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Through the Lens: Unveiling the Power and Promise of Google's Visual Search Technology 9

Through the Lens: Unveiling the Power and Promise of Google'...

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

作者： Salim, Devano Hofker Susanto, Jose Juan Manalu, Sonya R. Shiddiqi, Hafizh Ash School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Mobile Application & Technology Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

The digital landscape is undergoing profound changes, with visual search technology like Google Lens emerging as a pivotal force shaping our interaction with information. This research paper delves into Google Lens, a cutting-edge tool redefining how we engage with visual data. We comprehensively examine its functionalities, underlying principles, and real-world applications, particularly in education and library services. Through meticulous analysis, we evaluate its efficacy in addressing practical challenges and assess its accuracy, efficiency, and breadth of capabilities compared to other visual search technologies. By exploring existing research and the intricacies of Google Lens, this paper aims to provide a holistic understanding of this technology and its potential impact on how we access and interact with information in the real world. Finally, we explore the future of technology by contemplating the prospective roles of Google Lens across various industries and its potential for shaping the future of information access. © 2024 IEEE.

关键词： future of technology google lens visual search technology

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Use of Deep Learning to Identify COVID-19 through DNA/RNA Sequencing 4

Use of Deep Learning to Identify COVID-19 through DNA/RNA Se...

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4th International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2024

作者： Triananda, Gregory Febrian, Dylan Jabar, Bakti Amirul Purnomo, Fredy Prasetyo, Simeon Yuda School of Computer Science Bina Nusantara University Computer Science Department Jakarta15143 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350378368

COVID-19, or coronavirus, is a virus that causes a pandemic where the severe acute respiratory syndrome virus 2 (SARS-Cov-2) affects the respiratory organs such as the lungs. As of 4th February 2024, there are over 774 million confirmed cases of COVID-19 with more than 7 million deaths have been reported globally. Although the pandemic has slowly become controlled over the years, there is room to find a more effective way to cure COVID-19. The aim of this study is to find out an effective machine learning model to identify COVID-19. The dataset used for this study is the GSE152418 from GEO. After testing 4 proposed models, this study shows that RFE-SVM is the most effective method for identifying COVID-19, with an accuracy of 0.8733, precision of 0.9021 and a recall score of 0.8571. Due to the high score across all metrics, the RFE-SVM model is able to be used and improved for future studies. © 2024 IEEE.

关键词： COVID-19

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Approaches, Applications, and Challenges of Using Sentiment Analysis for False Information Detection: A Systematic Literature Review 19

Approaches, Applications, and Challenges of Using Sentiment ...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Fanlau, Nathan Edmund Hiu, Michael Achmad, Said Sutoyo, Rhio Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331507817

In today’s digital age, the prevalence of false information on the internet, spanning from fake news to fake user accounts, poses a pressing concern that undermines the reliability of online information. Such misinformation can have harmful consequences, such as promoting biased ideologies, swaying public opinion, and inciting unrest. Implementing sentiment analysis technology in false information detection significantly impacts combating misinformation on digital platforms. It analyzes textual content in real time, which is crucial for effectively identifying and mitigating false information such as fake news, fake reviews, fake accounts, and fake websites on digital platforms. This paper explores sentiment analysis’s approaches, applications, and challenges for false information detection, providing insights for future research and application. A systematic literature review was conducted using the PRISMA methodologies to identify and evaluate relevant studies. The results of this paper contribute to understanding the current state of sentiment analysis in combating misinformation. This paper provided the common and available applications of sentiment analysis, such as fake news detection, fake review detection, fake social media account detection, and more. Approaches that can be taken to achieve these applications include the machine learning approach, neural network approach, hybrid approach, and more. Moreover, this work highlights the need for more advanced algorithms and ongoing research to address challenges such as sarcasm detection, bot account identification, and effectively interpreting informal language and slang in texts. © 2025 IEEE.

关键词： false information machine learning natural language processing sentiment analysis

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Attention Mechanisms in Semantic Convolutional Neural Networks for Flood Area Segmentation

Attention Mechanisms in Semantic Convolutional Neural Networ...

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2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Basit, Muhammad Banin Shidqi, Muhammad Dzaki Prasetyo, Simeon Yuda Purwanto, Eko Setyo School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331513320

Flood area segmentation remains a significant challenge in remote sensing, primarily due to complex textures, lighting variations, and shadow effects in flood imagery. This study introduces Attention-SCNN, a novel semantic convolutional neural network framework enhanced with spatial and channel attention mechanisms. These modules refine feature maps by prioritizing relevant regions while suppressing noise, addressing critical segmentation challenges. Using the Flood Segmentation Area dataset, the proposed model achieved superior performance metrics, including an IoU of 67.04%, Dice Coefficient of 80.26%, and Accuracy of 84.89%, outperforming state-of-the-art models such as Fast-SCNN, Mobile Fast-SCNN, and PSPNet. Comparative analyses revealed that AttentionSCNN effectively delineates flood boundaries, reduces false positives, and demonstrates robust segmentation capabilities under adverse conditions. Furthermore, its lightweight architecture ensures computational efficiency, enabling realtime flood monitoring in resource-constrained environments. These findings underscore the potential of Attention-SCNN for advancing disaster management systems and highlight directions for future research, including the integration of multi-modal data for enhanced segmentation performance. © 2025 IEEE.

关键词： Convolutional neural networks

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The future of virtual reality: Prospect and problems 9th

The future of virtual reality: Prospect and problems

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

作者： Lie, Andrew Alfonso Buntoro, Owen Tamashi Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Virtual Reality (VR) is a technology that allows users to interact with a simulated environment created by virtual reality capabilities through the intermediary of computers, simulator tools, and others, providing new experiences and can feel experiences that are not possible in the real world. This research aims to identify and analyze what challenges may occur such as technical, social, and ethical barriers in the development and application of VR technology. Using a mixed methods approach, we collected quantitative data from Kaggle's dataset and conducted in-depth interviews with active VR users. Our findings show that although VR has significant potential in various fields, such as education, healthcare, and entertainment, challenges such as motion sickness, high costs, and limited and engaging content hinder the widespread adoption of VR technology. The study also highlights the importance of improving technologies that need to be implemented such as visual capabilities, motion responsiveness, and audio quality to enhance the user experience when using VR. By overcoming these barriers, VR can maximize its positive impact and become a revolutionary tool in various sectors. © 2024 The Authors.

关键词： Virtual environments

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AI-Powered Mobile Application for Image-Based Food Ingredient Detection and Recipe Generation 9

AI-Powered Mobile Application for Image-Based Food Ingredien...

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

作者： Rosaline, Thessalonica Maria Luisse Suciawan, Calvin Anacia Ng, William Achmad, Said Moniaga, Jurike V School of Computer Science Bina Nusantara University Mobile Application & Technology Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

This research explores the critical impact of dietary habits on health, addressing the lack of awareness regarding nutritional content that leads to issues such as malnutrition. The proposed mobile application utilizes the YOLOv8 algorithm with a deep convolutional neural network (CNN) architecture, deployed on the Roboflow server, a local MySQL database server, and ChatGPT. The architectural framework of this application is integrated through the NodeJs server via an API system embedded within the Android application. Despite this approach, the study highlights the need for further refinement in the accuracy of the ingredients' object detection model. The refined YOLOv8 model, trained on an extensive dataset of 24,583 images across 13 food ingredient classes, outperforms the initial YOLOv8 model trained on 9,867 images. Improvements are notable, particularly in addressing misclassifications of ingredients such as beef, pork, onion, and egg in the initial model. However, the YOLOv8 model can still be improved by introducing more ingredient variations and expanding the training dataset. This application can detect multiple food ingredients from an image and evaluate their nutritional value, specifically in calorie count. It also provides tailored recipes to help users meet their daily nutritional requirements, offering a practical solution for users to achieve their dietary goals. This research connects computer vision and nutrition, providing insights for developing practical solutions in this domain. © 2024 IEEE.

关键词： Convolutional neural networks

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Trend analysis in competitive programming using topic modeling 9th

Trend analysis in competitive programming using topic modeli...

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

作者： Ramahalim, Albert Yulius Karuniawan, Christoffer Edbert Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Competitive programming (CP) is a mind sports activity where people solve problems using command-line computer programs to provide correct output for the given test cases. Competitors need to practice problem-solving and mathematics as well as study algorithms and data structures to perform well in CP. This study aims to provide an original way to perform a trend analysis in CP, distinguishing topics frequently used in CP contests. To fulfill our goal, we create topic models based on previous topic modeling works to do natural language processing tasks using Latent Dirichlet Allocation (LDA) and Biterm Topic Model (BTM). For our dataset, we constructed a corpus from Codeforces blog posts, a popular website for competitive programmers, by extracting its content and user comments. Our results indicate that BTM is powerful enough to do trend analysis in CP. The trend analysis recognized that dynamic programming and complexity analysis have been the most prominent topics for the last ten years. Data structures and string algorithms are runners-up that may have potential trends in the future. This study opens up further research on other methods to perform trend analysis using better topic models and corpora. © 2024 The Authors.

关键词： Modeling languages

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Enhancing User Interface and Experience in Skin Type Classification and Skincare Product Recommendation System for Young Adults 9

Enhancing User Interface and Experience in Skin Type Classif...

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

作者： Himawan, Felicia Soerjadi, Vania Paramitha Handoyo, Yonathan Achmad, Said Moniaga, Jurike V School of Computer Science Bina Nusantara University Mobile Application & Technology Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

The escalating interest of skincare among Indonesian young adults reflects the rising trend in the skincare industry. However, this heightened interest also brings several challenges, like the lack of knowledge and confusion when selecting the right skincare products for our skin type. This underscores the need for an effective and efficient skin type classification and product recommendation system. This paper addresses this challenge by focusing on enhancing a skincare application's user interface and overall experience. This research employed User-Centered Design (UCD), implementing Design Sprint, and Goal-Directed Design (GDD) methodologies to achieve a high value of user satisfaction. The Sprint process is used to create the design prototype efficiently, and the Goal-Directed method is performed to create specific, measurable goals. Our evaluation shows that an iterative design process based on user feedback results in higher user satisfaction. The analysis indicated a significant increase of approximately 25% in both effectiveness and efficiency among users when using the application. Initially, the first iteration reveals a low value of approximately 59% in overall effectiveness and efficiency. However, a substantial boost to around 83.5% was seen after a redesign. This improvement proved that User-Centered Design methodologies highly influence user perception and satisfaction. © 2024 IEEE.

关键词： design sprint goal-directed design skincare UI/UX user-centered design

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Implementation of Gamification to Help Teenagers Prevent Diseases 9

Implementation of Gamification to Help Teenagers Prevent Dis...

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

作者： Haryanto, Michael Djaja, Fabian Putera Chrisandy, Michael Achmad, Said Moniaga, Jurike V Bina Nusantara University Mobile Application & Technology Program Computer Science Department School of Computer Science Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Adolescence is an important part of growing up to become an adult;keeping adolescent physique healthy is very important because poor dietary choices and a sedentary lifestyle will lead to diseases that they shouldn't have experienced yet. That is why teenagers need to be engaged to do more health- promoting behaviors, one way is by implementing gamification. This paper seeks to study which kind of gamification elements can be implemented from past research and whether the results were effective or not to prove whether gamification is a valid and effective strategy. At the same time, we are creating a gamified prototype for the mobile platform. The methodology starts with a systematic review of PubMed, MDPI, and Google Scholar to get a general idea of which gamification elements are the most implemented. Then a prototype will be made with the most used gamified elements from the systematic review. And then participants ranging from 10 - 19 years old will test the prototype and will give their opinions via a survey, From this, it is proven by our own gamified prototype survey results that using gamification as a way to engage teenagers is a fun and engaging way to do more health-promoting behaviors with a mostly positive feedbacks with the average rating of 8.33 out of 10 given from the participants. © 2024 IEEE.

关键词： Gamification

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News Sentiment-Enhanced GRU Encoder-Decoder for Advanced USD/IDR Exchange Rate Forecasting

News Sentiment-Enhanced GRU Encoder-Decoder for Advanced USD...

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2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Sutanto, Nathaniel Susianto Lucky, Henry School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331513320

Accurate prediction of a currency's exchange rate movement is crucial to various stakeholders, especially in the financial sector. Successful prediction could provide an insight into the market's movements and thus give a competitive advantage to traders. Unfortunately, this task proves to be challenging due to the numerous factors affecting a currency's exchange rate. This study explores the impact of news headline sentiments towards exchange rate prices. This study focuses on improving USD/IDR exchange rate predictions using Recurrent Neural Networks (RNN) and its variations by adding sentiment data. To achieve this goal, the research would process relevant financial news headlines using FinBERT to obtain sentiment data. The sentiment data would then be integrated into historical USD/IDR close prices. Afterwards, several multistep forecasting models are trained to predict the exchange rate prices 12 days ahead. 6 different RNN models were trained, two sets of LSTM, GRU and encoder-decoder GRU models are each trained on sentiment-added and historical price data. The results are evaluated using three evaluation metrics which are RMSE, MAPE and MAE. The results demonstrate that models trained on the added sentiment data obtained lower errors when forecasting USD/IDR prices, with the best model achieving an RMSE of 299.0. In particular, the encoder-decoder models achieved impressive long-term forecasting performance where it managed to maintain a stable error score. © 2025 IEEE.

关键词： Recurrent neural networks

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