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Technology Acceptance Readiness Analysis in the Context of Digital Tourism Village using Machine Learning Approach 9

Technology Acceptance Readiness Analysis in the Context of D...

引用

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

作者： Prawira, Indra Andryani, Nur Afny Catur Yufriadi, Ebnu Bina Nusantara University Faculty of Digital Communication and Hotel & Tourism Mass Communication Program Communication Department Jakarta11480 Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Digital tourism village potentially accelerates rural development hence improving a country's economy. The Indonesian government has developed hundreds of tourism villages in recent years but has yet to popularize the development of digital villages. Technology design and development are pivotal factors in tourism villages' digitalization. Therefore, the development digital tourism village must consider village readiness by measuring technology adoption readiness or technology acceptance of the corresponding stakeholders. This paper presents the technology acceptance analysis in the context of a digital tourism village. We use a quantitative analysis approach using the TAM-UTAUT model with data collected from a questionnaire of 101 villagers (n=101) in Pasanggrahan Village, West Java, Indonesia. This research uses machine learning with clustering procedures to analyze the level of technology adoption readiness by implementing K-means Clustering algorithm. In addition, to observe the significance of each influencing factor to the technology adoption readiness level, feature importance analysis approach using Extra tree classifier algorithm is employed. The research finds that attitude toward use, perceived ease of use, and facilitating condition-have a considerable impact on the degree of readiness for technology adoption. On the other hand, the perceived utility and social impact are found to have minimal bearing on the preparedness level for technology adoption. Perceived ease of use and facilitating conditions are the most important factors to afford the acceleration of technology adoption readiness. The research contributes to the development of a digital tourism village and the analysis of technology adoption readiness using machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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ConvNeXt Model for Breast Cancer Image Classification 6

ConvNeXt Model for Breast Cancer Image Classification

引用

6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Setiawan, Devin Karnyoto, Andrea Stevens Intan, Indo Pardamean, Bens Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83. © 2024 IEEE.

关键词： Lung cancer

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Server Cloud Based Security Surveillance Systems: a Design science Research 11

Server Cloud Based Security Surveillance Systems: a Design S...

引用

11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Nurenie, Alexander Ramadhan, Arief Abdurachman, Edi Trisetyarso, Agung Zarlis, Muhammad Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350305968

In this research, the author addresses the prevalent issues faced by users of cloud services, especially those using Peer-to-Peer (P2P) technology, such as connection losses, security concerns, and poor video quality. The study does not delve into the complex problems at the data center level but instead proposes a novel Design science Research approach. This new method, tested with five clients in varied locations, utilizes port forwarding to establish a direct server-client connection, bypassing traditional P2P frameworks. The results show that this approach significantly improves quality over the P2P system. Feedback from clients before and after the experiment indicates a positive response to this new method. This research offers insights into overcoming P2P-related issues in cloud services and contributes to the development of cloud-based server science, presenting a cost-effective, independent alternative to data center-dependent P2P services, with plans for ongoing research in this area. © 2023 IEEE.

关键词： Cost effectiveness

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Intelligent Decision Support System for Higher Education Institutions 9

Intelligent Decision Support System for Higher Education Ins...

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9th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2023

作者： Afriliana, Nunik Meyliana Gaol, Ford Lumban Soeparno, Haryono Bina Nusantara University Binus Graduate Program Computer Science Program Computer Science Department Jakarta Indonesia Bina Nusantara University School of Information System Information System Department Jakarta Indonesia

ISBN: (纸本)9798350369922

To ensure the sustainability of their operations, higher education institutions have to establish a robust strategic plan. Higher education institutions have an obligation to continuously assess the quality of their educational offerings in every program to comply with the standards established by the government and other educational entities. The implementation of intelligent decision support systems is a feasible approach among other techniques for addressing the aforementioned challenges. An intelligent decision support system has the potential to enable the business to uphold its adherence to regulations, enhance operational effectiveness, and optimize overall performance. This study aimed to determine the current state of intelligent decision support systems through the implementation of a comprehensive literature review. The second goal is to evaluate the best practices, methods, and implementation of intelligent decision support systems within higher education institutions. The findings indicate that intelligent decision support systems have the capacity to serve as a viable option for higher education, particularly in terms of enhancing the accuracy and efficiency of decision-making processes. Intelligent decision support systems exhibit a strong correlation with the fields of data mining and computational intelligence techniques. Nevertheless, the amount of research conducted on this subject is limited. © 2023 IEEE.

关键词： Data mining

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Survey of Online Exam Proctoring Model to Detect Cheating Behavior based on Face Recognition 8

Survey of Online Exam Proctoring Model to Detect Cheating Be...

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8th International Conference on Systems and Informatics, ICSAI 2022

作者： Rabiha, Sucianna Ghadati Kartowisastro, Iman Herwidiana Setiawan, Reina Budiharto, Widodo Bina Nusantara University Binus Graduate Program - Computer Science. Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Computer Science Department Binus Graduate Program - Computer Science Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665474481

Exams are an important component of any educational program, including online education. In any test, there is a possibility of cheating, so its detection and prevention is important. This study aims to conduct an in-depth study of the online exam monitoring model approach based on facial recognition used to detect cheating. Based on the inclusion and exclusion criteria designed, 13 selected studies were obtained. From these studies, we conducted further analysis regarding the Face Detection Method, Face Recognition Method, Initial Feature, Behavior Analysis and Evaluation Metrics used in each study so as to provide answers to research questions. the most frequently used Face detection method was Viola-Jones with a presentation of 20%, then CNN and MTCNN with a total presentation of 21%. The most widely used face recognition method in selected studies is CNN and metrics Accuracy is one of the most frequently used evaluations with a percentage of 33%. While the features that are usually used to detect cheating during online exams include facial motion and head pose which occupies the first position. The second is eye movement, then multiple faces gaze estimation and facial expression is in third place. Other features that also play a role in analyzing cheating behavior are mouth detection, facial vector, landmark location, gesture and posture. © 2022 IEEE.

关键词： Face recognition

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Comparative Analysis of CNN Models for Multiclass Detection on Colon Disease 9

Comparative Analysis of CNN Models for Multiclass Detection ...

引用

9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Sulistyawan, Jason Sebastian Nugroho, Kuncahyo Setyo Isnan, Mahmud Pardamean, Bens Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Gastrointestinal diseases are significant health concerns that mostly affect the digestive and biliary tracts. These can only be observed internally through endoscopy, or a modern approach named Wireless Capsule Endoscopy. Though the approach has proven to work, the process relies heavily on medical experts, which is prone to error when given a considerably large load. The use of deep learning in detecting diseases with images has been proven to work in various cases and can be implemented for gastrointestinal diseases. This study compared four deep learning models based on Convolutional Neural Network architectures consisting of the VGG16, ResNet152V2, InceptionV3, and Xception for classifying colon disease images. The experiment showed that the ResNet152V2 performed the best, compared to the other three with a testing accuracy score of 0.9837. On the other hand, the VGG16 had the lowest performance with around accuracy score of 0.89 while the Xception and InceptionV3models yielded similar accuracy scores of 0.9587 and 0.9549, respectively. The results highlight the effectiveness of ResNet152V2 in handling the complexity of colon disease detection © 2024 IEEE.

关键词： Convolutional neural networks

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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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Machine Learning Approaches in Detecting Autism Spectrum Disorder 8

Machine Learning Approaches in Detecting Autism Spectrum Dis...

引用

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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Random Forest Based QSAR Model Analysis for Predicting Drug Effectiveness Against Mycobacterium Tuberculosis Bacteria 11

Random Forest Based QSAR Model Analysis for Predicting Drug ...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Ekosputri, Alicia Josephine Ardianti, Amanda Anggreainy, Maria Susan Artanto, Brychan Fitriawati, Nora Valentina, Fanny Angelia Santoso, Vincent Kartamulya School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11530 Indonesia Bina Nusantara University Binus Online Learning Information Systems Department Jakarta11530 Indonesia

ISBN: (纸本)9798350389654

This research focuses on the use of machine learning methods, especially Random Forest, in designing tuberculosis drugs more effectively and efficiently. The research process begins with the creation of a QSAR (Quantitative Structure-Activity Relationship) model using a dataset from ChEMBL that includes compounds with activity against Mycobacterium tuberculosis. This model aims to predict the pIC50 value, which is an indicator of a compound's biological activity as a tuberculosis drug. The results of the Random Forest model showed high accuracy, which is 92%. This accuracy indicates that the model has a good ability in predicting the effectiveness of compounds against Mycobacterium tuberculosis. With this result, the developed model can be used as an auxiliary tool in the discovery of tuberculosis drugs and can be further developed to increase the effectiveness in drug research. © 2024 IEEE.

关键词： Prediction models

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Classification Prediction of Heart Disease Using Machine Learning Techniques 4

Classification Prediction of Heart Disease Using Machine Lea...

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Setiawan, Kevis Jonathan Beratha, Putu Satria Anggereainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350318432

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. © 2023 IEEE.

关键词： Support vector machines

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