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A Systematic Literature Review: The Effectiveness of ANPR for Electronic Law Traffic Enforcement 3

A Systematic Literature Review: The Effectiveness of ANPR fo...

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3rd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2023

作者： Setiadi, Gaudentius Matthew Stefani, Yulia Gunawan, Alexander Agung Santoso School of Computer Science Bina Nusantara University Mathematics Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

Automatic number-plate recognition (ANPR) is a system that reads vehicle registration plates and generates vehicle location data using optical character recognition. The implementation of Automated Number Plate Recognition for Electronic Law Traffic Enforcement has been a common thing lately. The government is urged to use this system for its efficiency in Traffic Enforcement. This Automatic Number Plate Recognition system mainly uses similar algorithms Plate acquisition, Plate Detection/Localization, Character segmentation, and Character Recognition. There are various techniques for each Detection, Segmentation, and Recognition. The authors of this study intend to undertake a Systematic Literature Review (SLR) of previously conducted research on the subject. Therefore, this paper discusses the method/techniques in Automated Number Plate Recognition, the reason they are being used, the accuracy of each method, and the devices that can process each system. Towards the end of this study, Edge information is the most effective and accurate based on localization, for segmentation is character feature, and Support Vector Machine (SVM) for character recognition. © 2023 IEEE.

关键词： Support vector machines

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Comparison of Machine Learning Models for Classification of DDoS Attacks 5

Comparison of Machine Learning Models for Classification of ...

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Prima, Felix Dylan, Leonardo Gunawan, Alexander Agung Santoso Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

Distributed Denial of Service (DDoS) is one of the major threats for security networks and systems. The DDoS attack is flooding a network with huge packets to weaken the performance of the network. The ability to accurately and classify these attacks is crucial for mitigating the security problem. In this paper, machine learning models are compared in a classification task for detecting benign (normal) and DDoS attack traffic. This paper used 14 different popular classifier models. The models are trained and tested by using 'DDoS Dataset' from Kaggle which is the extraction from public IDS Datasets which captured in different years from 2016-2018. The experiment result shown that all the models have good scores in precision, recall, and F1 score with Random Forest algorithm shown the best result at 100% accuracy. © 2023 IEEE.

关键词： Denial-of-service attack

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Towards the Fun Therapy on People with Mental Disorder – Analysis on Serious Game for Mental Therapy 6

Towards the Fun Therapy on People with Mental Disorder – An...

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

作者： Pratama, Galih Dea Soeparno, Haryono Arifin, Yulyani Gaol, Ford Lumban Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Mental health is one of the states of well-being which enables people to cope with various things in everyday life. Despite that, mental health is often overlooked, driving the emergence of numerous mental disorders on people. Along the presence of technological advancements, there is serious game which is utilized to entertain and achieve non-game goals related to real-life issues. Due to that, serious game can be leveraged as a media put into health sectors, particularly in presenting fun therapy to people diagnosed with mental disorders. This research presents systematic literature review on publications discussing the utilization of serious game in providing fun therapy for people with mental disorders, focusing on matters like the mental disorders emphasized within, the game elements presented in each publication, and ways to evaluate the serious game. The result indicates overall well-received responses regarding the usage of serious game in serving the purpose, albeit with different ways on evaluation process based on the mental disorders each publication addressed for. © 2024 IEEE.

关键词： Serious games

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Enhancing Bankruptcy Prediction with Feature Selection in AdaBoost Algorithm 10

Enhancing Bankruptcy Prediction with Feature Selection in Ad...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Ferren, Felicia Kurniadi, Felix Indra School of Computer Science Bina Nusantara University Statistics Department Jakarta11530 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350339543

Since the 1960s, bankruptcy prediction research has been intensively examined, with the importance of accurate forecasting representing a nation's economic development. The high costs related to business failures have prompted efforts to enhance prediction techniques, which have been highlighted further as the effects of globalization have increased. There are two primary types of bankruptcy prediction models: statistical and artificial intelligence (AI) methods. The study found that several AI algorithms, namely C5.0, CART, and Support Vector Machines with evolutionary computation, demonstrated efficacy in forecasting impending bankruptcies, as well as short-and long-Term predictions. Prior studies have proposed the utilization of Multilayer Perceptron (MLP), ensemble learning methodologies, and AdaBoost as potential approaches for the purpose of bankruptcy prediction. The significance of feature importance is frequently disregarded in such models, resulting in prejudiced outcomes. This study presents a proposed approach for feature selection in AdaBoost algorithm that incorporates the consideration of feature importance, with the aim of enhancing the aforementioned findings. The proposed methodology is validated through a comparative analysis with several other machine learning algorithms. The result showed that ADABoost with feature selection using feature importance generated better value with increased around 5-7% in accuracy score. © 2023 IEEE.

关键词： Adaptive boosting

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File Encryption Application using Menezes-Vanstone Elliptic Curve Cryptography Based on Python 8

File Encryption Application using Menezes-Vanstone Elliptic ...

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

作者： Heru, Edward Keitaro Faisal Pranoto, Hady Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Humans have been affected by the advent of information technology in many different ways. Information can be sent freely as a result of the conveniences offered. However, unauthorized parties may take advantage of this to retrieve and utilize the information without the owner's consent. In this paper, the authors perform text, image, audio, and video data security using a modified version of the Menezes-Vanstone elliptic curve cryptography algorithm. The study implements Menezes-Vanstone Elliptic Curve Cryptography in a program for file text, image, audio, and video encryption and decryption, while also providing performance data for both our program and alternative approaches for comparison. The outcome demonstrates that the encrypted file size is smaller than alternative approaches. Nevertheless, our program might benefit from some enhancements in terms of encryption and decryption times. © 2023 The Authors. Published by Elsevier B.V.

关键词： Elliptic curve cryptography(ECC) file encryption Menezes-Vanstone ECC

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Comparative Evaluation of Several Convolutional Neural Network Architectures in Diabetic Retinopathy Classification

Comparative Evaluation of Several Convolutional Neural Netwo...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Yuda Prasetyo, Simeon Nurhasanah Fadlan Hidayat, Muhammad Nabila Izdihar, Zahra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369359

The increasing global prevalence of diabetes mellitus has brought diabetic retinopathy (DR) to the forefront as a significant cause of visual impairment and blindness among adults. DR, a complication of diabetes, can be broadly categorized into Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR), each comprising distinct clinical stages with varying severity. The accurate and timely detection and classification of these stages are essential for preserving vision, a task that grows in importance as the worldwide diabetes epidemic continues to rise, with an estimated 693 million cases projected by 2045. In response to this healthcare challenge, the development of efficient automated systems for DR diagnosis and classification has emerged as a critical research focus. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have demonstrated substantial promise in addressing this issue. This research presents a comparative evaluation of three prominent CNN architectures, namely Le-Net, AlexNet, and VGG16, in the context of DR classification, aiming to provide valuable insights into their performance. Among these architectures, VGG16 emerges as the standout performer, achieving an accuracy of 74.4%. This outcome underscores the potential of VGG16 as a robust tool for DR diagnosis, with its deeper and more intricate architecture proving adept at capturing intricate retinal features. As the research landscape evolves, future directions may encompass exploring newer model variations, incorporating more extensive and diverse datasets, fine-tuning hyperparameters, and enhancing the interpretability of deep learning models for real-world medical applications, ultimately propelling the field toward more efficient and accurate DR diagnosis and classification. © 2023 IEEE.

关键词： Classification (of information)

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Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine

Sentiment Analysis in Indonesian Trading using Lexicon-based...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Oetama, Raymond Sunardi Yanfi, Yanfi Ikhsan Assiddiq Up, Masagus M. Muhamad Isa, Sani Universitas Multimedia Nusantara Information System DepartmentFaculty of Engineering and Informatics Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%. © 2023 IEEE.

关键词： Support vector machines

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A Systematic Literature Review: Diabetic Retinopathy Detection Using Deep Learning

A Systematic Literature Review: Diabetic Retinopathy Detecti...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Suwandi, Felicia Aurelia Kurnianda, Paulus Ricky Gunawan, Alexander Agung Santoso Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339215

Diabetic retinopathy is a form of complication of diabetes mellitus in which high insulin levels eventually result in damage to the blood vessels of the retina of the eye, especially in light-sensitive tissues. Diabetic retinopathy can cause blindness if it is still untreated. To detect diabetic retinopathy, some doctors are evaluating using digital color images to see the retinal fundus to determine if there is any presence of lesions or vascular abnormalities on the eyeball. Looking at those conditions, this paper will summarize many of the papers which had innovated some detection of the emersion of diabetic retinopathy automatically using deep learning concepts to make the detection way become more accessible and has a high level of accuracy. It also would have much more time-saving than doing it manually. This paper used a systematic literature review based on several papers from the Google Scholar search engine with the keywords which must contain "diabetic retinopathy"and "deep learning". This paper aims to assist in the detection of DR using deep learning and evaluate the literature from various papers to obtain important information in a more concise manner regarding the most widely used datasets and the details, how deep learning algorithms work and detect DR, as well as make a comprehensive evaluation of the performance of different deep learning models and bring up an overview and design. It will focus on key metrics such as accuracy, sensitivity, specificity, and AUC (Area Under the Curve), which are common indicators for assessing the effectiveness of detection algorithms. This paper found that the MESSIDOR is the most often used dataset. This paper also found that the most widely used model and the highest percentage of accuracy or AUC is ResNet-50. The ResNet-50 model produces accuracy figures that are always over 95% © 2023 IEEE.

关键词： Search engines

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Deep Learning-based Approach on sgRNA off-target Prediction in CRISPR/Cas9

Deep Learning-based Approach on sgRNA off-target Prediction ...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Imani, Alyssa Valiant, Jonathan Santoso Gunawan, Alexander Agung Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

CRISPR/Cas9 as a gene editing tool has been widely applied in many organisms. However unintended mutation caused by sgRNA off-target effect is still possible to occur while implementing CRISPR/Cas9. To reduce the off-target possibility some researchers already develop deep learning models to predict the sgRNA off-target to the corresponding DNA target. Those models implement deep learning approaches such as CNN and embedding to compute and obtain the off-target scores. Therefore, the aim of this study was to compare five different combination models of Word2Vec embedding and CNN to find which one is the best for predicting off-target sgRNA in classification schema. The final result shows that a combination of Word2Vec embedding and biLSTM in CNN model can achieve auROC and auPRC score of 99.61% and 86.67% respectively, which is better than CnnCRISPR model that was used as the reference for model architecture in this study. By comparing five different models, the highest accuracy achieved in this experiment reached 99.67%. © 2023 IEEE.

关键词： Embeddings

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Comparative Performance Analysis of Javascript Frontend Web Frameworks 3

Comparative Performance Analysis of Javascript Frontend Web ...

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3rd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2023

作者： Tong, Jason Jikson, Ricky Rivaldo Gunawan, Alexander Agung Santoso School of Computer Science Bina Nusantara University Mathematic Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

One of the most popular tools today for building engaging, robust, and easy to manage websites is the JavaScript programming language. Over the past 10 years, numerous front-end frameworks have been developed on top of JavaScript, making it simpler to develop, design, and maintain interactive websites. Currently, the most popular JavaScript front-end frameworks are *** and ***. *** is a relatively new framework that is also catching developers' attention and growing in popularity. This paper assesses the performance of these prominent front-end JavaScript frameworks, analyzing aspects like browser performance, speed index, interactive time, and the efficiency of specific DOM operations such as adding, editing, and removing elements. Using benchmark applications tailored for each framework, we conducted extensive testing to gauge their performance across different DOMs, leveraging technology-based data. Finally, our study of three front-end frameworks concluded that *** offers superior performance, ***'s focus on JavaScript dynamics leads to latency, and *** exhibits average speed, particularly in 'to-do list' and 'image gallery' project contexts. © 2023 IEEE.

关键词： Websites

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