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International Conference on ICT For Smart Society (ICISS)

作者： Tanty Oktavia Kawang Utomo Ford Lumban Gaol Takaaki Hosoda Information Systems Management Department BINUS Graduate Program - Master of Information System Management Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Decision Science Department Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Since the emergence of Pokémon Go in 2016, the world has been introduced to Augmented Reality (AR) games. Many IT companies have begun developing augmented reality (AR) games due to the great commercial potential in this industry. Certain products may have managed to remain viable in the market, while others were forced to close due to one or two factors. Since the emergence of Pokémon Go, no other game has managed to surpass its unprecedented success. The company responsible for developing Pokémon Go has decided to shut down one of their games due to a lack of consumer interest. The technology of augmented reality (AR) is consistently associated with the concept of immersion. Immersion is a key aspect of the game that allows users to deeply engage and feel fully involved in the experience. This study will employ a quantitative methodology, utilising a questionnaire that will be distributed to and completed by those who have engaged in augmented reality (AR) games. The data will be analysed using Smart PLS to examine the impact of user experience on immersion, which in turn influences intention. After gathering over 200 participants by spreading google form questionnaires and doing data analysis, the findings indicate that only a few aspects of user experience, namely Brand Experience and User Need Experience, have an impact on reasons. Niantic, the developer of Pokémon Go, was unable to replicate their success with a similar augmented reality game called Harry Potter: Wizards Unite. The initial release occurred on June 21, 2019, and the shutdown took place on December 21, 2021. In the beginning, they shown a profound enthusiasm for the game but were unable to sustain it, much as Pokémon Go. According to Niantic CEO John Hanke, the primary reason for the game's shutdown was its immersion. Immersion has a notable impact on User Confirmation, which in turn has a notable impact on Satisfaction, and Satisfaction has a notable impact on Intention. Moreover, there have be

关键词： Surveys Industries Data analysis Games Companies User experience Software Hardware Internet Augmented reality

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Design of an Automatic Price Tag System for Web-Based Retail Business

Design of an Automatic Price Tag System for Web-Based Retail...

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International Conference on Information Management and Technology (ICIMTech)

作者： Suryadiputra Liawatimena Devina Gunawan Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Deparment BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

Modern retail businesses face a significant challenge with the inefficiency of manually changing price labels on shelves. This manual process not only consumes valuable time and resources but also increases the likelihood of errors, leading to potential inaccuracies in pricing and a less streamlined shopping experience for customers. The proposed solution is the implementation of an Electronic Shelf Label (ESL) system that automatically displays the prices of goods on retail business shelves. This system connects a website (front end and back end) to e-paper via a Wi-Fi network and microcontroller, allowing retail business owners to update prices more easily. Additionally, buyers can search for desired items through the website. The results are the time to send data from the website to the e-paper, namely, to know the performance of the e-paper used. The average time required from 10 attempts to send data from the website to the e-paper is 20.935 seconds. Since the data is connected to the server and will be updated automatically, using this system will be more efficient than manually changing the price label, although it takes time to transfer data from the website to the e-paper.

关键词： Electric potential Microcontrollers Pricing Manuals Information management Servers Wireless fidelity Faces Business

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Classification Prediction of Heart Disease Using Machine Learning Techniques

Classification Prediction of Heart Disease Using Machine Lea...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Kevis Setiawan Jonathan Putu Satria Beratha Maria Susan Anggereainy Afdhal Kurniawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 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

Heart disease is also called a common one of global health concerns. A lot of research has been done before to predict someone whether has a heart disease or not by machine learning. In this study, we use five machine learning techniques as comparison which machine learning technique has a most accuracy to recognize heart disease in someone's condition. In this case, we are using UCI Cleveland Dataset as a sample and the result shows that the Support Vector Machine and K-Nearest Neighbor gives the most accuracy which is 85% along with many aspects respectively.

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Deep Learning Based Facial Emotion Recognition using Multiple Layers Model

Deep Learning Based Facial Emotion Recognition using Multipl...

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2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021

作者： Sandra, Lidia Heryadi, Yaya Lukas Suparta, Wayan Wibowo, Antoni Krida Wacana Christian University Psychology Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665499699

A Facial Emotion Recognition (FER) system is an important tool to be implemented in any psychology academic field and beyond. This paper aims to show a system of Facial Emotion Recognition that can be done using the multiple layers model of ResN et50 which is an ANN. The dataset is an array that consists of 7 different emotions represented by the numbers 0-6. The emotion included in the dataset is 'anger', 'disgust', 'fear', 'happiness', 'sadness', 'shock', and 'neutrality'. This research found that the highest accuracy in the application of the model was recorded at 65% and the test was 60% to recognize facial emotion from a graphical input. © 2021 IEEE.

关键词： Emotion Recognition

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Hybrid approach sentiment analysis using Transformer-LSTM in the Indonesian language

Hybrid approach sentiment analysis using Transformer-LSTM in...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Angel Karentia Ferdinand Winaya Derwin Suhartono Computer Science Department BINUS Graduate Program – Master of Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Sentiment analysis is widely used as a tool to find valuable insight from texts without explicitly expressed. Lots of techniques have already been used to get it but there still have shortcomings in data source or the model strategy itself. Indonesian language approximately has 199 million speakers across the world where 44 million speakers natively. Even with that great number, the resources for the Indonesian language's natural language processing are still limited, and hard to find the perfect way to define sentiment analysis in the Indonesian language. The state-of-the-art sentiment analysis method uses LSTM on a small corpus while the best in town is Transformer where it's easy to transfer learning from the Transformer pre-trained model into specific tasks. From this potential, combining the Transformer pre-trained model with LSTM can be an innovative strategy. This research compared the hybrid model of Transformer-LSTM build using three Indonesian languages’ Transformer-based pre-trained model on sentiment analysis task which can surpass the Transformer model with the highest increased 2.40% accuracy and 3.02% on F1 score.

关键词： Seminars Sentiment analysis Analytical models Accuracy Law Soft sensors Transfer learning Medical services Transformers Long short term memory

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E-Govchurch as a Digital Transformation Initiative for Nation-wide Christianity Organization 1

E-Govchurch as a Digital Transformation Initiative for Natio...

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1st International Conference on Technology Innovation and Its Applications, ICTIIA 2022

作者： Paramita, Adi Suryaputra Prabowo, Harjanto Ramadhan, Arief Sensuse, Dana Indra Graduate Program Universitas Ciputra Surabaya Bina Nusantara University Surabaya Indonesia/Jakarta Information Systems Department/Doctor Of Computer Science Indonesia Doctor Of Computer Science Graduate Program Bina Nusantara University Jakarta Indonesia Universitas Indonesia Faculty Of Computer Science Depok Indonesia

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

The process of digital transformation is part of the 4.0 industrial revolution. Through digital transformation, it is hoped that all processes inside an organization will function more efficiently. Internet technology is the major driver of an organization's digital transformation effort. Information Systems for the statewide scope of the two main religion-based organizations in Indonesia are required to satisfy the Indonesian government's National Medium-Term Development Plan for the years 2020-2024 in accordance with Indonesian President Decree No. 18 of 2020. The regulation mandates that the ministry of religious affairs provide information and data services. E-Govchurch, an abbreviation for Electronic-Governmental Church, is the suggested framework. E-Govchurch is a digital program for Information Systems that incorporates a framework for data governance. E-Govchurch will develop utilizing SSM, RBV, the Zachman Framework, and the DAMA framework. The purpose framework in research will lead the next phase of framework development process. © 2022 IEEE.

关键词： Information systems

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LDA Topic Modeling for Bioinformatics Terms in arXiv Documents

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Procedia computer science 2024年 245卷 229-238页

作者： Andrea Stevens Karnyoto Matthew Martianus Henry Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

A wide variety of disciplines contribute to bioinformatics research, including computer science, biology, chemistry, mathematics, and physics. This study determines the number of research articles published on arXiv classified as bioinformatics topics and the most frequently used bioinformatics terms using topic modeling, Latent Dirichlet Allocation (LDA). An algorithm based on LDA is used to discover topics hidden within large collections of documents through the use of statistical analysis. Our research examined 226453 articles on arXiv between January 2023 and January 2024. As a result, there are more than 10521 articles categorized into bioinformatics topics. Most commonly, 6352 documents are in the "Mathematical Physics" category. The second most popular category is "computer science," with 2950 documents. Accordingly, the terms 'RNA,' 'sequence,' 'tree,' and 'homology' are the three most commonly used terms in bioinformatics. The study of RNA plays a vital role in molecular biology; thus, the study of RNA is prevalent in bioinformatics. Sequential data refer to the order in which nucleotides or amino acids can be found in a DNA molecule or a protein.

关键词： arXiv bioinformatics Latent Dirichlet Allocation

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Semi-automated meningioma segmentation with bounding boxes

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Procedia computer science 2024年 245卷 583-590页

作者： Nur Adhianti Heryanto Mahmud Isnan Matthew Martianus Henry Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Segmentation is manually performed by physicians, which takes considerable time and may be subject to observers. Automating this task can increase efficiency and consistency. Existing studies on meningioma segmentation used data from limited study centers, indicating the need for research on multi-center data to assess generalizability. In this work, two semi-automated methods with bounding box priors, LiteMedSAM and BBU-Net, are evaluated on the brain tumor segmentation (BraTS) 2023 meningioma dataset collected from five study-centers. Preprocessing included exclusion of small tumors, z-score normalization, and extraction of slices that contain tumors, generating 25,602 2D axial magnetic resonance imaging (MRI) scans. A fine-tuning strategy is adopted for LiteMedSAM while BBU-Net is trained from scratch. The models are evaluated using a five-fold cross-validation, with data split at the case level. Results show that while U-Net models can achieve performance close to LiteMedSAM, the foundation model has overall better performance, with more than 90% in all evaluation scores.

关键词： Bounding boxes Meningioma Semi-automated segmentation

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Influence of Feature Extraction Methods using Neighborhood Component Analysis (NCA) to Improve Classification Performance for Hepatitis C Disease

Influence of Feature Extraction Methods using Neighborhood C...

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International Conference on Informatics and Computing (ICIC)

作者： Christiella Abinosy Setiawan Maria Susan Anggreainy Nora Fitriawati Raymasterio Vera Lucky Renatha Dewi Sabrina Steven Matthew Vierdaria Wijayanti Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Research in the development of Hepatitis C disease prediction is increasingly developing, especially using machine learning models which is able to make predictions quickly and accurately. In this study, a comparison of several classification methods was carried out by also applying feature reduction, NCA. In this study, a comparison of performance was carried out if the data was entered into the NCA feature extraction method with KNearest Neighborhood (KNN) and Support Vector Machine (SVM) and a comparison of performance if the data did not use the NCA feature extraction method. The performance comparison metrics used in the study were accuracy, sensitivity, specification, Matthews Correlation Coefficient (MCC), and Kappa value. The highest accuracy (99.36%), sensitivity (91.94%), specification (99.67%), MCC $(0.895)$ and the best Kappa value $(0.889)$ were obtained in the KNN-NCA prediction method.

关键词： Support vector machines Accuracy Sensitivity Liver diseases Computational modeling Nearest neighbor methods Predictive models Feature extraction Diseases Testing

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Exploring EfficientNet Variants for Image Encoding in Auxiliary Signal Guided Knowledge Encoder-Decoder Framework

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Procedia computer science 2024年 245卷 391-398页

作者： Matthew Martianus Henry Nur Adhianti Heryanto Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The growing number of medical images has led to radiologist burnout, which seriously impacts the radiologist's performance. To address the previously mentioned issue, an Auxiliary Signal Guided Knowledge (ASGK) multimodal encoder-decoder framework was designed to automatically generate the medical report based on the proposed medical graph and natural language decoder. It utilizes DenseNet-121 as the image encoder. With DenseNet-121 lack of computational and memory efficiency, this study aims to explore the potential of EfficientNetB0 to EfficientNetB4 as an ASGK image encoder substitute. The framework is trained with IU X-Ray dataset for 30 epochs, with Adam optimizer, a learning rate of 0.01 with 0.8 decay rate, binary cross entropy loss for the medical tags, and cross-entropy loss for the generated medical captions. During the framework training process with each image encoder, the parameter that achieves the highest CIDEr score on the validation set is considered the best image encoder parameter and will be used on the test set. On the test set, EfficientNetB3 as an ASGK image encoder has been shown to increase the CIDEr score to 0.35, a significant increase from the 0.28 CIDEr score obtained by the ASGK using DenseNet-121. This score is only a 1% decrease from the best validation score. It suggests that not only EfficientNetB3 increases the framework's performance, it is also less prone to overfitting. This study has demonstrated that EfficientNetB3 is a potential image encoder substitute for DenseNet-121 in the ASGK framework.

关键词： Automated medical report generation DenseNet-121 EfficientNet Encoder-decoder framework

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