure and understand the performance of distributed database systems in a cloud computing environment. This is important because proper performance evaluation is needed to ensure distributed database systems can operate effectively and efficiently in such environments. This research will analyze the features of distributed database systems, factors that affect performance, how to measure and compare system performance, and how to improve system performance. Analyzing the performance of distributed database systems in a cloud computing environment can help users choose the most appropriate and efficient cloud computing platform for their business needs and improve operational efficiency. © 2023 IEEE.

关键词： Distributed database systems

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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Cardiovascular Disease Analysis Using Correlational Analysis and Association Rules Mining for In-depth Analysis to Identify Predominant Variables

Cardiovascular Disease Analysis Using Correlational Analysis...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Siswanto, Boby Soeparno, Haryono Sianipar, Nesti F. Budiharto, Widodo Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Engineering Faculty Food Technology Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short ( © 2023 IEEE.

关键词： Association rules

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