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The Development of an Artificial Intelligence-Powered Visualization Tool for the Presentation of a Tea Cafe Startup 11

The Development of an Artificial Intelligence-Powered Visual...

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

作者： Hariyoko, Chelytha Pramono, Andi Daniswara, Desy Silvia Richard Ho, Alken Kurniawan, Bambang Kartono Nabillah, Salsa Binus Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350389654

Numerous beverages available in Indonesia belong to the category of harmful drinks, which contributes to the country's high diabetes rate. As a result of the rising prevalence of diabetes among the population in Indonesia in recent years, primarily attributed to the growing use of harmful beverages, ***. Tea assistance with the rise of the tea startup. The primary objective of this startup is to establish a tea shop within the mall. In order to plan the interior design, the author requires a prototype in the form of visual support. This research aims to develop a design for a tea cafe startup employing advanced methodologies that utilize Artificial Intelligence (AI) software. The employed approach entails a solitary case study employing software for generating three-dimensional (3D) objects and artificial intelligence (AI)-driven software for rendering. The first phase of the planning process involves creating a mood board and designing a three-dimensional tea cafe layout. The final result is a 3D image that is devoid of colour, as well as one that has been filled with colour. Software powered by artificial intelligence generates this image, responding to different triggers. Images that adhere to interior design principles when selecting furniture and supporting elements will yield superior rendering outcomes and align more closely with the desired results of the company owner. In this study, the author also incorporates Sustainable Development Goals (SDGs) number 2 and 3 when examining the role of this startup. © 2024 IEEE.

关键词： Architectural design

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Rust's Memory Safety Model: An Evaluation of Its Effectiveness in Preventing Common Vulnerabilities 8

Rust's Memory Safety Model: An Evaluation of Its Effectivene...

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

作者： Oey, Kevin Andrian Santoso Kwee, Catherine Chua, William Nabiilah, Ghinaa Zain Rojali Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Ensuring safe program operation is crucial for systems programming, and memory safety is a significant aspect of this. This paper assesses the memory safety strategies used in Rust and C++ programming languages. Rust is a modern language that has become popular due to its focus on safety, performance, and concurrency, whereas C++ is known for its efficiency and low-level programming capabilities. The analysis compares and contrasts the memory safety features of both languages, including Rust's ownership and borrowing model and C++'s manual memory management techniques. Additionally, the study examines the development tools available for both languages and compares ease of use and productivity. The analysis shows that Rust has several memory safety advantages over C++. Rust's ownership and borrowing model enables safe memory management without requiring manual intervention. By enforcing strict rules for memory usage, the compiler in Rust prevents typical memory-related errors like null and dangling pointers. However, in contrast to Rust, C++ provides more low-level hardware control but needs manual memory management, exposing it to memory errors such as buffer overflows and memory leaks. Moreover, Rust's memory safety features simplify the development of secure and dependable code, especially for applications with crucial security requirements. In contrast, C++ demands significant memory management expertise to avoid memory-related errors. In conclusion, Rust offers several memory safety benefits over C++, making it a superior choice for applications with critical safety needs. However, the selection of language ultimately depends on the specific project requirements and the development team's experience and preferences. © 2023 The Authors. Published by Elsevier B.V.

关键词： C++ programming Language Memory Safety Rust programming Language Security Software Systems programming

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Efficacy Comparison of Augmented Reality and Virtual Reality-based Games for Post-Stroke Rehabilitation: A Systematic Literature Review 9

Efficacy Comparison of Augmented Reality and Virtual Reality...

引用

9th International HCI and UX Conference in Indonesia, CHIuXiD 2023

作者： Taufiq, Milkhatussyafa'ah Wicaksono, Danang Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350304084

The usage of Augmented Reality (AR) and Virtual Reality (VR) games is seen to have the potential to deliver considerable advantages for post-stroke rehabilitation since it can increase healing progress while saving time and money. This study compares the use of AR and VR in games for post-stroke rehabilitation by assessing the game design, measurement technique, and challenges that may be improved for future studies. Using the PRISMA concept to conduct a Systematic Literature Review (SLR) of related research by identifying relevant data sources, evaluating abstracts, determining eligible publications, and analyzing data using the meta-analysis approach. We analyzed 198 publications from multiple database sources during this procedure and identified 24 papers for additional investigation. The study's implications show that the game design and assessment technique that best meets post-stroke rehabilitation assignments is the most important aspect in assessing the capabilities of AR and VR games for post-stroke rehabilitation, which influences future research on AR and VR games. It was also discovered that numerous obstacles to the usage of AR and VR games must be solved in order to maximize the post-stroke recovery process. However, the data sources employed in the study have limitations. To optimize the utilization of AR and VR games in post-stroke rehabilitation, it is required to examine rules and infrastructure, as well as to employ more diverse data sources. © 2023 IEEE.

关键词： Augmented reality

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Towards Improved Age Verification System for Security in Social Media using Convolutional Neural Network (CNN) with ResNet-50 6

Towards Improved Age Verification System for Security in Soc...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Tananjaya, Vincen Elfan Lim, Rayson Calvianto Edelyne, Felicia Pratama, Galih Dea Hasana, Silviya Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315639

Social media is an excellent platform to communicate with loved ones, friends, discover new information, and more. Social media is used by many different kinds of people which makes many security concerns that threaten the safety of kids and teens. Social media have many threats such as cyberbullying, identity theft, and strangers who may groom the child. Furthermore, social media is used to spread adult videos or pictures that harm underage children. Therefore, it is necessary to address social media security concerns. We addressed this difficult problem by researching age recognition to determine user age to prevent children from accessing it. The experimental results showed that using ResNet-50 which has a 50-layer deep pre-trained version of the convolutional neural network can identify photos that have detailed feature representations for a diverse set of images that can overcome vanishing gradient problems. We obtained a good accuracy of 78%, for age detection. © 2023 IEEE.

关键词： Face recognition

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Book Recommendation Using Double-Stack BERT: Utilizing BERT to Extract Sentence Relation Feature for a Content-Based Filtering System 1

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16th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2023

作者： Suhartono, Derwin Subalie, Adhella Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031364020

ISBN: (纸本)9783031364013

Traditional entertainment such as reading is still heavily enjoyed and is even having an increase in interest. But there is a significant discrepancy in search keywords "book recommendation" and "similar books to", where the latter is higher than the former. This research proposed a method that attempted to recommend books by their story progression that is called Double-Stack BERT which generates a document embedding from a book synopsis as a representation in the vector space. This document embedding will then be measured by its similarity against each other using cosine similarity to retrieve a similar book recommendation. Double-Stack BERT builds the document embeddings by extracting and adding its sentence relation feature by treating each sentence in a document as a token. This addition proved to increase the performance of document embedding when tested on datasets with similar characteristics. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Embeddings

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Tree Counting and Health Assessment in Central Jakarta, Indonesia using Aerial Imagery Data with Deep Learning

Tree Counting and Health Assessment in Central Jakarta, Indo...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Hananto Kusumo, Rafael Edwin Gravita, Adella Prasojo, Ricky Irwansyah, Edy Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327472

Trees play a crucial role as one of the primary oxygen producers. They have particular significance in urban areas, where they contribute to residents physical and mental well-being by providing a healthy living environment. With the important role that trees play in the ecosystem, it becomes imperative to maintain and monitor tree populations. Counting and mapping existing trees is a fundamental step in this process, and deep learning methods offer a practical solution. This research employed the DeepForest algorithm, a Python package capable of detecting trees in aerial RGB imagery. Specifically, this study utilized aerial imagery data to study the Gelora Bung Karno (GBK) area and TPU Karet Bivak in Central Jakarta, Indonesia. The results using the DeepForest algorithm yielded F1 scores of 21% accuracy for the first GBK area, 60% accuracy for the second GBK area, and 70% accuracy for the TPU Karet Bivak area. The lower accuracy in GBK area is influenced by the dense canopy, making it more difficult to detect individual trees. Alongside the DeepForest algorithm, this study also includes tree health assessment by using Visible Atmospherically Resistant Index (VARI) calculation. The VARI analysis relies on the tree counting results from TPU Karet Bivak due to its higher accuracy. The analysis categorizes trees into different classes based on their different levels of health, enabling a more comprehensive review and maintenance process. Specifically, the study identifies 27 trees in needs inspection, 41 trees in declining health, 15 trees in moderate health, and 2 trees identified as healthy. © 2023 IEEE.

关键词： Aerial photography

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Convolutional Neural Network Algorithms for Car Accident Classification 10

Convolutional Neural Network Algorithms for Car Accident Cla...

引用

10th International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2023

作者： Marchella, Angeline Mary Hardiansyah, Naufal Santoso, Patricia Valerie Tjuatja, Carwyn Edbert, Ivan Sebastian Suhartono, Derwin School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350329919

Road accident is one of the top contributors to the number of death tolls around the globe. Consequently, much effort has been put in to minimize road accidents, and not often are many lives lost due to delayed response from emergency medical teams, notably in secluded or congested areas in the event of road accidents. The implementation of numerous deep learning algorithms has been widely used in many research fields;road accident detection is no exception. Therefore, this paper highlights and compares 3 different classification models from convolutional neural networks (CNN), namely MobileNetV3-Large, EfficientNetV2L, and ResNet-50 trained with a dataset of Closed-Circuit Television (CCTV) scenes obtained from Kaggle. The evaluation metrics consist of four categories for each proposed model, such as accuracy, precision, recall, and f1-score from two distinct classes, which are accident and non-accident events. Based on our study, it is evidently proven that ResNet-50 gained the overall highest f1-score of 95.13% followed by EfficientNetV2L and MobileNetV3-Large with 83.24% and 75.56% respectively. Additionally, ResNet-50's result validates the highest point with an average of 95.19% in terms of the remaining evaluation metrics, then 83.49% and 75.62% for EfficientNetV2L and MobileNetV3-Large correspondingly. Ultimately, ResNet-50 serves as the most appropriate and reliable convolutional neural network model in detecting a road accident effectively, followed with the lowest risk of detection inaccuracies. © 2023 IEEE.

关键词： Classification (of information)

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House Base-Price Prediction with Machine Learning Methods

House Base-Price Prediction with Machine Learning Methods

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

作者： Simanungkalit, David Gabriel Immanuel Meylia, Belinda Salim, Julius Edbert, Ivan Sebastian Suhartono, Derwin Bina Nusantara University School of Computer Science Computer Science Department Jakarta11840 Indonesia

ISBN: (纸本)9798350307306

Various factors like location, size, age, and condition must be considered to predict house prices accurately. This study utilizes historical sales data and demographic information in conjunction with machine learning algorithms, such as Random Forest, XGBoost, Support Vector Regression, and Artificial Neural Networks, to forecast home prices. The Kaggle dataset "House Sales in King County, USA"was used to test the proposed models. This research aims to help buyers find affordable properties and make informed decisions. Previous studies have shown that machine learning algorithms are more effective than traditional regression models in predicting house values. By utilizing artificial intelligence and machine learning, this study aims to improve the performance and effectiveness of predicting home prices. Our findings indicate that the ANN model has the highest performance results compared to the other models, with an performance rate of 0.785 before tuning and 0.887 after tuning. XGBoost comes in second with an performance rate of 0.886 after tuning, followed by Random Forest with an performance rate 0.855 after tuning. Finally, SVR is in fourth place with an performance rate 0.849 after tuning. Based on these results, we recommend using the ANN approach for estimating property prices in future research. © 2023 IEEE.

关键词： Neural networks

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Instagram Augmented Reality Marketing in Boosting Brand Awareness and e-WOM in the Indonesian Bottled Tea Industry

Instagram Augmented Reality Marketing in Boosting Brand Awar...

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2024 International of Seminar on Application for Technology of Information and Communication, iSemantic 2024

作者： Rinaldi, Banu Herlina, Maria Grace Iskandar, Karto Bina Nusantara University Business Creation Program Department of Binus Business School Undergraduate Program Jakarta11480 Indonesia Bina Nusantara University Binus Business School Undergraduate Program Management Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350366648

This study explores the efficacy of Instagram's augmented reality (AR) features in enhancing brand awareness and electronic word-of-mouth (e-WOM) within Indonesia's bottled tea industry. The research uses a cross-sectional survey and partial least square structural equation modeling (PLS-SEM) to focus on how AR can transform consumer engagement and interaction on social media platforms. Findings from a sample of 260 bottled tea consumers in Greater Jakarta indicate that AR significantly enhances brand awareness, substantially influencing e-WOM. The integration of AR into Instagram marketing not only captivates and engages users but also fosters stronger consumer-brand relationships and promotes brand loyalty through interactive and immersive digital experiences. The study's implications underscore AR's role in revolutionising marketing strategies by providing immersive experiences that are shown to increase brand visibility and influence purchasing decisions. Limitations and future research directions are discussed, highlighting the potential for broader applications of AR in different market segments and its impact on various consumer demographics. © 2024 IEEE.

关键词： Commerce

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Edu Venture Application: Bridging Honorary Teacher with Skill-BasedProject

Edu Venture Application: Bridging Honorary Teacher with Skil...

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2023 International Conference on Information Technology and Computing, ICITCOM 2023

作者： Rahmadhani Citra Karisma, Chantikka Riffka Vincent Kesuma, Michael Awarsa Kurniawan, Daniel Kevin Kudori, Dio Saputra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350359633

In an era characterized by rapid digital transformation, the education sector stands at the forefront of the quest for tailored and personalized solutions. However, educators, especially honorary teachers, have often been overlooked in the development of mobile apps and platforms. These educators face distinct challenges, such as temporary contracts and limited institutional support. The EduVenture application, developed using the Rapid Application Development (RAD) methodology, addresses these unique needs by offering specialized job opportunities. Employing user-centric design and the innovative 'Skills Based Project Recommendations' feature, EduVenture provides a comprehensive platform tailored to educators, redefining their engagement with professional opportunities. A User Experience Questionnaire (UEQ) evaluation underscores the app's excellent attractiveness and stimulation while rating it as good in perspicuity, efficiency, dependability, and novelty. Overall, EduVenture represents a significant step toward reshaping how honorary educators interact with the digital professional world. This research elucidates the development process, confirming its potential to revolutionize educational employment platforms. © 2023 IEEE.

关键词： E-learning

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