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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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Enhancing Waste Classification with YOLOv8 Models for Efficient and Accurate Sorting 9th

Enhancing Waste Classification with YOLOv8 Models for Effici...

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9th International Conference on Computer Science and Computational Intelligence, ICCSCI 2024

作者： Vieri, Deverel Susanto, Rendy 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

As of 2023, Indonesia ranks as the second largest waste-producing country globally. The waste produced is not segregated properly, leading to the vast amount of waste piling up in local landfills. Traditional methods, such as manual sorting, have been widely used to segregate waste but suffer from inefficiencies and inaccuracies. In contrast, deep learning models offer an alternative solution for waste classification, overcoming the limitations of traditional methods. A deep learning approach using YOLOv8 was proposed to classify waste into six distinct categories. Three different YOLOv8 variants: nano, small, and medium, are trained after the dataset has been augmented into 3,500 labeled images. The results indicate that these models were able to achieve high accuracy in classifying images, with the nano variant having the least training time and an accuracy of approximately 89%. © 2024 The Authors.

关键词： Contrastive Learning

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Quantitative analysis of sign language translation using artificial neural network model 9th

Quantitative analysis of sign language translation using art...

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9th International Conference on Computer Science and Computational Intelligence, ICCSCI 2024

作者： Wijaya, Fendy Dahendra, Leonardo 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

Sign language is one of the technique to support communication with deaf and speech impaired people. Nowadays, human needs become more complex, so are the needs of people with those disabilities. Therefore, with the sophistication of modern technology in the field of computer science, it is necessary to have a sign language translation system which is capable to convert human gestures into words or spelling letters in a natural human language, especially Indonesian language and SIBI sign language. This research is conducted to analyze the capabilities of an Artificial Neural Network (ANN) system to translate SIBI sign language into Indonesian language. The analysis is performed quantitatively from the experiment results to gain a descriptive insights for the generated models. By creating two datasets containing alphabets and words in SIBI sign language, two models were generated to predict alphabets and words, respectively. The result shows that ANN model could effectively and efficiently perform a sign language translation with accuracy of alphabet prediction reached 96.15% and words prediction reached 99.45% in duration of prediction without exceeding 0.15 seconds. Regarding to the results, it can be concluded that ANN model quantitatively suitable to be implemented as an Indonesian sign language translator system. © 2024 The Authors.

关键词： Neural machine translation

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Predictive Analytics for Future Air Pollution Levels Based on Population Growth

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Procedia Computer Science 2024年 245卷 834-843页

作者： Darel Yeshe Irma Kartika Wairooy Brilly Andro Makalew Computer Science Department School of Computer Science Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Jakarta Indonesia 11480

Air is very beneficial and crucial for every living creature on earth, hence it is very important to protect the air quality in order to avoid diseases. However, due to the increase in population, some human activities have been causing detrimental damage to the air quality. Some people have realized the potential impact of this problem and have done studies on predicting future air pollution. Some implemented machine learning models such as random forest regression, support vector machine, etc, while others have utilized deep learning models in their research.. This study will implement five different models, specifically linear regression, ridge regression, random forest regression, and multilayer perceptron regression. There will be two datasets used in this paper which will be merged and processed. There will be three key evaluation metrics being used in this paper namely root mean squared error, mean absolute error, and r-squared. The results from all of the models have concluded that more optimization and factors are needed in order to boost the final result of this study.

关键词： Machine Learning Predicting Regression Algorithm technology Education Artificial Intelligence

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Database Availability Security: Handling Denial of Service (DoS) Attacks

Database Availability Security: Handling Denial of Service (...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Jeffrey Surianto Irma Kartika Wairooy Sandy Wirananda Brilly Andro Makalew Computer Science Department School of Computer Science Jakarta Indonesia Computer Science Department Mobile Application & Technology Program Jakarta Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

In the data based digital age, there are risks of attacks that cause denial of service (DoS) or distributed denial of service (DDoS) which pose threats to the security and availability of databases especially with those who are affected by this which is very disturbing. This research aims at analysing the methods of detecting and preventing DoS and DDoS attacks, with more focus on the security of databases. This research identifies and evaluates various strategies employed to counter such threats to cloud and Internet of Things (IoT) systems from the literature review conducted using the PRISMA approach. The analyses show that machine learning algorithms are capable of enhancing the detection of DoS/DDoS attack's efficiency and accuracy, and the cloud and IoT security solutions, including complex load balancing and powerful firewalls, are crucial for preventing and mitigating such attacks. This study also shows that, AI should be included in the security plans and security measures should be adaptable and proactive. This work is useful for scholars, developers, and cybersecurity experts who are interested in enhancing measures to counter and prevent advanced cybercrimes and ensuring continued and reliable functionality in data centric settings.

关键词： Firewalls (computing) Prevention and mitigation Distributed databases Telecommunication traffic Denial-of-service attack Load management Security Internet of Things Computer crime Protection

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Addressing the role and opportunities of machine learning utilization in brain tumor detection

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Procedia Computer Science 2024年 245卷 869-878页

作者： Vallerie Delia Lesmana Holly Agustine Irma Kartika Wairooy Brilly Andro Makalew Computer Science Department School of Computer Science - Binus University Jakarta 11480 Indonesia Mobile Application & Technology Program Computer Science Department - Binus University Jakarta 11480 Indonesia

This research aims to develop a brain tumor detection model by utilizing the machine learning techniques and Convolutional Neural Network (CNN). A significant matter to address is revolving around early detection and the proper handling regarding the brain tumor. This research's methodology consists of collecting the dataset, identifying the tools and language to use, prepare and preprocessing the data, data augmentation, splitting and label encoding, building the model architecture, compiling the model, training, and evaluating the model, predicting the model, and comparing it with other models. Dataset consists of 7022 MRI images, divided into training and testing subsets; and four classes: glioma, meningioma, pituitary, and no tumor. There are four different CNN models that have been built and evaluated, namely VGG16, InceptionV3, ResNet50, and DenseNet121. The result gained shows VGG16 with the best performance achieving an accuracy rate of 96.43%, followed by DenseNet121 (94,96%), InceptionV3 (92,40%), and ResNet50 (78,69%). Although there is still room for improvement regarding overfitting and increasing the models’ overall performance, this result is promising enough to enhance early diagnosis and offer an appropriate and effective treatment for patients.

关键词： Machine Learning Deep Learning CNN Brain Tumor Detection MRI VGG16 InceptionV3 ResNet50 DenseNet121

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Mental illness detection using sentiment analysis in social media

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Procedia Computer Science 2024年 245卷 971-978页

作者： Kasimirus Derryl Odja Jasson Widiarta Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

This research tries to detect mental illness using sentiment analysis on Reddit data, as well as comparing the performance of the k-Nearest Neighbors (k-NN), Random Forest, and Neural Network models. Using text post data from the pre-pandemic and post-pandemic periods, we concluded that the Random Forest model had the highest overall performance with an F1 Score, accuracy, recall and precision of 80.6%, making it quite effective in detecting depression. Even though the Neural Network model shows slightly lower accuracy, namely 79%, in fact this model has the lowest error rate, namely 0.06496. The k-NN model showed the lowest accuracy and higher error rate. These findings highlight the potential of sentiment analysis and machine learning in identifying mental health issues on social media and suggest that better models can improve early detection and intervention efforts.

关键词： Sentiment Analysis Mental Illness k-NN Random Forest Neural Network

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Quantitative analysis of sign language translation using artificial neural network model

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Procedia Computer Science 2024年 245卷 998-1009页

作者： Fendy Wijaya Leonardo Dahendra Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

关键词： sign language Artificial Neural Network SIBI prediction quantitative analysis

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The future of virtual reality: Prospect and problems

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Procedia Computer Science 2024年 245卷 355-364页

作者： Andrew Alfonso Lie Owen Tamashi Buntoro Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

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.

关键词： Virtual Reality immersive experience technical barriers social impact ethical considerations

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Trend analysis in competitive programming using topic modeling

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Procedia Computer Science 2024年 245卷 591-600页

作者： Albert Yulius Ramahalim Christoffer Edbert Karuniawan Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

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.

关键词： competitive programming topic modeling trend analysis

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