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3rd IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Lin, Ming-Yi Hsieh, Sheng-Hsien Chen, Ching-Han Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Bachelor's Program in Artificial Intelligence and Mechatronics National Pingtung University of Science and Technology Pingtung Taiwan

ISBN: (纸本)9798350333862

Researchers emphasize the importance of hardware accelerators for mathematical morphology. If there are any issues, the hardware architecture may need to be redesigned. Thus, we propose a novel, reconfigurable hardware architecture with pipeline techniques for acceleration. With the order control register, there is no need to modify the architecture even if different results are expected. Furthermore, the hardware architecture caters to specific purposes instead of requiring a purpose-built hardware design. © 2023 IEEE.

关键词： Reconfigurable hardware

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Machine Learning Approaches in Detecting Autism Spectrum Disorder

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Procedia computer science 2023年 227卷 1070-1076页

作者： Daniel Nicholas Dominic Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classifi-cation 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.

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

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Implementation of Dynamic Image for Facial Expression Recognition on Indonesian Facial Expression Dataset

Implementation of Dynamic Image for Facial Expression Recogn...

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IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

作者： Irene Anindaputri Iswanto Andry Chowanda Haryono Soeparno Widodo Budiharto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

In recent times, there has been considerable attention directed towards Facial Expression Recognition (FER) due to its extensive utility across diverse domains. However, the universality of facial expressions has been challenged by studies suggesting that cultural backgrounds significantly influence the perception and recognition of emotions. This paper addresses the need for culturally specific datasets in FER tasks, particularly in underrepresented regions like Indonesia. The study introduces dynamic images as an alternative input representation for facial expression recognition tasks, aiming to assess their efficacy using the Indonesian Mixed Emotions Dataset (IMED). Through experimentation using EfficientNet model, the performance of dynamic images is compared with static image and video inputs. Results indicate that dynamic images exhibit promising performance, with an accuracy of 94.28%. These results outperform static image datasets and nearly match the performance of video-based models, which achieved an accuracy of 97.93 %, despite using fewer data. Nonetheless, challenges such as data imbalance and the quality of generated dynamic images persist, suggesting avenues for further research and model refinement. This study provides valuable insights into methodological advancements in FER, particularly in limited dataset conditions, laying the groundwork for future developments in dynamic image-based facial expression recognition algorithms.

关键词： Emotion recognition Image recognition Accuracy Face recognition Heuristic algorithms Data models Cultural differences Artificial intelligence

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Individual Performance of Students in Online Learning Environments: Study of User Satisfaction 10

Individual Performance of Students in Online Learning Enviro...

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

作者： Alamsyah, Doni Purnama Indriana Utomo, Satrio Matin Siswanto, Boby Susanti, Leni Morika, Doni Bina Nusantara University BINUS Business School Entrepreneurship Department Jakarta11480 Indonesia Bina Nusantara University Management Program BINUS Business School Jakarta11480 Indonesia Bina Nusantara University Computer Science Study Program School of Computer Science Jakarta11480 Indonesia Bina Nusantara University Interior Design Study Program School of Design Jakarta11480 Indonesia

ISBN: (纸本)9798350322729

Investigation on e-learning is important, especially user satisfaction in support of student academic performance. Evaluation of user satisfaction can be measured by evaluating perceived quality on the user's side, so this research aims to investigate the role of perceived quality over online learning on User satisfaction. Users of online learning targeted are students because students have experience and knowledge of the implementation of online learning at the university. Collected data and information from students related to satisfaction with online learning through online questionnaires. 615 students filled out questionnaires, then questionnaire data was processed quantitatively through SPSS with statistical testing based on research hypotheses. Three factors essentially increase user satisfaction in online learning, regarding the system, service, and information. Student satisfaction can be improved when supported by information on the platform. The role of a platform regarding information in the online learning context is important, a measure that explains the quality of information among other things useful, understandable, interesting, and reliable. Research findings provide important information for universities as organizers of education, aimed at attaining information quality related to online learning. © 2023 IEEE.

关键词： Students

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Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model

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Procedia computer science 2023年 227卷 168-175页

作者： Mahmud Isnan Gregorius Natanael Elwirehardja Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 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.

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

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Construction of a Self-Balancing Cube With Embedded Reaction Wheel Drive and Preliminary Investigation of Simple Harmonic Motion Patterns

Construction of a Self-Balancing Cube With Embedded Reaction...

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science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Baihaqi Siregar Opim Salim Sitompul Mohammad Andri Budiman Fahmi Computer Science Doctoral Program Universitas Sumatera Utara Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

The use of reaction wheels to help maintain the position of objects in balance remains an interesting topic to discuss in research. The harmonic motion resulting from the continuous rotation of the reaction wheel is read by the installed sensor, so that acceleration and angular velocity values are obtained on the X -axis, Y -axis and Z -axis. The self-balancing cube was built to be used as a vehicle for the reaction wheel to be embedded. The phenomenon that occurred during the experiment showed that there was a certain pattern formed as a result of the self-balancing cube swinging to maintain its balance.

关键词： System testing Wheels Life estimation Position measurement Harmonic analysis Angular velocity Information technology Standards

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Video Quality Indicators for Video-Based Learning System in Higher Education 23

Video Quality Indicators for Video-Based Learning System in ...

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9th International Conference on Frontiers of Educational Technologies, ICFET 2023

作者： Zulkarnain, Novan Prabowo, Harjanto Gaol, Ford Lumban Isa, Sani Muhamad Computer Science Department BINUS Graduate Program Computer Science Information System Bina Nusantara University Indonesia Department School of Information System Bina Nusantara University Indonesia Management Department BINUS Business School Undergraduate Program Information System Department School of Information System Bina Nusantara University Indonesia

ISBN: (纸本)9798400707353

Higher education worldwide has adopted Video-Based Learning (VBL) over the past decade. They have tried to build a VBL system to improve services to students. However, the researcher's topic was not fully explored, especially in making the video itself. What features are mandatory and must be considered in making a video lecture to increase student engagement. A systematic review of the literature (SLR) is a way to identify these features. This study provides a critical analysis of the current research in VBL over the last five years (2017 - 2021). It presents a general trend examining 1336 journals and many literary works related to VBL design in Higher Education, selected through a stepwise process based on PRISMA Framework guidelines, using pre-defined selection criteria. We carefully selected 32 journals and found 22 features referring to the Lecture Video Design succession. © 2023 ACM.

关键词： Learning systems

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Artificial Intelligence-Powered Intent Classification for Indonesian E-Wallet Customer Service Chatbots

Artificial Intelligence-Powered Intent Classification for In...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Christopher Owen Derwin Suhartono Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

The rapid expansion of e-wallet services in Indonesia has significantly heightened the need for efficient customer service solutions, making chatbots an essential tool for user support. However, many providers continue to rely on rule-based chatbots, resulting in rigid interactions that struggle to meet diverse user needs or handle the linguistic complexities of the Indonesian language. This study addresses these limitations by developing an AI-powered intent classification model specifically tailored for Indonesian e-wallet customer service, aimed at delivering a more accurate, adaptive, and user-centered experience. A custom dataset was created from user comments on social media associated with Indonesia's top e-wallet providers, followed by data pre-processing and clustering using the BERTopic model. To improve interpretability, OpenAI's GPT-4 was employed for label refinement, resulting in enhanced clarity. Various models were tested, including IndoBERT, RoBERTa, and Convolutional Neural Network (CNN) architectures in both 2D and 3D configurations. The highest-performing model combined IndoBERT embeddings with a 3D CNN classifier, achieving an accuracy of 84.30%, precision of 84.33%, recall of 84.30%, and an F1-score of 84.24%. This study contributes a unique Indonesian-specific dataset and demonstrates the potential of AI to transform customer service interactions in Indonesia's e-wallet sector, offering a clear advancement over traditional rule-based approaches.

关键词： Adaptation models Solid modeling Three-dimensional displays Accuracy Online banking Intent recognition Customer services Transforms Chatbots Convolutional neural networks

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The Utilization of DistilBERT Features Space for Topics Suggestion in the Context of Learning Content

The Utilization of DistilBERT Features Space for Topics Sugg...

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Artificial Intelligence and Image Processing (IWAIIP), International Workshop on

作者： Kenny Jingga Hidayaturrahman Hidayaturrahman Computer Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Recommender system is one of the popular topics in artificial intelligence fields as it can widely be used in the *** service provider, e-commerce, e-learning, and many other fields can utilize recommender system to give the personalization for the users. This research will try to use recommender system to provide the recommended topics that are suitable to each learning content. As part of developing the most suitable recommender system, this research will also focus on the data pre-processing,as the data is still raw and contains too much unused information yet. Text vectorization or the embedding process was conducted to the dataset using DistilBERT, pre-trained BERT model. After the vectorization, the recommender system used cosine similarity from the result to discover the largest cosine similarity, which was used to determine the recommendation. Based on the experiment, using cosine similarity could do the recommendation well enough by giving the appropriate topics recommendation based on the content. For example, given the content: “Chapter 2: Comments Chapter 2 of the book on C programming”, the top 5 recommended topics were: “Objective-C”, “C++”, “MATLAB”,”VBA”, and “Perl”. Based on the results, it can be considered that the recommender system had performed as expected. However,it still had a lot of areas that could be improved for the future research, especially in the data preprocessing. Other text vectorization models can be considered to be used, such as: BERT Multilingual, RoBERTa, and SpanBERT. Other consideration is the content preparation that will be used as the input for the system. Combination of DistilBERT and cosine similarity as the recommender system can be considered to be implemented for other areas.

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Implementation of Random Forest Model with Hyperparameter Tuning for Diabetes Mellitus Classification

Implementation of Random Forest Model with Hyperparameter Tu...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Ajeng Wulandari Maria Susan Anggreainy Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduiate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

This research aims to classify Diabetes Mellitus (DM) using the Random Forest (RF) model by exploring feature selection techniques and hyperparameter tuning. DM is a metabolic disorder in the body due to bodily incompetence produces or delivers the finished hormone insulin causes blood sugar levels to rise above normal. High sugar levels in the bloodstream can cause heart disease, stroke, kidney disease, blindness, nerve damage in the feet, etc. So, diabetes must be detected as early as possible so that sufferers can receive early treatment. One of the machine learning models that can be used to create a diabetes detection model is the random forest (RF) model. The results from RF will be compared with other machine learning models such as K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost). The comparison results show that RF model is better and achieves the highest accuracy with an accuracy value of 97%.

关键词： Radio frequency Accuracy Forestry Nearest neighbor methods Diabetes Random forests Tuning Sugar Diseases Overfitting

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