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2021 11th International Workshop on computer science and Engineering, WCSE 2021

作者： Salim, Calvin Erico Rudy Suhartono, Derwin Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9789811817915

Hate speech is one of the most challenging problem internet is facing today. With increasing numbers of users online, hate speech also rise and takes time to be classified manually particularly in languages other than English. This research examines hate speech detection problem in form of Bahasa Indonesia. Millions of comments and text posts are added to various social media and discussion platforms. Manual classification in all of the internet as hate speech and offensive language is a near impossible and time-consuming task. This research uses Long Short-Term Memory (LSTM) and Bidirectional Long Short Term Memory (Bi-LSTM) for the method of classifying hate speech and abusive language. The final accuracy is 88,44% by using 200 neurons with Bi-LSTM method. Most common challenges are different languages, out of vocabulary words, long range dependencies, and sarcasm. © 2021 11th International Workshop on computer science and Engineering, WCSE 2021. All Rights Reserved.

关键词： Long short-term memory

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Enhancing cybersecurity resilience through advanced red-teaming exercises and MITRE ATT&CK framework integration: A paradigm shift in cybersecurity assessment

Cyber Security and Applications

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Cyber Security and Applications 2025年 3卷

作者： Yulianto, Semi Soewito, Benfano Gaol, Ford Lumban Kurniawan, Aditya Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

As cybersecurity threats evolve alarmingly, conventional defense strategies are becoming increasingly ineffective. In response to this urgent challenge, our study presents a transformative approach to red-teaming exercises by integrating the MITRE ATT&CK framework. This innovative integration leverages real-world attacker tactics and behaviors to create highly realistic scenarios that rigorously test defenses and uncover previously unidentified vulnerabilities. Our comprehensive evaluation demonstrates a significant enhancement in the realism and effectiveness of red-teaming, leading to improved vulnerability identification and the generation of actionable insights for proactive remediation. This study uniquely contributes by providing a structured, data-driven methodology that aligns current defenses with emerging threat tactics and promotes continuous improvement in cybersecurity resilience. Such advancements are essential for organizations seeking to stay ahead of attackers and maintain robust cyber defenses in dynamic threat environments. The significance of this study lies in offering a structured approach to strengthen cybersecurity resilience against evolving threats and providing a model for continual defense enhancement. Future research should investigate the impact of this integration on long-term cybersecurity strategies. © 2024 The Authors

关键词： Continuous defense improvement Cyber resilience Cybersecurity MITRE ATT&CK framework Red-teaming exercises

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Object Size Recognition as Intra-class Variations using Transfer Learning

Object Size Recognition as Intra-class Variations using Tran...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Rissa Rahmania Harco Leslie Hendric Spits Warnars Benfano Soewito Ford Lumban Gaol Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

The ability to differentiate various products in the retail store plays an essential role to provide effectiveness to customers and reduce or even eliminate long queues. However, traditional machine learning algorithms are incapable of recognizing many subordinate categories in various retail product. This paper aims to recognize the retail product recognition algorithm based on the YOLOv7 model in terms of intra-class variations, with the sub-categories of brand and size. We used two schemes of the dataset to compare recognition performance between them. Firstly, the YOLOv7 is applied in the two schemes of the dataset that is annotated with the subordinate category to detect the brand as meta category. Secondly, the proposed method is applied by adding the object size classification into the YOLOv7 model where the square area of the bounding box was calculated to classify the product according to size. Confidence score and square area are used to verify the object and to obtain the product size, which represents sub-category of the product. The experimental results show that our proposed method achieves higher recall compared to baseline object detection.

关键词： computer science Machine learning algorithms Automation Transfer learning Object detection computer architecture Information technology

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Service Oriented Fuzzy Decision Model for Road Traffic Engineering

Service Oriented Fuzzy Decision Model for Road Traffic Engin...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Ditdit Nugeraha Utama Fauzan Affan Zaki Intan Juliani Munjeri Nidya Utami Putri Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department UIN Syarif Hidayatullah Jakarta Indonesia

A number of attempts have already been implemented formally to solve road traffic congestion. However, the objective strategy type of road traffic engineering could not be proven truly. Try and error is one inefficient way in road traffic engineering to degrade the level of congestion. The study was conducted to propose a new service-oriented model (SOM) that combines fuzzy-logic and water flow algorithm methods (called FWFA). The method combination was operated as the main method to construct the decision model for selecting the objective strategy in road traffic engineering. Also, service-oriented architecture (SOA) is realized to implement such a constructed model practically. The Model can suggest the most optimal strategy decision in road traffic engineering. Here, a main traffic road of Juanda in area Ciputat, Tangerang Selatan, province Banten, Indonesia; was selected as a research object in this study. The constructed SOM for road traffic engineering was structurally delivered in this paper.

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Indonesian Automatic Speech Recognition with Xlsr-53

SSRN

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SSRN 2022年

作者： Arisaputra, Panji Zahra, Amalia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This study focuses on the development of Indonesian Automatic Speech Recognition (ASR) using XLSR-53 pre-trained model. The use of this XLSR-53 pre-trained model is to significantly reduce the amount of training data in non-English languages required to achieve competitive WER. The total amount of data used in this study is 26 hours: (1) TITML-IDN 14,5 hours;(2) Magic Data 3,5 hours;and (3) Common Voice 8 hours. With a WER of 22 %, the model built in this study can compete with similar models using the Common Voice dataset split test. WER can be decreased by around 10% using a language model, resulting in a WER of 12 %. Thus, the results of this study have succeeded in perfecting previous research in contributing to the creation of a better Indonesian ASR with a smaller amount of data. © 2022, The Authors. All rights reserved.

关键词： Statistical tests

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Comparative Analysis of CNN Models for Multiclass Detection on Colon Disease

Comparative Analysis of CNN Models for Multiclass Detection ...

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

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

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

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

关键词： Hands Deep learning Accuracy Endoscopes Real-time systems Gastrointestinal tract Data models Colon Medical diagnostic imaging Diseases

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The Classification Model of Learners Based on Diagnostic Assessment Results in E-learning With Machine Learning 1

The Classification Model of Learners Based on Diagnostic Ass...

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1st International Conference on Software Engineering and Information Technology, ICoSEIT 2022

作者： Yel, Mesra Betty Zarlis, Muhammad Suwilo, Saib Efendi, Syahril Sihombing, Poltak Universitas Sumatera Utara Graduate Program of Computer Science Department of Computer Science Medan Indonesia Universitas Binus Department of Computer Science Jakarta Indonesia Universitas Sumatera Utara Department of Mathematics Medan Indonesia

ISBN: (纸本)9781665473033

The development of technology and artificial intelligence, especially in the era of Industrial Revolution 4.0 almost covers all fields, one of which is e-learning. In its development, it is very important to determine the factors to produce the quality of e-learning. Machine learning can help to find various solutions in the real world, especially to solve problems for which a specific algorithm has not been found. The implementation of e-learning is often ineffective and not on target which has an impact on learning achievement that is not achieved, this is due to several things namely the learning model is applied in general, the learning materials and weights are applied equally to all users. Seeing these problems, it is necessary to have a model to classify students according to their abilities, talents, and interests before starting learning to achieve their goals to be achieved. This paper aims to create a machine learning approach model to determine the level of ability and needs of students before starting learning which can reduce disparities between students and follow up learning materials. With the machine learning based on learning model, e-learning can provide teaching materials according to the abilities, and interests of students, sustainably and improve the quality of students. © 2022 IEEE.

关键词： Students

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Smartphone Application for the Deaf and the Deaf Caring Community 6

Smartphone Application for the Deaf and the Deaf Caring Comm...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Pryandi, Pinnus Dewantara, Muhammad Bayu Warnars, Harco Leslie Hendric Spits Arief, Ramadhan Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Telkom University Bandung11480 Indonesia

ISBN: (纸本)9798350398496

In life, various challenges and problems faced by deaf people such as communication skills and other problems, including emotional, mental, and societal development. However, technology is needed that can help the process. This Smart Application try to build communication between the deaf to share thoughts in group chats, including getting information about the deaf, so that the communication learning process for the deaf becomes easier. The proposed model is using Unified Modeling Languages (UML) diagrams, such as case diagrams for the current proposed idea process and class diagrams for relational tables or database stores. In addition, the User interface (UI) is implemented in this research work, Personal Home Pages (PHP) as a web server programming and MySQL database as an open-source database. © 2023 IEEE.

关键词： User interfaces

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A systematic literature review of machine learning application in COVID-19 medical image classification

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Procedia computer science 2023年 216卷 749-756页

作者： Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia. Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia. Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia.

Detecting COVID-19 as early as possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help to diagnose COVID-19 more quickly and accurately. This report aims to find out how far research has progressed and what lessons can be learned for future research in this sector. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. According to the findings, chest X-ray were the most commonly used data to categorize COVID-19 and transfer learning techniques were the method used in this study. Researchers also concluded that lung segmentation and use of multimodal data could improve performance.

关键词： COVID-19 classification disease machine learning medical images

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Automatic Multiple Choice Question Generation: A Systematic Literature Review

Automatic Multiple Choice Question Generation: A Systematic ...

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Matthew Martianus Henry Nur Adhianti Heryanto Mahmud Isnan Kuncahyo Setyo Nugroho Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Despite their drawbacks, multiple-choice questions (MCQ) have been widely used to assess the students' understanding of lectures through examinations. The development of automatic MCQ generation is beneficial, especially for educators. As a starting point for further development, a Systematic Literature Review (SLR) is conducted to uncover current trends, future challenges, and opportunities in automatic MCQ generation. Previously, an SLR was conducted, but it lacks coverage of the utilization of transformer-based models. This SLR covers the development of automatic MCQ generation using either traditional or advanced approaches such as Transformers. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was used to gather the data from Scopus, IEEE Xplore, SpringerLink, arXiv, and Semantic Scholar. The included articles must be open-access computer science conference papers or journal articles and written in English less than five years ago. Four independent reviewers analyzed the research workflow, evaluation metric, and dataset used in each study. There are 18 included studies, where 17% (n = 3) studies are from 2024, 33% (n = 6) studies are from 2023, 22% (n = 4) studies are from 2022, 11% (n = 2) studies are from 2021, and 17% (n = 3) studies are from 2020. There are 33% (n = 6) of the studies used the traditional feature-based engineering approach, 39% (n = 7) of the studies used the Transformer-based model fine-tuning approach, and the remaining used novel approaches. The study found that BERT variants are the most utilized Transformer-based model in automatic MCQ. The research notes some challenges, but also open various opportunities for further research, including Large Language Model (LLM) utilization for automatic MCQ generation, the utilization BERT-based models for standardized machine-learned evaluation metrics, and the initiative for the creation of an MCQ dataset benchmark.

关键词： Measurement Semantics Reinforcement learning Benchmark testing Ontologies Transformers Question generation Prompt engineering Standards Systematic literature review

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